Bionic Nanosystems

Manu Sebastian Mannoor

A DISSERTATION

PRESENTED TO THE FACULTY

OF PRINCETON UNIVERSITY

IN CANDIDACY FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

RECOMMENDED FOR ACCEPTANCE

BY THE DEPARTMENT OF

MECHANICAL AND AEROSPACE ENGINEERING

ADVISER: MICHAEL C. MCALPINE

JUNE 2014

© Copyright by Manu Sebastian Mannoor, 2014.

All rights reserved.

iii

Dedication

To my loving wife Teena, for all her encouragement, forbearance, prayers and support.

*****

To Amma, for all her sacrifices and prayers.

*****

To God almighty - my help and refuge.

Great are the works of the LORD, studied by all who delight in them. (Psalm 111:2)

iv

Abstract

Direct multidimensional integration of functional electronics and mechanical elements with

viable biological systems could allow for the creation of bionic systems and devices possessing

unique and advanced capabilities. For example, the ability to three dimensionally integrate

functional electronic and mechanical components with biological cells and tissue could enable

the creation of bionic systems that can have tremendous impact in regenerative medicine,

prosthetics, and human-machine interfaces. However, as a consequence of the inherent

dichotomy in material properties and limitations of conventional fabrication methods, the

attainment of truly seamless integration of electronic and/or mechanical components with

biological systems has been challenging.

Nanomaterials engineering offers a general route for overcoming these dichotomies,

primarily due to the existence of a dimensional compatibility between fundamental biological

functional units and abiotic nanomaterial building blocks. One area of compelling interest for

bionic systems is in the field of biomedical sensing, where the direct interfacing of nanosensors

onto biological tissue or the human body could stimulate exciting opportunities such as on-body

health quality monitoring and adaptive threat detection. Further, interfacing of antimicrobial

peptide based bioselective probes onto the bionic nanosensors could offer abilities to detect

pathogenic bacteria with bio-inspired selectivity. Most compellingly, when paired with additive

manufacturing techniques such as 3D printing, these characteristics enable three dimensional

integration and merging of a variety of functional materials including electronic, structural and

biomaterials with viable biological cells, in the precise anatomic geometries of human organs, to

form three dimensionally integrated, multi-functional bionic hybrids and cyborg devices with

unique capabilities.

v

In this thesis, we illustrate these approaches using three representative bionic systems: 1)

Bionic Nanosensors: featuring bio-integrated graphene nanosensors for ubiquitous sensing, 2)

Bionic Organs: featuring 3D printed bionic ears with three dimensionally integrated electronics

and 3) Bionic Leaves: describing ongoing work in the direction of the creation of a bionic leaf

enabled by the integration of plant derived photosynthetic functional units with electronic

materials and components into a leaf-shaped hierarchical structure for harvesting photosynthetic

bioelectricity.

vi

Acknowledgements

This journey of the past 5 years in graduate school would not have been a success without the

generous help, guidance, support and prayers of many truly incredible and amazing people.

First and foremost, I would like to express my deepest gratitude to my advisor Professor

Michael McAlpine. He is an amazing scientist and mentor and his guidance encouragement and

support had the greatest influence on my success as a graduate student. His mentoring style that

is custom to each one of his lab members, paying attention to their specific strengths and

weaknesses is admirable. He pushed me to develop my weaknesses and exploit my strengths. I

cannot thank him enough for showing confidence in me and offering continued support for in all

my scientific endeavors. Further, I am extremely grateful of his patience to withstand my many

failures and stupid mistakes that I made along the way of my graduate career. He patiently

corrected me in the right direction without ever taking offenses at my mistakes. From the

beginning of my Ph.D. research, Professor McAlpine was keen in training me to be an

independent researcher-by encouraging me to come up with new and cutting-edge research ideas

and guiding me in this process of developing these ideas and gathering rigorous scientific results

by asking the key questions. His hard work and commitment has always been an inspiration for

me. Further, over the years, he trained me in writing high impact publications as well as in

maintaining highest standards in published results. Progressively towards the end of my graduate

studies, he started giving more weightage to develop my ability to do independent research and

thereby preparing me to start a career in academia (which Professor McAlpine always

encouraged each one of us to pursue). In addition, having had the opportunities to serve as

Assistant in Instruction with him, being a true and passionate teacher himself, he has given me

tips and advices many times in improving my teaching skills. All of these I believe to be a very

vii

unique and most valuable experience for any Ph.D. student. I am very thankful to him for this

training and feel myself to be very lucky to have had his guidance and the chance to work under

his supervision as a Ph.D. student.

Next, I would like to thank the past and current members of the McAlpine lab, whose

help and support many times has been instrumental in the success of my research projects. I

would like to especially thank Dr. Yi Qi, Dr. Yue Cui, Ann Mularz, Dr. Thanh Nguyen, Dr.

Kellye Cung, Yao-Wen Yeh, Dr. Maneesh Gupta, Dr. Blake Johnson, Huai-An Chin, Ian

Tamargo, Nina Masters and all the undergraduate researchers over the years for their help and

keeping me company in the lab. I would also like to thank in a very special way, Yong Lin Kong,

my good friend inside and outside the lab for his company and willingness to help always. I

would also like to thank Ziwen Jiang, a high school researcher from Peddie School (soon to be

an undergraduate student at MIT) and Jeff Clayton, a former Chemistry senior thesis student

(now graduate student at MIT) with whom I had the privilege to work on some incredible

projects.

I would like to thank my collaborators and those who served as academic and research

mentors all throughout my graduate career. I do not have words to adequately express my

genuine appreciation and gratitude to them, who were so generous in providing me guidance and

support along way of my graduate studies. I am very much indebted to Professor Claire Gmachl,

Professor of EE and vice dean of SEAS, for being always willing to give me advice and guidance

and for her continued support through the entire 5 years of my graduate life in Princeton. I also

want to express my deepest gratitude to Professor Winston Soboyejo of MAE for his advice,

guidance and continued support all throughout my Ph.D. years. I do not have words to express

my gratitude and respect to Professor Barrie Royce, Professor Emeritus in MAE, for his

viii

continued guidance, support and also for his generous help with the preparation for Ph.D. general

exam. I also do not know how to even begin to thank Dr. Nan Yao, director of PRISM imaging

and Analysis center for being so generous and a great teacher and teaching me everything that I

know of materials characterization and imaging- I benefitted immensely from his materials

characterization class MSE 505. I also wish to extend my appreciation and gratitude to Professor

David Gracias of Johns Hopkins University, for collaboration and valuable suggestions in the 3D

Printed Bionic Ear project and also for his support, Professor Naveen Verma of EE for the

collaboration in both the bionic nanosensor and bionic ear project and also for help with

electrical measurements for always being so willing to help when I show up at his office and

Professor James Link for collaboration in the antimicrobial peptide characterization project. I

don’t have words to adequately convey my appreciation and gratitude to Dr. Roger Cubicciotti,

President Nanomedica Inc., Professor Howard Stone of MAE, Professor Thomas Thundat of

University of Alberta, Professor George John of CUNY, Professor Marc Madou of UC Irvine,

Dr. Bill Braunlin and Dr. Les Beadling of RAD for their advice and continued support. Also, I

want to thank other faculty members in MAE: Professor Mikko Haataja, director of graduate

studies, Professor Craig Arnold, Professor Philip Holmes and everybody else who were generous

in helping me in my graduate life at Princeton. I also want to thank my Master’s advisor Dr.

Dentcho Ivanov for his guidance and support during the years of my Masters in Biomedical

Engineering at NJIT. I thank Professor Fiorenzo Omenetto of Tufts University, Professor

Amartya Sengupta of IIT Delhi (previously at Geoscience Department, Princeton) for

collaboration and valuable discussion in the bionic nanosensor project. My special thanks go to

Dr. Karen Malatesta of MAE for training me in cell culture and related protocols and for good

conversations and keeping company in the biolab. Also, one of the most beautiful part of

ix

graduate school was going through it together with other graduate students. I want to thank all

my friends whom I met in Princeton- Anand Ashok, Fadi Abdeljawad, Srevatsan Muralidharan,

Stimit Shah, Bryan Benson, Josh Heyne, Ismail Yakub, Yusuf Oni, Mykola Bordyuh and others

who made the Ph.D. years fun and wonderful!

I also couldn’t have done without the help of administrative staff members of MAE,

PRISM MFNL and IAC. Thanks very much to Jill Ray, our graduate administrator for all her

help and encouragement for academic and life matters. Also, thanks to Candy Reed, Carolyn

Arnesen, Joe Palmer, Dr. Pat Watson, Jerry Poirier for all the help and assistance. My thanks

also goes to Jonathan Prevost for all this help, support and good conversations and Mike

Vocaturo for all this help during last 5 years.

I also want to thank Father Dave Swantek and Father Martin Miller, chaplains at

Princeton University for their prayers and personal guidance. My deepest gratitude also goes to

Saint John’s soup kitchen, Newark, NJ and our friends and family for their constant prayers,

generous support all throughout.

Finally, I would like to thank my beautiful wife Teena and our wonderful son John.

Teena’s encouragement, constant prayers, quiet patience and unwavering love were undeniably

the driving force and inspiration for the past several years of my life. The sacrifices that she took

and her tolerance is a testament in itself of her patient love and unyielding support. Being a

graduate student herself at Johns Hopkins University, doing Ph.D. in a closely related field, I feel

so blessed to have taken this journey of graduate school with her company. Although, our

evenings and weekends were often filled with conversations about failed experiments or rejected

manuscripts, seeing the cutest smile on my son’s face (which he seem to reserve for the most

x

desperate moments in our life) makes us forget all the aches and pains of the day and divert my

attention to yet more subtle but joyous things in my life. I would also like to thank my mother,

brothers and sister, Teena’s parents and brother and sisters for their continued prayers and

encouragement.

Most of all, I am grateful to the Lord for watching over all my steps and guiding me in

my paths. “From whence cometh my help? My help cometh from the Lord, who made heaven

and earth” ( Psalm 121:1-2)

This dissertation carries T-3282 in the records of the Department of Mechanical and Aerospace

Engineering.

xi

Table of Contents

Dedication ...................................................................................................................................... iii

Abstract .......................................................................................................................................... iv

Ackowledgements .......................................................................................................................... vi

Table of Contents ........................................................................................................................... xi

List of Figures ............................................................................................................................. xvii

Chapter 1 ........................................................................................................................................1

Bionic Systems: Introduction ........................................................................................................1

1.1 Bionics ..............................................................................................................................1

1.2 History of Bionics:Implantable Devices and Prosthetics..................................................2

1.3 Biological Materials and Systems .....................................................................................5

1.4 Disparity in Properties between Engineered Systems and Biological Systems ................7

1.4.1 Dichotomy in Formation .............................................................................................8

1.4.2 Limitations of the Current Fabrication Methods ........................................................9

1.5 Overcoming the Differences: Nanoscale Science and Engineering ..............................10

1.5.1 Nanoscale Mechanics: Influence of Size on Mechanical Behavior ..........................11

1.6 Nanoscale Functional Electronic and Structural Materials ............................................12

1.6.1 Carbon Nanomaterials and Graphene ......................................................................13

1.6.2 Semiconducting Quantum Dots ...............................................................................16

1.6.3 Metallic Nanoparticles, Nanowires and Nanorods ..................................................17

xii

1.7 Bioorthogonal Approaches for Bionic Integration .........................................................18

1.7.1 Biomimetics: Engineering biocompatibility via biomimicry ..................................18

1.7.2 Self- Assembly ........................................................................................................19

1.7.3 Phage display ...........................................................................................................20

1.7.4 Tissue Engineering ..................................................................................................21

1.8 Additive Manufacturing for Bottom-up Three Dimensional Integration .......................22

1.9 Thesis Overview .............................................................................................................23

1.10 References .....................................................................................................................24

Chapter 2 ......................................................................................................................................35

Bionic Nanosensors ......................................................................................................................35

2.1 Overview .........................................................................................................................35

2.2 Biointegration of Sensors ................................................................................................35

2.3 Results & Discussion ....................................................................................…………..38

2.3.1 Graphene Silk Sensor ................................................................................................38

2.3.2 Materials Integration and Characterization ...............................................................40

2.3.3 Functionalization of graphene with AMPs ...............................................................48

2.3.4 Single bacterium detection ........................................................................................51

2.3.5 Wireless remote query monitoring of S.aureus. .......................................................54

2.3.6 Tooth platform monitoring of breath and saliva .......................................................57

2.3.7 Discussion .................................................................................................................60

xiii

2.4 Materials & Methods ......................................................................................................62

2.4.1 Reagents and Biologicals ..........................................................................................62

2.4.2 Prepartion of silk films.............................................................................................62

2.4.3 Fabrication of Graphene/silk sensors ........................................................................63

2.4.4 Biotransfer onto biomaterials ....................................................................................63

2.4.5 Graphene functionalization with AMPs ....................................................................63

2.4.6 Single bacterium detection measurements ................................................................64

2.4.7 Wireless sensing experiments ...................................................................................65

2.5 Conclusion ......................................................................................................................70

2.6 References .......................................................................................................................71

Chapter 3 ......................................................................................................................................79

Antimicrobial Peptides as Molecular Probes on Bionic Sensors ................................................... 79

3.1 Overview .........................................................................................................................79

3.2 Introduction .....................................................................................................................80

3.3 Antimicrobial Peptide based Sensitive Detection of Bacteria ........................................83

3.4 Effect of AMP Immobilization Density ..........................................................................87

3.5 Selectivity Measurements ...............................................................................................89

3.6 Real-Time Detection .......................................................................................................95

3.7 Materials and Methods ....................................................................................................97

3.7.1 Antimicrobial Peptides and Bacterial Cells ..............................................................97

xiv

3.7.2 Interdigitated Microelectrode Array (IMA) and Microfluidic Flow Cell .................98

3.7.3 Sensor Surface Functionalization with Magainin .....................................................99

3.7.4 Fluorescent Microscopy ............................................................................................99

3.8 Impedance Spectroscopy Measurement Details ...........................................................100

3.8.1 Measurement setup .................................................................................................101

3.8.2 Equivalent Circuit ...................................................................................................102

3.9 Conclusion ....................................................................................................................104

3.10 References ...................................................................................................................105

Chapter 4 ....................................................................................................................................112

3D Printed Bionic Ears .................................................................................................................. 112

4.1 Overview .......................................................................................................................112

4.2 Introduction ...................................................................................................................112

4.3 Our Approach................................................................................................................114

4.4 3D Printing of Bionic Ear: Steps ..................................................................................115

4.5 Growth and Viability of the Bionic Ear ........................................................................117

4.5.1 Viability of the Printing Process .............................................................................119

4.6 Histologic Characterization ..........................................................................................120

4.7 Biochemical and Biomechanical Characterization .......................................................121

4.7.1 Tensile Testing 3D Printed Cartilage Dog bones ...................................................122

4.7.2 Hardness Testing of 3D Printed Neocartillage .......................................................123

xv

4.8 Electrical Characterization ............................................................................................123

4.8.1 Bionic Ears: Listening of Stereo Music ..................................................................126

4.9 Materials and Methods ..................................................................................................126

4.9.1 Chondrocyte Culturing............................................................................................126

4.9.2 Alginate Formulation and Chondrocyte Seeding ....................................................127

4.9.3 3D Printing ..............................................................................................................127

4.9.4 Culturing conditions...............................................................................................128

4.9.5 Cellular and Tissue Viability ..................................................................................131

4.9.6 Biochemical Analyses .............................................................................................132

4.9.7 Histologic Evaluation of the Bionic Ear .................................................................136

4.9.8 Biomechanical Characterization .............................................................................138

4.10 Conclusions .................................................................................................................141

4.11 References ...................................................................................................................142

Chapter 5 ....................................................................................................................................147

3D Printed Bionic Leaves for Photosynthetic Bioelectricity .................................................... 147

5.1 Overview .......................................................................................................................147

5.2 Introduction ...................................................................................................................148

5.3 3D Printing of Bionic Leaf ...........................................................................................151

5.4 Thylakoid Isolation and Characterization .....................................................................152

5.4.1 Determination of the Chlorophyll Content in Isolated Thylakoids ........................154

xvi

5.5 Photosynthetic Electron Generation: Hill Reaction ......................................................155

5.6 Electronic Conduction Medium- Formulation and Characterization............................158

5.6.1 Characterization of Electronic Conduction Medium ..............................................159

5.7 Photosynthetic Material ................................................................................................162

5.8 Production for Photosynthetic Bioelectricity ................................................................163

5.9 3D Printable Bionic Leaf Architecture .........................................................................164

5.10 Conclusions .................................................................................................................165

5.11 References ...................................................................................................................166

Chapter 6 ....................................................................................................................................169

Conclusions and Future Outlook ..............................................................................................169

6.1 Summary of Main Conclusions ....................................................................................169

6.2 Future Outlook ..............................................................................................................172

xvii

List of Figures

Figure 1.1 Artistic rendition of bionic human.................................................................................2

Figure 1.2 Image of conventional cochlear implant system ...........................................................4

Figure 1.3 Ashby plots of biological and abiotic materials ............................................................7

Figure 1.4 Discrepancies between biological and engineered systems ..........................................9

Figure 1.5 Carbon nanomaterials ..................................................................................................13

Figure 1.6 Ambipolar field effect in graphene ..............................................................................14

Figure 1.7 Mechanical properties of graphene..............................................................................15

Figure 1.8 Semiconducting quantom dots.....................................................................................16

Figure 1.9 Metallic nanoparticles..................................................................................................17

Figure 1.10 Self assembly .............................................................................................................19

Figure 1.11 Bacteriophage and phage display biopanning ...........................................................20

Figure 1.12 Tissue Engineering Approaches. ...............................................................................22

Figure 1.13 Thesis Overview- Bionic Systems .............................................................................23

Figure 2.1 Biotransferrable graphene wireless nanosensor ..........................................................39

Figure 2.2 Graphene biotransfer and characterization ..................................................................41

Figure 2.3 Raman spectra of tooth enamel and Bombyx mori silk fibroin film ............................42

Figure 2.4 Biotransfer of the sensor onto skin ..............................................................................43

Figure 2.5 Return loss (S11) of the wireless sensing element. .....................................................44

Figure 2.6 Optical microscopy images of graphene on surfaces ……………… .........................45

Figure 2.7 Stability of sensor in running water. ............................................................................46

Figure 2.8 Structural integrity testing of sensor on bovine tooth enamel. ....................................47

Figure 2.9 Graphene functionalization with antimicrobial peptides. ............................................50

xviii

Figure 2.10 Single bacterium detection. .......................................................................................53

Figure 2.11 Wireless monitoring of S. aureus. .............................................................................55

Figure 2.12 Structural integrity testing of sensor integrated onto IV bag .....................................56

Figure 2.13 Tooth sensor monitoring of breath and saliva ...........................................................58

Figure 2.14 Impedance spectrum of the reader coil. .....................................................................65

Figure 2.15 Impedance spectrum of the sensing element. ............................................................68

Figure 2.16 Complex impedance spectrum of the sensing element. .............................................69

Figure 2.17 Electrical equivalent circuit of the wireless sensor-reader system.. ..........................70

Figure 3.1 AMP-based electrical detection of bacteria ................................................................83

Figure 3.2 Sensitivity of the AMP electronic biosensor ...............................................................85

Figure 3.3 Impedance spectra of various concentrations of E. coli O157:H7 ..............................86

Figure 3.4 The effect of the surface density of immobilized Magainin I .....................................88

Figure 3.5 Optical microscopy of the selectivity of AMPs...........................................................90

Figure 3.6 Impedance spectroscopy of the selectivity of AMPs...................................................92

Figure 3.7 Impedance spectra of the sensor after exposure to pathogenic bacteria ......................93

Figure 3.8 Impact of varying pH ...................................................................................................94

Figure 3.9 Real-time binding of bacteria to AMP biosensors .......................................................96

Figure 3.10 Schematic of the impedance spectroscopy measurement setup ..............................103

Figure 4.1 Three dimensional interweaving of biological tissue and electronics ......................115

Figure 4.2 Growth and viability of the bionic ear. ......................................................................118

Figure 4.3 Gross morphology of the 3D printed bionic ear ........................................................120

Figure 4.4 Biomechanical characterization of the 3D printed neocartilage tissue ......................122

Figure 4.5 Electrical characterization of the bionic ear ..............................................................125

xix

Figure 4.6 Resistivity measurements ..........................................................................................128

Figure 4.7 Images of the 3D printed ear auricle .........................................................................129

Figure 4.8 Image of neocartilage growth of the 3D printed ear ..................................................129

Figure 4.9 Images of the 3D printed left bionic ears at various stages of growth. .....................130

Figure 4.10 Electrical resistance of the coil antenna in culture ..................................................131

Figure 4.11 LIVE/DEAD® assay of chondrocytes.....................................................................132

Figure 4.12 DNA content standard curve obtained from calf thymus DNA ..............................133

Figure 4.13 DNA content in the 3D printed ear at various stages during culture .......................134

Figure 4.14 Hydroxyproline standard curve obtained from L-Hydroxyproline .........................135

Figure 4.15 GAG standard curve obtained from Chondroitin-6-Sulphate. .................................136

Figure 4.16 Tensile testing of 3D printed dog bone samples......................................................139

Figure 4.17 Hardness measurement of the 3D printed ear cartilage ...........................................140

Figure 5.1 Schematic illustration of the bionic leaf architecture. ...............................................147

Figure 5.2 3D Printed Bionic Leaf for Energy............................................................................151

Figure 5.3 Isolation of Thylakoids ..............................................................................................153

Figure 5.4 Microscopy characterizations of isolated thylakoids .................................................154

Figure 5.5 Determination of the chlorophyll content in the isolated thylakoids.........................155

Figure 5.6 Hill Reaction using DCPIP ........................................................................................156

Figure 5.7 Hill Reaction- change in absorbance of the sample ..................................................157

Figure 5.8 Schematic illustration of electrical interfacing of thylakoids ....................................158

Figure 5.9 Formulation of the electronic conduction medium....................................................159

Figure 5.10 XRD characterization of the electronic conduction medium.. ................................160

Figure 5.11 Raman spectroscopy of the electronic conduction medium ....................................161

xx

Figure 5.12 Formulation of the photosynthetic material.............................................................162

Figure 5.13 Characterization of the photosynthetic material. .....................................................163

Figure 5.14 Measurement of the photosynthetic current ............................................................164

Figure 5.15 CAD of the bionic leaf architecture .........................................................................165

1

Chapter 1

Bionic Systems: Introduction

1.1 Bionics

Bionics is defined as the study of mechanical systems that function like living organisms or parts

of living organisms [Oxford dictionaries]. It is believed that the term bionics is coined by Dr.

Jack E. Steele, MD (who was also a US Air Force colonel) around 1958. There are two possible

arguments about the etymology of the word bionic. Some suggest that a possible origin could be

from the technical term bion (pronounced bee-on) (from Ancient Greek: βίος), meaning 'unit of

life' and the suffix -ic, meaning 'like', with combined meaning of 'like life'

[http://en.wikipedia.org/wiki/Bionics]. Some other sources suggest that the word is formed as a

portmanteau of bi (as in life) + onics (as in electronics) [National Geographic, 2010]. Both of

the suggested origins and their implied meanings is fitting to the technical definition of the term

“Bionics” as well as represents the functionality of the class of systems that are formed in

general as a merger of biological systems and engineered functional electronic or mechanical

systems (Fig. 1.1).

2

Figure 1.1 Artistic rendition of bionic human [Image credit: EDN December 2011 original

source: ST Microelectronics].

1.2 History of Bionics: Implantable Devices and Prosthetics

The use of implanted technological aids and prostheses has long been in existence as a means to

compensate for injuries and deformations resulting from trauma or diseases1,2

. In general, these

primitive versions of bionics involved the use of metallic materials in the form of plates, screws

and other prosthesis for the creation of implants and repairing fractures3. For example, the use of

artificial tooth constructs made of wrought iron was prevalent among the Romans even as early

as the first century AD, as replacement teeth3. A major milestone in the development of

implantable devices as replacement parts was marked by the performance of a total hip

3

replacement in 19381. However, the lack of suitable materials and poor engineering designs of

these early implants made them less successful.

In more recent years, rapid progress in the field of microelectronics and semiconductor

device fabrication techniques has contributed immensely to the advancement of implantable

devices. For example, the first successful implantation of a cardiac pacemaker in 1958 made a

significant impact in the medical field. Examples of other major implantable medical devices

include retinal implants and cochlear implants for hearing disabled persons .The most recent

development in cochlear implantation is in the treatment of single-sided nerve deafness (Fig 1.2).

The implanted device has external and internal parts. The external device consists of a digital

sound processor microphone and a transmitting coil. The sound picked up by the microphone is

sent to the transmitting coil via the speech processor. The transmitter sends the signal through the

skin barrier to the internal implanted device utilizing electromagnetic induction. The internal

implanted device consists of an electronics package that leads to an electrode array shaped in the

form of human cochlea. The electronics converts the signal into electrical energy and is passed to

the electrode array. This stimulates the nerve fibers and the signal is carried to the brain via

auditory nerve and is recognized by the brain for sound.

4

Figure 1.2 Image of a cochlear implant system showing the external device and the internal

implant. [Image credit: NIH].

Review articles such as the one on cyborg devices2 and text books such as “Bionics” by

Nachtigall4 are good sources for an elaborate survey on implantable devices and prosthetic

bionic systems.

The original idea of bionic systems and cyborg organisms has advanced in the recent

years by taking advantage of the development of novel functional materials and advanced

fabrication techniques5-10

. Approaches for the direct multidimensional integration of functional,

electronic and mechanical components with viable biological systems could open up tremendous

opportunities across a broad array of disciplines in science and engineering11,12

. These range

from the realm of direct interfacing of functional electronic and mechanical elements with pre-

grown, mature biological tissue and systems, to the development of a seamlessly merged,

5

chimeric bionic system with advanced functionalities13-21

. For example, the ability to three-

dimensionally integrate functional electronics with biological cells and tissue could enable the

creation of bionic organs that can have tremendous impact in regenerative medicine, prosthetics,

and human-machine interfaces. In general, the creation of a functional integration between

engineered and biological systems could provide opportunities for enhancing human

performance2,22

. However, the design and implementation of such systems demands a

fundamental understanding of the inherent properties and disparities between the biological and

engineered systems and their composition.

The following sections will thus attempt to take a closer look at the biological and

engineered systems, consider their major differences in the properties and functionalities, and

discusses approaches that enable us to overcome these differences for the design and

development of multifunctional bionic hybrids.

1.3 Biological Materials and Systems

Biological materials are ubiquitous in nature and form the multipotent constituents of all

prokaryotic and eukaryotic living organisms23-26

. They serve a diversity of functions including

mechanical support as in the case of skeletal bones, conversion of chemical energy into

mechanical energy as in the case of muscle cells and tissue, and as a reservoir for essential

minerals as in the case of bones serving as source of calcium and phosphorous27

. Biological

materials such as enzymes perform catalytic reactions, whereas the main functions of cellular

membranes include acting as selective ion barriers in addition to providing structural support for

the organelles and subcellular components28

. Chloroplast organelles in green plants and

cyanobacteria perform the function of producing food via photosynthesis. Thylakoid grana in the

6

chloroplasts function as the centers of photosynthetic light reaction with the help of integral

membrane proteins, Photosystems I and II that perform light induced water splitting reactions

and many are the unique and amazing functionalities of naturally occurring biological

components29-33

. A thorough review of the composition and properties of biological materials

and systems is hence beyond the scope of this chapter.

Ashby and Wegst classify biological materials into the following four groups34

:

1. Polymers and polymer composites: Examples of which include, silk, tendon, ligaments

and exoskeletons of arthropods35-37

.

2. Elastomers: Elastomeric biogenic materials are characterized by the ability to undergo

large strains (stretchable). Examples include skin, muscle, blood vessels and individual

cells.

3. Ceramics and ceramic composites: These are mostly comprised of minerals. Examples

include, bone, teeth, shells and diatoms23,38,39

.

4. Cellular materials: These include lightweight materials such as feathers, interiors of beak,

wood and cancellous / spongy bone 25,40,41

.

Proteins and other biological materials are formed from the basic building blocks of 20

amino acids23

. The long molecular chains of structural proteins such as collagen, keratin, elastin,

chitin, resilin, actin, myosin and abductin can result in a range of elastic strengths through

hierarchical organization23,42

. In the case of hard biological materials such as bone and dentin a

mineral phase is embedded in a collagen based organic matrix. Another major class of biological

materials is cellular with a foamy structure resulting in high stiffness and low weight23

.

7

1.4 Disparity in Properties between Engineered Systems and Biological Systems

There exist major differences in properties between the engineered abiotic systems and

biological materials that prevent a seamless integration of the both. First of all, the design

principles used in biological system formation and engineered systems are very much different43-

46.

Figure 1.3 Ashby plots, Young’s modulus E (which corresponds to stiffness) versus strength for

a variety of biological and abiotic materials [image source: Knowles et al 43

Copyright 2011

Macmillan Publishers Ltd (Nature Publishing Group)

8

This difference often stem from the differences in the primary elemental constituents in

both the systems. For example, biological systems are dominated by light elements such as C, N,

O, H, Ca etc. and elements such as iron, chromium, nickel, etc. are very rarely found, certainly

not in metallic form which makes a wide difference in their mechanical properties27

(Fig.1.3).

Iron found in red blood cells exists in ionic forms bound to hemoglobin where its function is

chemical, to bind oxygen instead of mechanical. In contrast, structural materials found in

biological systems are either polymers or their composites with ceramic particles27

.

1.4.1 Dichotomy in Formation

Another strikingly major difference exists between abiotic engineered and biological systems in

the processes resulting in their formation47-52

. Biological systems and materials are grown, (not

made) in to a whole organism (plant or animal) via self-assembly, guided by the principles of

developmental biology. As a consequence, biological systems are, in general, able to reconfigure

and adapt to environmental changes and self-heal when damaged. In addition, the biologically

controlled self-assembly driven growth takes place at near room temperature at atmospheric

pressure under common physiological conditions53,54

. In contrast, abiotic electronic or

mechanical systems are fabricated from the selected materials based on secure engineering

design considering extreme conditions. This design driven fabrication process often involves

high temperatures, pressure and what is considered to be “biologically harsh” chemical

conditions. The following figure (Fig.1.4) illustrates some of the key differences in properties

and functionalities between the two classes of materials and systems originating from the

fundamental disparity in the growth and fabrication processes.

9

Figure 1.4 Discrepancies in the elemental composition and mode formation of biological and

engineered systems [source: Fratzl et al 27

].

1.4.2 Limitations of Current Fabrication Methods

The inherent limitations of the current machining and fabrication methods allows only for a

static process. In general, an engineered electronic or mechanical system is designed and the

constituent materials are selected taking into account the functionalities, requirements and other

lifetime issues such as fatigue during service. In contrast, biological materials and systems are

grown and thus enables for a dynamic process allowing adaptation to environmental factors55-57

.

Here the problems of fatigue are taken care of using a constant renewal of materials by growth.

Further, the growth process allows for extremely complicated three dimensional integration of

various functional materials in a single system. Organs of animals and parts of plants such as

10

leaves represent very good example of such hierarchical integration of materials and structures

achieved via the growth process. As a consequence, biological systems can also be functionally

graded with hierarchical structures, a property that is hard to achieve via conventional

fabrication methods58

. Examples of such hierarchical structures in biogenic tissue consisting of

completely different composition include: wood-a total polymeric structure, glass sponges-

composed entirely of silica and bone ( an organic-inorganic composite) consisting of

approximately half polymer and half mineral27

.

1.5 Overcoming the Differences: Nanoscale Science and Engineering

Nanoscale science and engineering offers a general strategy to overcome the

discrepancies between the biotic and abiotic worlds. Despite the dichotomies existing between

the two classes of materials and systems, going down on the size scale to the dimensions of the

fundamental building blocks in the micro and nano regimes in each group allows for a

synergistic integration. This symbiosis arises as a consequence of the natural compatibility that

lies between biomolecules and the nanoscale inorganic electronic and structural materials. Such

an interconnect could be aided by two factors: size and electronic charge. For example, the size

scale of DNA duplex, protein molecule, viral particle are in the order of 1nm, 10nm and 100nm

respectively. In comparison, the diameter of a single walled carbon nanotube is about 1nm and

the cross section of fabricated semiconductor nanowire is in the order of 10nm. This size

similarity makes ‘Nano’ the natural length-scale for functional abiotic interfaces with biological

systems59

. The second feature that aids the linkage between the two classes of materials is the

electronic charge. The charge distribution and localized electric dipoles in biological molecules

are crucial in attaining wide variety of their functionalities.

11

Nanotechnology enabled bio-orthogonal tools and processes utilize this dimensional

similarity and charge based affinity between biological materials and abiotic functional

nanomaterials to create a symbiotic integration as described in detail in the following sections.

Here the term “bio-orthogonal” is used to identify/qualify nanoscale materials and

bionanotechnology processes that enable to control, probe and modify biological systems

without interfering their native processes but can in fact complement and enhance their natural

functionalities.

1.5.1 Nanoscale Mechanics: Influence of Size on Mechanical Behavior

Biological systems are organized hierarchically, with unique characteristics and functionalities

spanning multiple length scales which demands the selection of the right organizational length

scale for biointerface design. In the case of sub-cellular organization, this length scale is

determined by the size of individual organelles which are on the order of tens to hundreds of

nanometers. Enabling a close contact is the key element in the success of integration between the

biological and man-made systems2. The integration should be achieved to such a degree that the

contact points of the engineered system/machine comes to the size range of the biological

functional unit2. In the case of biogenic organs, tissues, and cells, this size range extends from

centimeters to nanometers ( for example, in the case of nerves in muscle tissue, they are the

order of centimeters while in the case of ion channels of individual nerve cells, they are of the

order of nanometers)2. Further, the quality of the interfacing between organism and machine is

further increased with the mechanical compatibility. As an illustration, arrays of electrodes

mounted on flexible and stretchable materials were in order to address the mechanical

requirements of soft, deformable tissue. For example, Rogers et al have developed stretchable

form of electronics from various materials including single-walled carbon nanotubes and single-

12

crystal micro- and nanoscale wires and ribbons of gallium nitride, silicon, and gallium arsenide

on flexible substrates22,60

. Further, the influence of Van der Waals forces increases as the size

scale of the structures reduces to nanoscale. This often provides a high degree of adhesion

energy for the nanoscale abiotic functional materials for biointerfacing applications. Thus, in

terms of achieving a high fidelity interfacing, functional electronic and structural nanomaterials

are a size-compatible fit, allowing for bottom up integration and building of multifunctional

bionic systems.

1.6 Nanoscale Functional Electronic and Structural Materials

Functional nanomaterials are capable of exhibiting a wide range of configurations, allowing for

facile integration with the biological systems61

. Over the past decades, several such

nanomaterials exhibiting size dependent properties that are distinct from the bulk were realized

and studied. Among such nanoscale building blocks, carbon nanotubes (CNTs), graphene,

metallic nanoparticles, quantum dots (QDs) and semiconducting nanowires (NWs) gained much

attention and a brief summary of their functional properties are described below.

Materials in the nanometer dimension, in general, possess interesting functional

properties, originating mainly from the quantum confinement effect62

. The confinement of the

charge carriers in these nanoscale materials influences their density of states, leading to size and

shape dependent properties. Such ability to tune the functional properties of the materials without

changing the chemical constituency has opened up a variety of applications in biomedicine and

engineering. In addition, fine tuning of the properties of these nanomaterials can be further

attained via integration into hybrid systems consisting of inorganic-inorganic or inorganic-

13

organic interfaces. These properties make nanoscale functional materials ideal building blocks

for the bottom-up creation of multi-functional bionic systems and devices63,64

.

1.6.1 Carbon Nanomaterials and Graphene

Carbon is the starting material for life and as a consequence of its flexibility in chemical bonding

it exists in unlimited forms of structures, exhibiting a variety of physical properties65

. Most of

the physical properties of the carbon structures are dependent on their physical size and shapes.

Graphene is a 2D allotrope of carbon arranged in a honeycomb lattice (Fig. 1.5). Fullerenes

represent zero dimensional objects with carbon atoms arranged in a spherical structure and from

a physical point of view can be thought of as wrapped up graphene. Carbon nanotubes (CNTs)

are one dimensional objects and can be thought of as rolled up graphene with reconnected ends.

Graphite, the three dimensional allotrope of carbon is made up of stacks of graphene layers.

14

Figure 1.5 Graphene is a 2D building material for carbon materials of all other dimensionalities.

It can be wrapped up into 0D buckyballs, rolled into 1D nanotubes or stacked into 3D graphite

[Image source: Geim et al 66

] Copyright 2007 Macmillan Publishers Ltd (Nature Publishing

Group)

The most notable feature among the electronic properties of graphene is its ambipolar

field effect characteristics- its ability to continuously tune the charge carriers from electrons to

holes (Fig. 1.6) Further, at low temperatures and high magnetic fields, graphene exhibits

quantum Hall effects as a consequence of its exceptional mobility. The quantum Hall effect in

graphene is observed to show plateaus at half integer multiples of instead of the

conventional integer multiples of , believed to be a consequence of its unique band

structures. The strong gate dependence of graphene conductance makes it an ideal nanomaterial

transducer for sensing applications.

15

Figure 1.6 Ambipolar field effect in graphene [Image source: Geim et al66

] Copyright 2007

Macmillan Publishers Ltd (Nature Publishing Group)

In addition to the remarkable electronic properties67

, the mechanical properties of

graphene also make it a stand out material (Fig. 1.7). Graphene has an ultimate tensile strength of

~130 GPa68

. Atomic microscopic (AFM) tests conducted on graphene sheets suspended over

silicon dioxide cavities exhibited a Young’s modulus (different to that of graphite) of 1 TPa68

.

Van der Waals forces play a major role in the mechanical behavior of graphene by keeping its

individual layers together as well as holding it “clamped” to a substrate69

. Pressurized blister

tests performed on graphene layers on silicon dioxide substrates directly measured the adhesion

energy to be 0.45 ± 0.02 J/m2 for monolayer graphene and 0.31 ± 0.03 J/m

2 for samples

containing 2-5 graphene sheets70-72

. These values are comparable to solid/liquid adhesion

energies and are comparatively higher than what is typically seen in micromechanical

structures70

.

Figure 1.7 Blister test for adhesion on graphene [Image source: Huang et al 64

]. Copyright 2011

Macmillan Publishers Ltd (Nature Publishing Group)

This surprisingly high value of adhesion energy is believed to be the result of high level of

flexibility exhibited by graphene layers that allows for a conformal lamination against the

16

surface of a substrate. These unique electronic and mechanical properties make graphene an ideal

nanomaterial building block for bio-interfacing applications.

1.6.2 Semiconducting Quantum Dots

These are nanocrystals consisting of semiconducting materials which exhibit quantum

mechanical properties as a consequence of their small size scales73

. When the semiconducting

material is confined in physical dimensions to size scales that are comparable or lower than the

exciton Bohr diameter, its functional properties will become sensitive to the size and shape as a

result of quantum confinement effect62

. The excitons in QDs are confined in all three spatial

directions and hence they exhibit electronic properties that are between that of bulk material and

single molecules74,75

.

Figure 1.8 Quantum dots with vivid colors stretching from violet to deep red. [Image

courtesy: Antipoff, Wikipedia, previously published: www.plasmachem.com].

Electronic characteristics of quantum dots are thus highly influenced by their size and shape.

Since the band gap of quantum dots is related inversely to their size, the frequency of emitted

light increase as the size decreases. This allows for highly tunable optical properties, thus making

17

the semiconducting quantum dots very interesting for applications such as light emitting diodes

(LEDs), solar cells and in biomedical imaging76

.

1.6.3 Metallic Nanoparticles, Nanowires and Nanorods

Nanoscale objects made of noble metal elements such as gold (Au) and silver (Ag) have unique

optical, electrical and thermal properties that make them interesting for a wide range of

applications (Fig. 1.9). Examples include, applications in electronics as conductive inks taking

advantage of their high electrical conductivity and molecular diagnostics and photonic devices

utilizing their novel optical properties77

. Near-IR absorbing gold nanoparticles produce heat

when excited by light at wavelength corresponding to their surface plasmon resonce (SPR),

making them interesting for applications such as plasmonic photothermal antenna. Additional

applications include diagnostics and in catalysis of chemical reactions.

Figure 1.9 (A) Localized surface plasmon resonance in nanoparticles.( B) Various forms of

nanoparticles (A) gold nanospheres, (B) nanorods, (C) nanoshells, (D) nanocages. [Image

source: Claire et al 77

]. Copyright 2010 Royal Society of Chemistry

18

The conduction electrons at the surface of the metallic nanoparticles undergo collective

oscillation at specific wavelengths corresponding to their SPR resonance. As a consequence they

exhibit strong scattering and absorption properties. These SPR peak wavelengths can be tuned in

a broad range from visible to infra-red regions by simply modifying the size and shape of the

metallic nanoparticles.

1.7 Bio-orthogonal Approaches for Bionic Integration

A seamless engineering of bio-abio interface can be achieved from the nanoscale building blocks

utilizing a set of tools and approaches that enable probing the biological world without

perturbing its natural functionality. Such bio-orthogonal tools are generally grouped under the

broader umbrella of Bionanotechnology78-82

.

1.7.1 Biomimetics: Engineering biocompatibility via biomimicry

Engineering biocompatibility between viable biological systems and functional abiotic materials

is a significant step in achieving an efficient bio-abio interface83-87

. It is often the case, that the

best material suitable for achieving the most efficient engineered device functionality does not

always fulfill the material characteristics necessary for biocompatibility88-90

. Biomimicry offers a

means of modification of functional materials, at macro- and mesoscopic scales, so as to render

them more biocompatible for bionic integration when compared to their unmodified states91-93

.

Chemical functionalization, modification and derivatization of surfaces are often used as a way

to impart biomimetic features to a material. Further, modification of the mechanical properties of

engineering materials to mimic the surrounding tissues of biological systems can also improve

biocompatibility94

.Mechanical property of tissues and organs in body varies in a wide range and

hence matching the mechanical properties of the engineered materials to that of host tissue is a

19

significant part in the bionic device design. Typically the bio-abio interface is constructed

through a cascade of protein and cellular interactions that are determined by the composition and

physical structure of the materials that make up the bionic systems.

1.7.2 Self- Assembly

Self-assembly is the primary approach that natural biological systems make use of for their

formation95

. A beautiful example of self-assembly in a biological system is a bacteriophage (Fig.

1.10). Once broken up in a blender, phages have been shown to have the ability to reassemble in

a test tube in a quasi-mechanical way without the use of any additional templates23

. Further, self-

assembly serves as a powerful tool in achieving nano-scale bionic interfaces. For achieving a

bionic integration, self-assembly serves as a bridge between the top-down approaches and

bottom-up methods96,97

. It makes possible the patterning or hierarchical integration of

nanostructures made by bottom up synthesis in a programmable manner96-99

. In general, self-

assembly serves as the best method in bionic integration scenarios where, the components are too

small for top down, there are too many components for conventional placement, a

multidimensional integration is desired or when the fragility of the biological molecular

components is an issue100,101

.

20

Figure 1.10 Self-assembly (A) Schematic illustration of broken down phage particles

spontaneously self-assemble to the full organism [image adapted from Meyers et al 23

] Copyright

2008 Elsevier.

1.7.3 Phage display

An interesting application of bacteriophages in molecular biomimetics is using them to find

proteins that show unique surface interactions with functional inorganic substrates23

. Naturally

occurring biomolecules such as proteins and peptides are formed via evolutionary processes 102

.

Combinatorial biology techniques such as phage display (PD) enable to create artificial

biomolecules that mimics the naturally occurring proteins for technological applications102,103

.

Conventionally, phage display has been used in biomolecular engineering for the selection of

ligands for proteins and peptides (Fig. 1.11)104-106

. Over the past decades this combinatorial

approach had been adapted and modified to be a powerful bio-orthogonal tool to select material-

specific peptides107-110

.

Figure 1.11 Phage display biopanning to select graphene binding peptides [image source: Cui et

al 111

] Copyright 2010 American Chemical Society.

21

Such phage display based forced evolutionary strategies called in-vitro selection, has

enabled finding of peptide linkers for variety of functional nanomaterials including metallic,

semiconducting and carbon nanomaterials112

.

1.7.4 Tissue Engineering

Tissue engineering utilizes interdisciplinary strategies from various biological sciences and

engineering disciplines to develop and grow bioartificial tissues or organs for applications in

regenerative medicine113,114

. Typically, scaffolds are used to provide cells with mechanical

support and a growth environment with sufficient supply of gaseous and liquid media (Fig. 1.12).

This enables to bring cells in close proximity in a 3D environment, so that they can assemble to

form tissues115,116

. When supplied with sufficient nutrition under the appropriate growth

conditions, the scaffold is degraded and the cells deposit their own extracellular matrix (ECM)

molecules and self-assemble to form 3D tissue structures115,117

. For bionic systems engineering

applications, tissue engineering thus serves as a powerful technique that draws on the principles

of developmental biology to mimic the nature’s way of growth process in creating living bionic

systems64

. Further, incorporation of novel functional materials and the utilization of improved

fabrication techniques can significantly improve the functionality of the grown systems.

22

Figure 1.12 Tissue Engineering Approach. Schematic illustration of tissue engineering

approaches [image source: Khademhosseini et al 115

].

1.8 Additive Manufacturing for Bottom-up Three Dimensional Integration

Additive manufacturing such as 3D printing enables the creation of three dimensional (3D)

structures, in a layer by layer fashion, following a desired pattern. As a consequence, the amount

of material wasted during an additive manufacturing process is minimal, in contrast to the

conventional fabrication techniques that typically utilizes the removal of material from a bulk

substrate. 3D printing based additive manufacturing techniques thus serve as a unique tool to

enable multidimensional integration of a variety of abiotic and biotic functional materials such as

electronic, mechanical, biomaterials and biological cells. When paired with other bio-orthogonal

processes, this approach offers a strategy for simultaneous three dimensional integration between

biological components and abiotic functional nanomaterials in a precise 3D- geometry,

prescribed by a computer aided design (CAD) file to enable the creation of novel bionic hybrids

possessing unique functionalities.

23

1.9 Thesis Overview

This thesis presents a study on the design, development and characterization of a number of

prototype bionic systems with applications in biomedical and energy fields.

Figure 1.13 Thesis Overview- Bionic Systems. Schematic illustration of the three bionic systems

with applications in biomedical and energy presented in succeeding chapters of this thesis. (left)

Bionic Nanosensor, (middle) bionic organs and (right) bionic leaf.

The next chapters of this thesis are organized as follows: Chapter 2 presents the design,

development and testing of biointegrated nanosensors for ubiquitous biomedical sensing

applications. Chapter 3 presents the details of the characterization study on the antimicrobial

peptide based biological molecules, directly integrated on to electronic sensors to act as

bioprobes for bacterial sensing. Chapter 4 discusses the creation of bionic organs with three

dimensionally integrated electronic, structural and biological components using nanomaterials

engineering and additive manufacturing techniques. This concept is illustrated by discussing the

creation of a bionic ear with embedded electronics in a tissue engineered cartilaginous ear

auricle. Chapter 5 presents the design and development of bio-inspired systems for energy, such

as the creation of a bionic leaf, enabled by integrating plant derived photosynthetic functional

24

units with electronic materials and components into a leaf-shaped hierarchical structure, for

harvesting photosynthetic bioelectricity. Finally, the thesis concludes with a summary of the

research and discussion on future directions.

1.10 References

1 Learmonth, I. D., Young, C. & Rorabeck, C. The operation of the century: total hip

replacement. Lancet 370, 1508-1519 (2007).

2 Giselbrecht, S., Rapp, B. E. & Niemeyer, C. M. The chemistry of cyborgs - Interfacing

technical devices with organisms. Angewandte Chemie - International Edition 52, 13942-

13957 (2013).

3 Crubezy, E., Murail, P., Girard, L. & Bernadou, J. P. False teeth of the Roman world [3].

Nature 391, 29 (1998).

4 Nachtigall, W. Bionik – Grundlagen und Beispiele fu¨ r Ingenieure und

Naturwissenschaftler. Berlin: . Springer-Verlag (1998).

5 Katz, E. in Implantable Bioelectronics 1-5 (Wiley-VCH Verlag GmbH & Co. KGaA,

2014).

6 Mirkin, C., Nel, A. & Thaxton, C. S. in Nanotechnology Research Directions for Societal

Needs in 2020 Vol. 1 Science Policy Reports Ch. 8, 305-374 (Springer Netherlands,

2011).

7 Jin, C. et al. Injectable 3-D fabrication of medical electronics at the target biological

tissues. Scientific Reports 3 (2013).

25

8 Zopf, D. A., Hollister, S. J., Nelson, M. E., Ohye, R. G. & Green, G. E. Bioresorbable

airway splint created with a three-dimensional printer. New England Journal of Medicine

368, 2043-2045 (2013).

9 Piccolino, M. Animal electricity and the birth of electrophysiology: The legacy of Luigi

Galvani. Brain Research Bulletin 46, 381-407 (1998).

10 Ricotti, L., Assaf, T., Dario, P. & Menciassi, A. Wearable and implantable pancreas

substitutes. Journal of Artificial Organs 16, 9-22 (2013).

11 Wolbring, G. et al. Emerging Therapeutic Enhancement Enabling Health Technologies

and Their Discourses: What Is Discussed within the Health Domain? Healthcare 1, 20-52

(2013).

12 Clausen, J. Man, machine and in between. Nature 457, 1080-1081 (2009).

13 Service, R. F. Can sensors make a home in the body? Science 297, 962-963 (2002).

14 Service, R. F. The cyborg era begins. Science 340, 1162-1165 (2013).

15 Mannoor, M. S. et al. 3D printed bionic ears. Nano Letters 13, 2634-2639 (2013).

16 Clynes, M. E. & Kline, N. S. Cyborgs and space. Astronautics, 26-27 (1960).

17 Maharbiz, M. M. & Sato, H. Cyborg beetles. Scientific American 303, 94-99 (2010).

18 Wang, H., Huai, R., Yang, J. & Su, X. Review of research progress in biorobot.

Advanced Materials Research 655-657, 1061-1065 (2013).

19 Craelius, W. The bionic man: Restoring mobility. Science 295, 1018-1019+1021 (2002).

20 Lavine, M., Roberts, L. & Smith, O. If I only had a. Science 295, 995 (2002).

21 Green, D. W. Tissue bionics: Examples in biomimetic tissue engineering. Biomedical

Materials 3 (2008).

22 Kim, D. H. et al. Epidermal electronics. Science 333, 838-843 (2011).

26

23 Meyers, M. A., Chen, P. Y., Lin, A. Y. M. & Seki, Y. Biological materials: Structure and

mechanical properties. Progress in Materials Science 53, 1-206 (2008).

24 Meyers, M. A., McKittrick, J. & Chen, P. Y. Structural biological materials: Critical

mechanics-materials connections. Science 339, 773-779 (2013).

25 Seki, Y., Schneider, M. S. & Meyers, M. A. Structure and mechanical behavior of a

toucan beak. Acta Materialia 53, 5281-5296 (2005).

26 Whitesides, G. M. Organic materials science. MRS Bulletin 27, 56-65 (2002).

27 Fratzl, P. & Weinkamer, R. Nature's hierarchical materials. Progress in Materials

Science 52, 1263-1334 (2007).

28 Alberts, B. Cell biology: the endless frontier. Molecular biology of the cell 21, 3785

(2010).

29 Sundar, V. C., Yablon, A. D., Grazul, J. L., Ilan, M. & Aizenberg, J. Fibre-optical

features of a glass sponge. Nature 424, 899-900 (2003).

30 Fritz, M. & Morse, D. E. The formation of highly organized biogenic polymer/ceramic

composite materials: The high-performance microaluminate of molluscan nacre. Current

Opinion in Colloid and Interface Science 3, 55-62 (1998).

31 García, A. J. Get a grip: Integrins in cell-biomaterial interactions. Biomaterials 26, 7525-

7529 (2005).

32 Vukusic, P. & Sambles, J. R. Photonic structures in biology. Nature 424, 852-855 (2003).

33 Parker, A. R. & Lawrence, C. R. Water capture by a desert beetle. Nature 414, 33-34

(2001).

34 Ashby, M. F. Materials Selection in Mechanical Design (1992).

35 Altman, G. H. et al. Silk-based biomaterials. Biomaterials 24, 401-416 (2003).

27

36 Vollrath, F. Strength and structure of spiders' silks. Journal of Biotechnology 74, 67-83

(2000).

37 Gosline, J. M., DeMont, M. E. & Denny, M. W. The structure and properties of spider

silk. Endeavour 10, 37-43 (1986).

38 Song, F., Soh, A. K. & Bai, Y. L. Structural and mechanical properties of the organic

matrix layers of nacre. Biomaterials 24, 3623-3631 (2003).

39 Ryder, M. L. Structure of Rhinoceros Horn. Nature 193, 1199-1201 (1962).

40 Brink, D. J. & Van Der Berg, N. G. Structural colours from the feathers of the bird

Bostrychia hagedash. Journal of Physics D: Applied Physics 37, 813-818 (2004).

41 Frenkel, M. J. & Gillespie, J. M. The proteins of the keratin component of bird's beaks.

Australian journal of biological sciences 29, 467-479 (1976).

42 Feughelman, M. Mechanical properties and structure of alpha-keratin fibres. Mechanical

Properties and Structure of Alpha-Keratin Fibres (1997).

43 Knowles, T. P. J. & Buehler, M. J. Nanomechanics of functional and pathological

amyloid materials. Nature Nanotechnology 6, 469-479 (2011).

44 Bao, G. & Suresh, S. Cell and molecular mechanics of biological materials. Nature

Materials 2, 715-725 (2003).

45 Vincent, J. F. V. & Wegst, U. G. K. Design and mechanical properties of insect cuticle.

Arthropod Structure and Development 33, 187-199 (2004).

46 Ji, B. & Gao, H. Mechanical properties of nanostructure of biological materials. Journal

of the Mechanics and Physics of Solids 52, 1963-1990 (2004).

47 Christian, J. W. Physical Metallurgy (1965).

48 Gibson, L. J. & Ashby, M. F. Cellular Solids: Structure and Properties (1997).

28

49 Ratner, B. D., Hoffman, A. S., Schoen, F. J. & Lemons, J. E. Biomaterials Science: An

Introduction to Materials in Medicine (1996).

50 Thompson, D. W. On growth and form. 2nd ed., reprinted (1968).

51 Vincent, J. F. V. Structural biomaterials. revised ed.

52 Vincent, J. F. V. & Currey, J. D. The Mechanical Properties of Biological Materials

(1980).

53 Skalak, R., Farrow, D. A. & Hoger, A. Kinematics of surface growth. Journal of

mathematical biology 35, 869-907 (1997).

54 Weiner, S., Addadi, L. & Wagner, H. D. Materials design in biology. Materials Science

and Engineering C 11, 1-8 (2000).

55 Arzt, E. Biological and artificial attachment devices: Lessons for materials scientists from

flies and geckos. Materials Science and Engineering C 26, 1245-1250 (2006).

56 Vogel, H. G. Influence of age, treatment with corticosteroids and strain rate on

mechanical properties of rat skin. BBA - General Subjects 286, 79-83 (1972).

57 Jackson, A. P., Vincent, J. F. V. & Turner, R. M. A physical model of nacre. Composites

Science and Technology 36, 255-266 (1989).

58 Vincent, J. F. V. Arthropod cuticle: A natural composite shell system. Composites Part

A: Applied Science and Manufacturing 33, 1311-1315 (2002).

59 The Electronics – Biological Matter Interface.

http://www.physics.ucla.edu/research/biophysics/research/White%20Papers/bio-

electronics%20interface.pdf.

60 Ahn, J.-H. et al. Heterogeneous Three-Dimensional Electronics by Use of Printed

Semiconductor Nanomaterials. Science 314, 1754-1757 (2006).

29

61 Wallace, G. G., Higgins, M. J., Moulton, S. E. & Wang, C. Nanobionics: The impact of

nanotechnology on implantable medical bionic devices. Nanoscale 4, 4327-4347 (2012).

62 Thomas G, K. Tuning functional properties: From nanoscalebuilding blocks to hybrid

nanomaterials. Current Trends in Science (2009).

63 Cohen-Karni, T. & Lieber, C. M. Nanowire nanoelectronics: Building interfaces with

tissue and cells at the natural scale of biology. Pure and Applied Chemistry 85, 883-901

(2013).

64 Huang, R. Graphene: Show of adhesive strength. Nature Nanotechnology 6, 537-538

(2011).

65 Castro Neto, A. H., Guinea, F., Peres, N. M. R., Novoselov, K. S. & Geim, A. K. The

electronic properties of graphene. Reviews of Modern Physics 81, 109-162 (2009).

66 Geim, A. K. & Novoselov, K. S. The rise of graphene. Nature Materials 6, 183-191

(2007).

67 Wilson, M. Electrons in atomically thin carbon sheets behave like massless particles.

Physics Today 59, 21-23 (2006).

68 Lee, C., Wei, X., Kysar, J. W. & Hone, J. Measurement of the Elastic Properties and

Intrinsic Strength of Monolayer Graphene. Science 321, 385-388 (2008).

69 Huang, R. Graphene: Show of adhesive strength. Nature Nanotechnology 6, 537-538

(2011).

70 Koenig, S. P., Boddeti, N. G., Dunn, M. L. & Bunch, J. S. Ultrastrong adhesion of

graphene membranes. Nature Nanotechnology 6, 543-546 (2011).

71 Ishigami, M., Chen, J. H., Cullen, W. G., Fuhrer, M. S. & Williams, E. D. Atomic

structure of graphene on SiO 2. Nano Letters 7, 1643-1648 (2007).

30

72 Zong, Z., Chen, C. L., Dokmeci, M. R. & Wan, K. T. Direct measurement of graphene

adhesion on silicon surface by intercalation of nanoparticles. Journal of Applied Physics

107 (2010).

73 Rogach, A. L. Semiconductor nanocrystal quantum dots synthesis, assembly,

spectroscopy and applications. (2008).

74 Manasreh, O. Introduction to Nanomaterials and Devices. (2011).

75 Tetyana Torchynska, Y. V. Semiconductor II-VI Quantum Dots with Interface States and

Their Biomedical Applications, Advanced Biomedical Engineering, ISBN: 978-953-307-

555-6. (2011).

76 Ciftja, O. Quantum dots: Applications, synthesis and characterization. (2013).

77 Cobley, C. M., Chen, J., Cho, E. C., Wang, L. V. & Xia, Y. Gold nanostructures: A class

of multifunctional materials for biomedical applications. Chemical Society Reviews 40,

44-56 (2011).

78 Tian, B. & Lieber, C. M. Vol. 6 31-51 (2013).

79 Zimmerman, J., Parameswaran, R. & Tian, B. Nanoscale semiconductor devices as new

biomaterials. Biomaterials Science (2014).

80 Fine, D. et al. Silicon Micro- and Nanofabrication for Medicine. Advanced Healthcare

Materials 2, 632-666 (2013).

81 Vincent, J. F. V. & Mann, D. L. Systematic technology transfer from biology to

engineering. Philosophical Transactions of the Royal Society A: Mathematical, Physical

and Engineering Sciences 360, 159-173 (2002).

82 Sletten, E. M. & Bertozzi, C. R. Bioorthogonal chemistry: Fishing for selectivity in a sea

of functionality. Angewandte Chemie - International Edition 48, 6974-6998 (2009).

31

83 Guiseppi-Elie, A., Kotanen, C., Karunwi, O. & Wilson, A. N. 14 edn 13-29.

84 Karunwi, O., Wilson, A. N., Kotanen, C. & Guiseppi-Elie, A. Engineering the abio-bio

interface to enable more than moore in functional bioelectronics. Journal of the

Electrochemical Society 160, B60-B65 (2013).

85 Sarikaya, M. An introduction to biomimetics: A structural viewpoint. Microscopy

Research and Technique 27, 360-375 (1994).

86 Sarikaya, M., Tamerler, C., Jen, A. K. Y., Schulten, K. & Baneyx, F. Molecular

biomimetics: Nanotechnology through biology. Nature Materials 2, 577-585 (2003).

87 Tamerler, C. & Sarikaya, M. Molecular biomimetics: nanotechnology and

bionanotechnology using genetically engineered peptides. Philosophical Transactions of

the Royal Society A: Mathematical, Physical and Engineering Sciences 367, 1705-1726

(2009).

88 Förster, Y. et al. Surface modification of implants in long bone. Biomatter 2, 149-157

(2012).

89 Zhang, L. et al. Zwitterionic hydrogels implanted in mice resist the foreign-body

reaction. Nature Biotechnology 31, 553-556 (2013).

90 Sethi, M., Pacardo, D. B. & Knecht, M. R. Biological surface effects of metallic

nanomaterials for applications in assembly and catalysis. Langmuir 26, 15121-15134

(2010).

91 Sarikaya, M. et al. Biomimetic model of a sponge-spicular optical fiber-mechanical

properties and structure. Journal of Materials Research 16, 1420-1428 (2001).

92 Naik, R. R., Stringer, S. J., Agarwal, G., Jones, S. E. & Stone, M. O. Biomimetic

synthesis and patterning of silver nanoparticles. Nature Materials 1, 169-172 (2002).

32

93 Mann, S. The biomimetics of enamel: A paradigm for organized biomaterials synthesis.

CIBA Foundation Symposia, 261-274 (1997).

94 Busch, S. Regeneration of human tooth enamel. Angewandte Chemie - International

Edition 43, 1428-1431 (2004).

95 Snead, M. L. et al. Protein self-assembly creates a nanoscale device for

biomineralization. Materials Science and Engineering C 26, 1296-1300 (2006).

96 Bowden, N., Terfort, A., Carbeck, J. & Whitesides, G. M. Assembly of mesoscale objects

into ordered two-dimensional arrays. Science 276, 233-235 (1997).

97 Wu, H., Thalladi, V. R., Whitesides, S. & Whitesides, G. M. Using hierarchical self-

assembly to form three-dimensional lattices of spheres. Journal of the American

Chemical Society 124, 14495-14502 (2002).

98 Whitesides, G., Kriebel, J. & Mayers, B. in Nanoscale Assembly Nanostructure Science

and Technology (ed WilhelmT S. Huck) Ch. 9, 217-239 (Springer US, 2005).

99 Lee, S. W., Mao, C., Flynn, C. E. & Belcher, A. M. Ordering of quantum dots, using

genetically engineered viruses. Science 296, 892-895 (2002).

100 Kisailus, D., Truong, Q., Amemiya, Y., Weaver, J. C. & Morse, D. E. Self-assembled

bifunctional surface mimics an enzymatic and templating protein for the synthesis of a

metal oxide semiconductor. Proceedings of the National Academy of Sciences of the

United States of America 103, 5652-5657 (2006).

101 Huang, Y. et al. Programmable assembly of nanoarchitectures using genetically

engineered viruses. Nano Letters 5, 1429-1434 (2005).

102 Seker, U. O. S. & Demir, H. V. Material binding peptides for nanotechnology. Molecules

16, 1426-1451 (2011).

33

103 Naik, R. R. et al. Peptide templates for nanoparticle synthesis derived from polymerase

chain reaction-driven phage display. Advanced Functional Materials 14, 25-30 (2004).

104 Rader, C. & Barbas Iii, C. F. Phage display of combinatorial antibody libraries. Current

Opinion in Biotechnology 8, 503-508 (1997).

105 Dunn, I. S. Phage display of proteins. Current Opinion in Biotechnology 7, 547-553

(1996).

106 Pande, J., Szewczyk, M. M. & Grover, A. K. Phage display: Concept, innovations,

applications and future. Biotechnology Advances 28, 849-858 (2010).

107 Seeman, N. C. & Belcher, A. M. Emulating biology: Building nanostructures from the

bottom up. Proceedings of the National Academy of Sciences of the United States of

America 99, 6451-6455 (2002).

108 Morita, Y., Ohsugi, T., Iwasa, Y. & Tamiya, E. A screening of phage displayed peptides

for the recognition of fullerene (C60). Journal of Molecular Catalysis B: Enzymatic 28,

185-190 (2004).

109 Kriplani, U. & Kay, B. K. Selecting peptides for use in nanoscale materials using phage-

displayed combinatorial peptide libraries. Current Opinion in Biotechnology 16, 470-475

(2005).

110 Whaley, S. R., English, D. S., Hu, E. L., Barbara, P. F. & Belcher, A. M. Selection of

peptides with semiconductor binding specificity for directed nanocrystal assembly.

Nature 405, 665-668 (2000).

111 Cui, Y. et al. Chemical functionalization of graphene enabled by phage displayed

peptides. Nano Letters 10, 4559-4565 (2010).

34

112 Slocik, J. M., Stone, M. O. & Naik, R. R. Synthesis of gold nanoparticles using

multifunctional peptides. Small 1, 1048-1052 (2005).

113 Langer, R. & Tirrell, D. A. Designing materials for biology and medicine. Nature 428,

487-492 (2004).

114 Tian, B. et al. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues.

Nature Materials 11, 986-994 (2012).

115 Khademhosseini, A., Langer, R., Borenstein, J. & Vacanti, J. P. Microscale technologies

for tissue engineering and biology. Proceedings of the National Academy of Sciences of

the United States of America 103, 2480-2487 (2006).

116 George, A. & Ravindran, S. Protein templates in hard tissue engineering. Nano Today 5,

254-266 (2010).

117 Dvir, T., Timko, B. P., Kohane, D. S. & Langer, R. Nanotechnological strategies for

engineering complex tissues. Nature Nanotechnology 6, 13-22 (2011).

35

Chapter 2

Bionic Nanosensors§

2.1 Overview

Direct interfacing of nanosensors onto biomaterials could revolutionize health quality

monitoring and adaptive threat detection. Graphene is capable of highly sensitive analyte

detection. Here we show that the nanoscale nature of graphene allows it to be printed onto water-

soluble silk. This in turn permits intimate biotransfer of graphene nanosensors onto biomaterials,

including tooth enamel. The result is a fully interfaced sensing platform which can be tuned to

detect target analytes. For example, via self-assembly of peptides onto graphene, we show

bioselective detection of bacteria at single cell levels. Incorporation of a resonant coil eliminates

the need for onboard power and external connections.

Combining these elements yields two-tiered interfacing of peptide-graphene nanosensors

with biomaterials. In particular, we demonstrate integration onto a tooth for remote monitoring

of respiration and bacteria detection in saliva. Overall, this strategy of hierarchically interfacing

biomolecules with nanosensors and biomaterials represents a versatile approach for detecting

biochemical targets.

2.2 Biointegration of Sensors

§ The work reported in this chapter is based on the following original publication: Mannoor, M. S.; Tao, H.; Clayton,

J. D.; Sengupta, A.; Kaplan, D. L.; Naik, R. R.; Verma, N.; Omenetto, F. G.; McAlpine, M. C., Graphene-based

wireless bacteria detection on tooth enamel. Nature Communications 2012, 3, 763.

36

Interfacing electronic devices and sensors with biomaterials has been of interest for decades, for

on-body physiological and analytical measurements1-3

. Traditionally, device designs for such

applications involved either implantation of device electrodes into tissue4, or mechanical

mounting of components on the body using braces, clamps or adhesive tapes. Such systems

encased rigid and bulky onboard power sources, associated circuitry, and direct physical

connections between the sensing probes and data processing electronics5,6

. Further, the large

form factors and rigid substrates prohibited intimate integration on the soft and curvilinear

surfaces of biological tissues, causing discomfort during continuous use. Device designs and

platforms that minimize the mechanical discrepancy between such abiotic/biotic interfaces are

thus highly desired for conformal biointegrated electronics and sensors.

Electronic sensors based on nanoscale materials such as nanowires7, carbon nanotubes

(CNTs)8, and graphene

9 have been shown to boast parts-per-billion (ppb) sensitivities, a

consequence of the high surface areas of these materials. CNT-based composite materials with

passive circuits have enabled wireless chemical and gas sensors10

. Single-atom-thick, sp2

graphene is a particularly interesting material due to its remarkable electrical11

, mechanical12

,

and sensing13,14

properties. The growth of graphene films on supporting metallic films (Ni or Cu)

using chemical vapor deposition (CVD) methods15

, combined with post-etching of the

underlying metal, offers the ability to efficiently transfer graphene films to other substrates over

large areas16

for biocompatible sensing and flexible electronics applications17,18

. This is enabled

by graphene’s intrinsic strength of 42 N/m and Young’s modulus of ~1 TPa19

, as well as the high

interfacial adhesion exhibited by graphene to substrates (adhesive energy of 0.45 J/m2 on

SiO2)20

. These properties render graphene an ideal active material for direct interfacing onto

rugged surfaces.

37

Silk, a textile industry staple for thousands of years, has recently sparked increased

interest within the materials science community due to its impressive mechanical properties

including high elastic modulus, tensile strength, ductility, and toughness21,22

. As a result, silk

films have been shown to be an efficient “middleman” medium for transferring materials such as

passive metallic electrodes onto tissues via intimate contact and dissolution – a consequence of

the elasticity and biodegradability imparted by the unique molecular structure of silk. Silk films

have been patterned with metal electrodes and intimately “bioresorbed” onto brain and skin

tissues for electro-mapping experiments21,23

. Recent work has demonstrated the ability to

fabricate active electronic components such as transistors24

and metamaterials25

on films of

regenerated silk.

In addition to sensor sensitivity, selectivity toward defined chemical and biological

targets is a challenging goal in which bioinspired approaches are particularly useful. Aptamer

functionalized nanotube electrodes have been shown to detect single bacterial cells in real time26

.

Further, we have recently shown that phage display can be utilized to determine peptide

sequences which selectively bind to CNTs and graphene8,27-29

. This has enabled the generation of

bifunctional peptides containing a carbon nanomaterial recognition moiety combined with an

analyte binder to noncovalently self-assemble and impart selectivity on graphene sensor arrays.

Our recent study has demonstrated the ability of naturally occurring antimicrobial peptides

(AMPs) to serve as robust biorecogniton moieties in electronic biosensing platforms30

(please see

chapter 3 for details). Unlike antibodies, AMPs are significantly more stable and exhibit

broadband detection for a range of pathogenic bacteria31,32

.

Pathogenic contamination and resistant “superbug” infections remain critical concerns in

both developed and developing nations, due to extremely low minimum infective doses (MID)

38

for many bacteria and the lack of inexpensive and portable methods to detect at these limits33

.

Currently available methods for the detection of microbiological threats utilize specific

enrichment media to separate, identify and count bacterial cells26

. Alternatively, polymerase

chain reaction (PCR)34

and DNA-based nanobarcode35

detection strategies have proven to be fast

and highly sensitive, but such methods require pretreatment and cell lysis to extract DNA. An

alternative strategy is the development of methods that allow for direct and sensitive detection of

whole microbial cells or endotoxins. Particularly interesting would be sensors that could be

directly interfaced with contamination sources, including the body, food, and hospital equipment.

Here we describe a novel approach to directly interfacing passive, wireless graphene

nanosensors onto biomaterials via silk bioresorption. First, graphene nanosensors are

comprehensively printed onto water-soluble silk thin film substrates. The graphene is then

contacted by interdigitated electrodes, which are simultaneously patterned with an inductive coil

antenna. Finally, the graphene/electrode/silk hybrid structure is transferred to biomaterials such

as tooth enamel or tissue, followed by functionalization with bifunctional graphene-AMP

biorecognition moeities. The resulting device architecture is capable of extremely sensitive

chemical and biological sensing, with detection limits down to a single bacterium, while also

wirelessly achieving remote powering and readout. The generation of “biotransferrable” sensors

combined with high sensitivity and selectivity may provide a first line of defense against

pathogenic threats at the point of contamination.

2.3 Results & Discussion

2.3.1 Graphene/silk sensor

39

The fundamental operation and key functionalities of the sensor design are schematically

illustrated in Fig 2.1. First, a graphene-based sensing element with wireless readout coil is

generated on silk fibroin (Fig. 2.1a). Next, the ultra-thin nanosensors are intimately

biotransferred from the silk platform onto biomaterials, such as tooth enamel, via dissolution of

the supporting silk film (Fig. 2.1b). The extremely large surface area of the graphene and

electrodes ensures high adhesive conformability to the rugged surfaces of biomaterials.

Specificity in biological recognition is achieved by self-assembling designer bifunctional AMP-

graphene peptides onto the graphene monolayer (Fig. 2.1c), such that non-covalent

functionalization of graphene can be achieved without degrading its electronic sensing

properties. Further, Figure 2.1c illustrates the two other major functionalities of the hybrid

biosensor unit: battery free operation, and remote wireless sensing capability. Upon recognition

and binding of specific bacterial targets by the immobilized peptides (Fig. 2.1d), the electrical

conductivity of the graphene film is modulated and wirelessly monitored using an inductively

coupled radio frequency (RF) reader device. The key functionalities of the graphene/silk hybrid

sensing elements are thus derived from a synergistic integration of the individual materials

properties and components.

Figure 2.1 Biotransferrable graphene wireless nanosensor. (a) Graphene is printed onto

bioresorbable silk and contacts are formed containing a wireless coil. (b) Biotransfer of the

40

nanosensing architecture onto the surface of a tooth. (c) Magnified schematic of the sensing

element, illustrating wireless readout. (d) Binding of pathogenic bacteria by peptides self-

assembled on the graphene nanotransducer.

2.3.2 Materials integration and characterization.

Large area graphene monolayers are integrated with water soluble silk fibroin films (ca. 50

microns thick) using a simple transfer printing process (Fig. 2.2a, see Methods). Electrode

patterns are then incorporated onto the silk-graphene hybrid films via shadow mask assisted

electron beam evaporation of gold to generate the biosensor (Fig. 2.2b). Specifically, the

architecture consists of a parallel LRC resonant circuit with a gold inductive coil for wireless

transmission, and interdigitated capacitive electrodes contacting graphene resistive sensors. The

resulting device is a passive wireless telemetry system, obviating onboard power sources and

external connections. The thin film sensing elements thus fabricated on silk are then

biotransferred and intimately integrated onto a variety of substrates. Complete dissolution of the

silk matrix template in water led to strong attachment of the graphene-Au electrode structure

within a time period of 15-20 minutes. Significantly, Figure 2.2c shows a photograph of the

graphene nanomaterial with patterned gold electrodes integrated onto the surface of a human

molar, and Figure 2.2d shows a photograph of the graphene sensor biotransferred directly on

muscle tissue. To ensure complete dissolution of the silk substrate, we performed sensor

biotransfer experiments using fluorescent silk films, before and after the dissolution of the silk

(Fig. 2.2e, left). Complete quenching of fluorescence was verified after immersion in water for

20 min (Fig. 2.2e, right). Electronic and structural properties of the graphene were interrogated

using Raman spectroscopy36

: Figure 2.2f shows the Raman spectrum of graphene following

biotransfer onto a tooth surface

41

Figure 2.2 Graphene biotransfer and characterization. (a) Graphene printed onto bioresorbable

silk film. (b) Passive wireless telemetry system consisting of a planar meander line inductor and

42

interdigitated capacitive electrodes integrated onto the graphene/silk film. (c,d) Graphene

nanosensor biotransferred onto the surface of a human molar (c), and onto muscle tissue (d).

Scale bars are 5 mm. (e) Fluorescent image of sensor fabricated on a fluorescent silk film and

laminated onto the surface of a tooth, before (left) and after (right) dissolution of silk. Absence of

fluorescence signal confirmed complete removal of the silk matrix. Scale bar is 250 µm. (f)

Raman spectrum following interfacing of graphene onto the tooth surface.

The spectrum is in good agreement with other graphene monolayer spectra36

, and the

phosphate ν1 peak from the tooth enamel substrate is evident37

. Raman spectra of the bare tooth

enamel and silk fibroin film are provided in Fig. 2.3.

Figure 2.3 Raman spectra of tooth enamel and Bombyx mori silk fibroin film. Raman spectra of

(a) bare tooth enamel surface (b) silk fibroin substrate. The amide band at 1660 cm-1

indicates

the presence of domains of silk I structure in the silk film38

.

43

Sensor integration on human skin is shown in Fig 2.4

Figure 2.4 Biotransfer of the sensor onto skin. (a) Optical image of the graphene based wireless

sensing element on a water soluble silk fibroin substrate. (b) Conformal transfer of the sensing

element onto human skin via the dissolution of the supporting silk substrate. (c) Magnified

optical image of the sensor after transfer. Scale bars are 7 mm.

A full-wave electromagnetic simulation tool, Ansoft HFSS, was utilized to simulate and

design the planar coil antenna and interdigitated capacitive electrode geometries (Figure 2.5).

44

Figure 2.5 Return loss (S11) of the wireless sensing element. The S11 parameter of various

designs of the wireless sensing element was simulated using Ansoft HFSS software. Inset shows

the schematic of the LC design (left) and image of the design implemented on a Si chip (right).

Optical characterization of the transferred graphene revealed good structural integrity

(Fig. 2.6)

45

Figure 2.6 Optical microscopy images of graphene on surfaces. (a) Optical microscope image of

graphene film on Ni surface. (b) Optical microscope image of graphene transferred onto a

surface via the dissolution of the silk film.

For sensors interfaced with skin, no degradation or delamination was observed following

mild rinsing in running water as shown in Fig. 2.7.

46

Figure 2.7 Stability of sensor in running water. Optical images of (a) biotransferred sensor onto

a human arm, (b) mild rinsing in running water, and (c) the sensor following exposure to running

water. Scale bars are 1 cm.

47

The mechanical stability of the sensor on tooth enamel was analyzed via agitation in

commercial mouthwash for a period of 3 min followed by comparative Raman spectra analysis

(Fig. 2.8)

Figure 2.8 Structural integrity testing of sensor biotransferred onto bovine tooth enamel. Optical

images of (a) sensor interfaced on tooth before testing, (b) immersion of the tooth in mouthwash,

(c) vortexing of the tooth sensor in mouthwash, (d) structurally intact sensing element after

vortexing, and (e) the tooth after removal from the solution. All scale bars are 1 cm. (f) Raman

48

spectrum of the graphene surface before vortexing. (g) Raman spectrum of the graphene sensor

after vortexing in mouthwash, showing higher edge-induced D band intensity39

.

2.3.3 Functionalization of graphene with AMPs

The ability to specifically detect various species of pathogenic bacteria is useful for biomedical

applications and food, water, and air quality monitoring. Our previous study30

demonstrated that

AMPs may serve as robust biorecognition molecules with broad-spectrum activity towards

various pathogenic bacteria. Further, we have recently shown that phage display can be utilized

to determine peptide sequences which selectively bind to carbon nanomaterials8,27,40

. Here,

graphene nanosensors were functionalized with a chemically synthesized bifunctional peptide,

consisting of 1) a dodecapeptide graphene binding peptide (GBP), 2) a triglycine linker, and 3)

the AMP odorranin-HP (OHP), which shows activity toward both the Gram-negative bacteria E.

coli and H. pylori and the Gram-positive bacteria S. aureus41

. Figure 2.9a shows a molecular

drawing of the resulting 38 amino acid sequence, HSSYWYAFNNKT-GGG-

GLLRASSVWGRKYYVDLAGCAKA (GBP-OHP). Raman spectroscopic analysis of the

peptide functionalized graphene surface indicated slight doping of graphene (Fig. 2.9b),

consistent with our previous electronic measurements40

.

The activity of the immobilized GBP-OHP toward S. aureus and H. pylori cells were

compared via fluorescent assays with activity toward erythrocytes (Figs. 2.9c, 2.9d and 2.9e

respectively). The concentrations of bacterial cells used for the assays was 106 CFU/mL and the

concentration for erythrocytes was 40% hematocrit. The assays clearly show higher binding to

bacterial cells, evident by the higher fluorescent density. The specificity of the interaction of S.

aureus cells with GBP-OHP peptides was further analyzed via flow-through electrical

49

measurements of the graphene sensors (Fig. 2.9f). Following elution with DI water, the response

of GBP-OHP toward S. aureus is four-fold larger than the response toward both a GBP

functionalized sensor and a GBP-OHP sensor exposed to erythrocytes.

50

51

Figure 2.9 Graphene functionalization with Antimicrobial peptides. (a) Molecular model of a

bifunctional peptide consisting of a graphene binding peptide (GBP) coupled to an antimicrobial

peptide odorranin-HP (OHP) via a triglycine linker (-GGG-). (b) Raman spectra before (blue

line) and after (red line) immobilization of bifunctional peptides on the graphene surface. The

inset shows a shift in the 2D band of graphene due to molecular doping. (c-e) Fluorescent images

of the binding of S. aureus (c), H. pylori (d), and erythrocytes (e) to GBP-OHP functionalized

graphene. Scale bars are 10 μm. (f) Selectivity of GBP-OHP functionalized graphene sensor. ( )

Indicates resistance of graphene sensor functionalized with GBP-OHP upon exposure to DI

water. ( ) Indicates resistance of graphene sensor functionalized with only GBP upon exposure

to S. aureus. ( ) Indicates resistance of graphene sensor functionalized with GBP-OHP upon

exposure to erythrocytes. ( ) Indicates resistance of graphene sensor functionalized with GBP-

OHP upon exposure to S. aureus. Dotted line indicates elution with DI water. Inset shows image

of the flow-through measurement system. Scale bar is 1 cm. Concentrations of the bacterial cells

used for the assays is 106 CFU/mL, and the concentration of erythrocytes is 40% hematocrit.

2.3.4 Single Bacterium Detection

Detection of bacteria at the single-cell level is a critical goal for biosensors due to the

extremely low MID of many bacteria18,33,42

. To investigate the responsiveness of the graphene

nanosensors towards single bacterial cells, time-dependent resistance data and optical

measurements were carried out in parallel. Importantly, as shown in Figure 2.10a, simultaneous

collection of electrical and fluorescence data from bare graphene sensors in the presence of

fluorescently labeled E. coli cells clearly indicate a discrete change in electrical resistance

corresponding to the binding and unbinding of a single bacterial cell from the graphene surface.

The approximate 0.6% decrease in resistance due to binding of bacteria is readily explained by

52

the fact that Gram-negative bacteria such as E. coli possess an outer membrane with negatively

charged lipopolysaccharide (LPS), indicating p-type behavior of the graphene transducer

consistent with other studies11

.

Next, we determined the effect of the immobilized AMPs on bacterium binding. The

inset of Figure 2.10b shows a fluorescent image of the graphene nanosensor functionalized with

FITC-labeled GBP-OHP. The result suggests uniform coverage of the graphene surface with

peptides and selective recognition of the peptide toward graphene relative to the gold electrodes.

Simultaneous resistance and optical data were recorded on graphene sensors functionalized with

GBP-OHP (Fig. 2.10b). Significantly, compared to the bare graphene nanosensor (Fig. 2.10a),

peptide-coated electrodes display bacterium binding without concomitant unbinding, suggesting

a strong interaction between the bacterium and immobilized peptides.

53

Figure 2.10 Single bacterium detection. (a) Electrical resistance (upper) and fluorescence

(lower) data recorded simultaneously vs. time showing binding/unbinding of a single E. coli

bacterium on a bare graphene nanosensor. Images are (12 μm)2. (b) Resistance (upper) and

optical (lower) data recorded simultaneously vs. time showing binding of a single E. coli

bacterium on a peptide-functionalized graphene nanosensor. Images are (20 μm)2. Inset shows

54

fluorescent image of peptide functionalized graphene surface (green), with the black regions

representing electrodes. Scale bar is 250 µm.

2.3.5 Wireless Remote Query Monitoring of S. aureus.

A major functionality of the sensor construction is the wireless remote query capability. Certain

strains of S. aureus are notoriously antibiotic-resistant and responsible for over 500,000 post-

surgical wound infections in the US each year43,44

. S. aureus has been reported to survive up to 9

weeks on standard plastic and similar dry hospital environments44

. To simulate the use of

graphene wireless sensors in hospital sanitation and biohazard monitoring, we interfaced the

nanosensors with an intravenous (IV) bag (Fig. 2.11a). Next, 1 µL solutions containing various

concentrations (103-10

8 CFU/mL) of bacterial cells were allowed to dry on the sensor surface for

30 min. Figure 2.11b plots the bandwidth of the sensor corresponding to the different

concentrations of S. aureus cells incubated on the sensor surface. The percentage change in

graphene resistance is depicted in Figure 2.11c; significantly, wireless detection limits of 1

bacterium/µL were achievable in wireless operation mode. The change in graphene resistance

upon bacterial binding was wirelessly monitored as the bandwidth change in the sensor

resonance curve10

.

55

56

Figure 2.11 Wireless monitoring of S. aureus. (a) Optical image of the graphene wireless sensor

integrated ontothe surface of an IV bag. Scale bar is 1 cm. (b) Bandwidth of the sensor resonance

corresponding to various concentrations of S. aureus cells incubated on the sensor surface and

dried. (c) Percentage change in graphene resistance versus concentration of S. aureus. Error bars

show standard deviation (N = 3).

The stability of sensor on the IV bag was tested against mechanical stress associated

with bag crumpling by measuring the change in sheet resistance and transmittance of the

graphene (Fig. 2.12).

Figure 2.12 Structural integrity testing of sensor integrated onto IV bag. Optical images of (a)

IV bag sensor before testing, (b) Immersion of the IV bag sensor in water, (c) Structurally stable

57

sensor after recovery from water. Change in sheet resistance (d) and transmittance (e) of

graphene transducer after undergoing 3 cycles of harsh Q-tip rubbing. Scale bars are 1 cm.

2.3.6 Tooth Platform Monitoring of Breath and Saliva.

To investigate the performance of the sensor when directly integrated with biological tissue, the

sensor was biotransferred onto the surface of a bovine tooth (Fig. 2.13a). Teeth are in constant

contact with breath and saliva, which represent rich biologic media that can be probed for the

presence of disease, infectious agents, or metabolic changes45-47

. Monitoring dynamic

characteristics of respiration, including the presence of biomarkers and volatile organic

compounds (VOCs) in exhaled breath, is of growing interest in non-invasive disease diagnosis46-

48. Significantly, integration of the tooth sensor enabled remote monitoring of breath when

exhaling on the tooth. Figure 2.13b depicts the real-time change in graphene conductance on

exposure to breath. Importantly, the sensor shows rapid response to breath, and the signal was

observed to terminate quickly after the breath pulse.

58

Figure 2.13 Tooth sensor monitoring of breath and saliva. (a) Optical image of the graphene

wireless sensor biotransferred onto the surface of a tooth. Scale bar is 1 cm. (b) Electrical

conductance versus time upon exposure of the sensor to pulses of exhaled breath (red line).

Baseline conductance is shown as blue line (c) Percentage change in graphene resistance versus

time following exposure to ~100 H. pylori cells in human saliva (red line). The response to

“blank” saliva solution is shown as blue line (d) Percentage change in graphene resistance versus

concentration of H. pylori. Error bars show standard deviation (N = 3).

59

Next, we explored the response of the tooth sensors to Helicobacter pylori, a Gram-

negative pathogen found in the stomach and saliva which is estimated to be responsible for the

development of over 90% of duodenal ulcers and stomach cancers49,50

. In particular, we

investigated the ability of the graphene-AMP sensors to selectively recognize H. pylori cells in

human saliva. Tooth sensors were first exposed to a solution of saliva and the signal was allowed

to stabilize. Next, H. pylori cells dissolved in a pooled sample of saliva were allowed to come

into contact with the tooth sensor. Optical experiments with fluorescently labeled bacterial cells

verified that the immobilized AMPs recognize and bind bacterial cells, after incubating in saliva

for ca. 15 min.

Figure 6c depicts the real-time change in graphene resistance on exposure to a 1 µL

sample of human saliva containing ~100 H. pylori cells, while 1 µL of “blank” saliva solution

without any bacterial cells was used as a control. Figure 2.13d thus depicts the percentage

change in resistance as a function of bacterial concentration after 10 minutes by wirelessly

recording the characteristic frequencies at resonance25,51,52

(Methods). A linear relationship was

observed between the logarithm of bacteria concentration and the resistance change up to a

concentration of 106 bacterial cells, with a lower detection limit of ~100 cells. Promisingly, this

latter value is two orders of magnitude less than the minimum infective dose for H. pylori42

. This

demonstrates the natural specificity possessed by AMPs in recognizing and binding to

pathogenic bacterial cells, even in the presence of complex saliva interferrents such as epithelial

cells, proteins, and immunoglobulins.

60

2.3.7 Discussion

This work realizes the direct integration of highly sensitive and selective graphene nanosensors

with natural biomaterials for single cell detection, and for passive, wireless monitoring. The

sensor is realized via the synergistic, hierarchical integration of a variety of smart material

properties. The surface-dominated graphene sensing elements allow for intimate, robust

conformability onto biological tissues or teeth. As a sensing system, the resulting device has

several key meritorious properties, including 1) extremely high sensitivity due to the graphene

network, 2) biotransferability offered by the water-soluble silk fibroin platform, 3) broadly

selective biorecogniton enabled by robust and naturally occurring AMPs, and 4) the ability to

achieve battery-free, wireless remote query operation via the incorporation of a parallel resonant

circuit with a gold thin film patterned meander line inductor53,54

and interdigitated capacitive

electrodes.

Silk thin films serve as an ideal “temporary tattoo” platform due to their optical

transparency, mechanical robustness, biotransferability, flexibility and biocompatibility22,23

.

When crystallized in air, the silk fibroin secondary structure kinetically favors silk I formation, a

disordered collection of -helices and random coils resulting in aqueous solubility. Such films

possess programmable solubility rates dependent on -sheet content and fibroin concentration,

making them ideal substrates for the clean and controlled transfer of graphene to biological and

material surfaces.

Functionalization of graphene nanosensors with bifunctional peptides enables efficient

recognition of pathogenic bacteria. Previous results showed that graphene-binding peptides

display high surface coverage and strong binding activity due to -stacking interactions between

aromatic residues27

. Graphene sheets functionalized with OHP, an AMP isolated from the skin of

61

the frog species Odorrana grahami, enabled broadband detection of Gram-positive and Gram-

negative bacteria. Previous studies showed that bacterial binding of AMPs are observed as a

precursor to bacteriocidal activity32

, and OHP displays broad activity against: H. pylori, a Gram-

negative species found in the stomach and saliva which is responsible for ulcers and stomach

cancers; E. coli, a Gram-negative species found in the lower intestine of endotherms with known

strains capable of acute food poisoning and urinary tract infections resulting from unhygienic

meat and dairy preparation; and S. aureus, a Gram-positive species found on skin flora and

hospital equipment which is notoriously drug-resistant. OHP is also known to exert antimicrobial

activity against methicillin resistant strains of S. aureus (MRSA)41

.

A single layer thin film inductor-capacitor (LC) resonant circuit integrated in parallel

with the resistive graphene monolayer enables wireless readout and battery-free operation. The

change in conductance of the graphene nanosensor on binding of pathogenic bacteria is resolved

from changes in the characteristic frequencies and bandwidth of sensor resonance. Both the

characteristic frequencies and the bandwidth are quantities that are dominatingly determined by

the resonant circuit and readout can thus be insensitive to the mutual inductance (coupling

coefficient) between the sensor and reader coil. Therefore, the relative alignment and location of

the sensor with respect to the reader antenna is unimportant and flexible operation is achieved.

However, we note that improvements in sensor performance will require better control over

potential non-uniformities in analyte coverage, and a reduction of artifacts such as air bubbles in

the case of immersion in liquid.

62

2.4 Materials & Methods

2.4.1 Reagents and Biologicals

The bifunctional peptide GBP-OHP (HSSYWYAFNNKT-GGG

GLLRASSVWGRKYYVDLAGCAKA) containing a graphene binding motif linked to the

antimicrobial peptide odorranin-HP via a triglycine linker were chemically synthesized and

obtained from Peptide 2.0 Inc (Chantilly, VA). Heat-killed pathogenic Gram-negative bacterial

cells of E. coli O157:H7 and H. pylori were purchased from KPL (Gaithersburg, MD). Heat-

killed Gram-positive bacterial cells of S. aureus were purchased from Invitrogen Inc. The human

saliva sample was purchased from Lee Biosolutions (St Louis, MO). The stock solution of

peptide was prepared by reconstitution of the lyophilized powder in DI water. Different

concentrations of bacterial samples were prepared from stock solutions via dilution in deionized

water or human saliva. Phosphate buffered saline consisting of 137 mM NaCl, 2.7 mM KCl, 4.4

mM Na2HPO4 and 1.4 mM KH2PO4 (pH 7.4), FeCl3, sodium carbonate and lithium bromide for

the processing of silk were purchased from Sigma Aldrich (St. Louis, MO).

2.4.2 Preparation of Silk Films.

Bombyx mori cocoons were boiled in 0.02 M Na2CO3 for 30 minutes followed by thorough

rinsing with water. The degummed silk was then dissolved in 9.3 M aqueous lithium bromide

and the solution was dialyzed to remove excess salt. The resulting aqueous solutions were 6-10%

(w/v) fibroin and were preserved by storage at 5 °C. Silk films were made by casting fibroin

solutions onto PDMS and drying in air for 12-24 h, depending on the thickness. When

crystallized in air, silk fibroin secondary structure kinetically favors silk I formation and ca. 50

63

µm thick films. For the preparation of fluorescent silk films for silk dissolution test, silk fibroin

solution was mixed with fluorescence dye and allowed to crystallize overnight.

2.4.3 Fabrication of Graphene/Silk Sensors

CVD grown graphene monolayers from Ni thin films were released and transferred onto

PDMS stamps by removal of the Ni layer in FeCl3. Graphene was then transfer printed onto the

silk films. Clean transfer of graphene onto silk was achieved via moistening of the silk surface

using a wet cotton swab. Planar inductive and capacitive elements were then incorporated onto

the graphene/silk samples to enable wireless interrogation. A meander line design for the

inductive element was deposited on the graphene nanosensor via shadow mask assisted electron

beam evaporation of gold (150-200 nm).

2.4.4 Biotransfer onto Biomaterials

Integration of the sensor onto biomaterial surfaces was achieved via dissolution of the

supporting silk substrate. In the case of dry surfaces such as a tooth, a moistened cotton swab

was used to slightly wet the surface. The graphene-Au electrode sensing elements on the

temporary silk films were then carefully aligned and placed on the tooth surface with the device

side facing down. The temporary silk film platform was gradually dissolved off by application of

water, leaving the ultra-thin sensors intimately attached to the surface by van der Waals forces.

In the case of wet surfaces such as muscle tissue, dissolution of the silk film was faster. IV bags

(Baxter ViaFlex) used for the study were provided by the Princeton University Medical Center.

2.4.5 Graphene Functionalization with AMPs

64

The bifunctional peptide GBP-OHP was dissolved in DI water at a concentration of 1 mg/mL. 5

µL of the peptide solution was dropped onto the graphene and incubated for 15 min, followed by

washing with deionized water and drying.

2.4.6. Single Bacterium Detection Measurements

Electrical measurements for the detection of a single bacterium were performed with a lock-in

detection system using Stanford Research Systems 810 DSP lock-in amplifier. A signal of 50

mV was used with a modulation frequency of 30-70 Hz with zero DC bias to avoid

electrochemical reactions. The resistance of the graphene sensor was monitored continuously in

the presence of analyte samples of various dilutions of bacterial cells. Bacterial cells for optical

monitoring and for antimicrobial peptide-bacteria binding density studies were fluorescently

labeled with propidium iodide in a manner similar to previous studies30

. Stock solutions of

propidium iodide (PI), nucleic acid stain (Molecular Probes, Eugene, OR) were made from solid

form by dissolving PI (MW = 668.4) in deionized water at 1 mg/mL (1.5 mM) and stored at 4 ºC,

protected from light. Heat-killed bacterial cells rehydrated in PBS were then incubated with 3

µM solution of PI (made by diluting the 1 mg/mL stock solution 1:500 in buffer) for 10-15 min.

After incubation, the cells were pelleted by centrifugation and removal of the supernatant, and

were resuspended in DI water or 1×PBS buffer. The binding pattern of the different bacterial

cells was imaged using a Zeiss Axiovert inverted microscope and recorded with a Zeiss

AxioCam digital camera. For real-time detection of bacterial cells (E. coli O157: H7), a 1 µL

sample containing 100 bacterial cells was pipetted onto the graphene sensors. Simultaneous

bright field and fluorescent data were recorded together with lock-in resistance data, with the

focal plane set on the graphene surface to identify the events when the bacterial cells came close

65

to the sensor. The motion of the bacterial cells was tracked with the help of video spot tracker, a

custom automated tracking software (freely available at http://cismm.org/downloads/), and the

manual tracking plugin of the National Institutes of Health’s ImageJ software.

2.4.7 Wireless sensing experiments

A single-layer inductive-capacitive (LC) resonant circuit, integrated in parallel with the resistive

(R) graphene monolayer, formed the basis of the wireless readout unit. The reader device

consisted of a two-turn coil antenna connected to a frequency response analyzer (HP 4191A RF

impedance analyzer). The wireless reader, which was powered by an alternating current source,

was responsible for wirelessly transmitting power and receiving sensor data from the remote

passive sensor, all via inductive coupling (Fig. 2.14).

Figure 2.14 Impedance spectrum of the reader coil. The impedance spectrum of the reader coil

antenna in the presence and absence of the sensing element.

66

The equivalent circuit of the graphene nanosensor reader device is illustrated in Figure

2.17. The sensing element is modeled as an LRC circuit where L is the inductance of the meander

line inductor, C is the capacitance of the graphene/interdigitated electrode capacitive system and

R is the resistance of the graphene transducing element. The reader system for the wireless

measurement consists of a coil antenna connected to a radio frequency impedance analyzer

(Hewlett-Packard 4191A). The reader antenna is used to inductively couple and power the

remote sensing element. The reader coil is excited by an AC voltage signal generated by the

regulated voltage source in the impedance analyzer, and the corresponding current response is

measured as the frequency is varied. The AC sinusoidal signal on the reader coil will result in a

magnetic field in its vicinity, which is calculated based on Faraday’s law55

:

√

where H is the magnetic field intensity, I is the current through the reader coil, r is the radius of

the reader coil (circular coil), N is the number of turns of the coil, and x is the separation between

the reader and the sensor inductors along the central axis. The impedance analyzer is used to

continuously monitor the complex impedance spectrum of the reader-sensor system. In the

absence of the sensing element the impedance spectrum consists of the impedance of the reader

coil antenna alone (Figure 2.14). In the presence of the sensing element, the additional

impedance of the sensor resonant circuit is reflected on the measured complex impedance. The

expression for the complex impedance of the inductively coupled reader-sensor system can be

derived using linear circuit theory and is described elsewhere56

. The bandwidth of resonance is

measured between the half-power (-3dB) points (Figure 2.15). The characteristic frequencies of

67

sensor resonance (resonance frequency and zero reactance frequency) are monitored from the

complex impedance spectrum (Figure 2.16).

Passing an AC signal through the antenna generated a magnetic field, inducing current

via mutual inductance in the coil of the sensing element (Faraday’s law), and finally resulting in

a potential drop that depended on the conductance of the graphene nanosensor. Any change in

the conductivity of the sensor system – resulting from biological or chemical changes occurring

at the transducer surface – was reflected as a change in the frequency characteristics (namely

bandwidth) around the resonance point25,51,57

. This allowed the reader to wirelessly interrogate

the sensing element by measuring the complex impedance of the system. The change in the

capacitance (C), of the graphene-interdigitated electrode sensing system was deduced from the

resonance frequency shift (f), the expression for which is

(1)

√

The bandwidth of the sensor’s resonant-network (B) was measured from the magnitude of the

sensor impedance and used to calculate the change in resistance (R) of the graphene-electrode

sensing element on binding of the bacterial cells (Figure 2.15). The expression for the change in

resistance of the system is

(2) ⁄

68

Figure 2.15 Impedance spectrum of the sensing element. The impedance magnitude of the

sensing element as a function of frequency, illustrating measurement of the resonance bandwidth

from the half-power points.

Frequency measurements on the sensor system were performed by monitoring the complex

impedance spectrum of the system (Figure 2.16).

69

Figure 2.16 Complex impedance spectrum of the sensing element. The complex impedance of

the sensing element as a function of frequency, showing impedance magnitude, real part and

imaginary part. The measurement of the characteristic frequencies of resonance is illustrated.

The frequency at which the imaginary part of the sensor impedance vanishes (fz) was continually

monitored and used to calculate the graphene resistance:

(3) (

)

⁄

where, L is the inductance of the system and can be measured separately. The equivalent circuit

of the sensing element was used to calculate the bandwidth of the reader-sensor system, and the

input impedance of the system was viewed by the reader device (Figure 2.17).

70

Figure 2.17 Electrical equivalent circuit of the graphene based wireless sensing element-reader

system. The graphene based wireless sensing element is modeled as an LRC resonant circuit.

The reader system consists of a coil antenna connected to a radio frequency (RF) impedance

analyzer58

.

2.5 Conclusion

In conclusion, direct integration of highly sensitive graphene nanosensors with biomaterials such

as tooth enamel has enabled battery-free sensors for remote monitoring of pathogenic bacteria.

This work thus represents a fundamentally new paradigm in biochemical detection, and may

provide an in situ, first order monitoring and detection system for applications including point-

of-care diagnostics, hospital sanitation monitoring, and food safety analysis. Yet, these results

are only a prototype, ‘first generation’ platform for interfaced graphene nanosensors. Due to the

semi-selective nature of the interaction of AMPs with pathogenic bacteria, differentiation of

multiple species of pathogenic bacteria has not been achieved. Future work will involve

exploring strategies to improve this selectivity via investigations into multi-ligand59,60

and

aptamer based capture agents26,61

; and antibody based biorecognition molecules with improved

71

stability62

to provide stringent discrimination between species of pathogenic bacteria. Alternative

strategies for covalent and non-covalent functionalization of graphene sensors will also be

explored63

. Finally, future challenges in the sensor development will involve further

miniaturization of the wireless coil for integration onto a smaller footprint (such as a human

tooth)64,65

and testing of the platform on in vivo systems, including tissue and teeth in living

animals and humans.

2.6 References

1 Service, R. F. Can sensors make a home in the body? Science 297, 962-963 (2002).

2 Yao, H., Shum, A. J., Cowan, M., Lähdesmäki, I. & Parviz, B. A. A contact lens with

embedded sensor for monitoring tear glucose level. Biosensors and Bioelectronics 26,

3290-3296 (2011).

3 Grayson, A. C. R. et al. A BioMEMS review: MEMS technology for physiologically

integrated devices. Proceedings of the IEEE 92, 6-21 (2004).

4 Neat, M. L., Peacock, R. & Brittain, R. T. Implantation of electrodes in the dentine of an

upper canine tooth in the dog. British Journal of Pharmacology 43, 476P-477P (1971).

5 Li, C. M., Dong, H., Cao, X., Luong, J. H. T. & Zhang, X. Implantable electrochemical

sensors for biomedical and clinical applications: Progress, problems, and future

possibilities. Current Medicinal Chemistry 14, 937-951 (2007).

6 Schurr, M. O., Schostek, S., Ho, C. N., Rieber, F. & Menciassi, A. Microtechnologies in

medicine: An overview. Minimally Invasive Therapy and Allied Technologies 16, 76-86

(2007).

72

7 Zheng, G., Patolsky, F., Cui, Y., Wang, W. U. & Lieber, C. M. Multiplexed electrical

detection of cancer markers with nanowire sensor arrays. Nature Biotechnology 23, 1294-

1301 (2005).

8 Kuang, Z., Kim, S. N., Crookes-Goodson, W. J., Farmer, B. L. & Naik, R. R. Biomimetic

chemosensor: designing peptide recognition elements for surface functionalization of

carbon nanotube field effect transistors. ACS Nano 4, 452-458 (2010).

9 Schedin, F. et al. Detection of individual gas molecules adsorbed on graphene. Nature

Materials 6, 652-655 (2007).

10 Loh, K. J., Lynch, J. P. & Kotov, N. A. Passive wireless sensing using SWNT-based

multifunctional thin film patches. International Journal of Applied Electromagnetics and

Mechanics 28, 87-94 (2008).

11 Liao, L. et al. High-speed graphene transistors with a self-aligned nanowire gate. Nature

467, 305-308 (2010).

12 Chen, C. et al. Performance of monolayer graphene nanomechanical resonators with

electrical readout. Nature Nanotechnology 4, 861-867 (2009).

13 Liu, Y., Yu, D., Zeng, C., Miao, Z. & Dai, L. Biocompatible graphene oxide-based

glucose biosensors. Langmuir 26, 6158-6160 (2010).

14 Staii, C., Johnson Jr, A. T., Chen, M. & Gelperin, A. DNA-decorated carbon nanotubes

for chemical sensing. Nano Letters 5, 1774-1778 (2005).

15 Choi, W., Lahiri, I., Seelaboyina, R. & Kang, Y. S. Synthesis of graphene and its

applications: A review. Critical Reviews in Solid State and Materials Sciences 35, 52-71

(2010).

73

16 Yang, W. et al. Carbon nanomaterials in biosensors: Should you use nanotubes or

graphene. Angewandte Chemie - International Edition 49, 2114-2138 (2010).

17 Dong, X., Shi, Y., Huang, W., Chen, P. & Li, L. J. Electrical detection of DNA

hybridization with single-base specificity using transistors based on CVD-grown

graphene sheets. Advanced Materials 22, 1649-1653 (2010).

18 Mohanty, N. & Berry, V. Graphene-based single-bacterium resolution biodevice and

DNA transistor: Interfacing graphene derivatives with nanoscale and microscale

biocomponents. Nano Letters 8, 4469-4476 (2008).

19 Lee, C., Wei, X., Kysar, J. W. & Hone, J. Measurement of the elastic properties and

intrinsic strength of monolayer graphene. Science 321, 385-388 (2008).

20 Koenig, S. P., Boddeti, N. G., Dunn, M. L. & Bunch, J. S. Ultrastrong adhesion of

graphene membranes. Nature Nanotechnology 6, 543-546 (2011).

21 Kim, D. H. et al. Dissolvable films of silk fibroin for ultrathin conformal bio-integrated

electronics. Nature Materials 9, 511-517 (2010).

22 Vepari, C. & Kaplan, D. L. Silk as a biomaterial. Progress in Polymer Science (Oxford)

32, 991-1007 (2007).

23 Kim, D. H. et al. Epidermal electronics. Science 333, 838-843 (2011).

24 Kim, D. H. et al. Silicon electronics on silk as a path to bioresorbable, implantable

devices. Applied Physics Letters 95 (2009).

25 Ong, K. G., Wang, J., Singh, R. S., Bachas, L. G. & Grimes, C. A. Monitoring of bacteria

growth using a wireless, remote query resonant-circuit sensor: Application to

environmental sensing. Biosensors and Bioelectronics 16, 305-312 (2001).

74

26 Zelada-Guillen, G. A., Riu, J., Duzgun, A. & Rius, F. X. Immediate detection of living

bacteria at ultralow concentrations using a carbon nanotube based potentiometrie

aptasensor. Angewandte Chemie - International Edition 48, 7334-7337 (2009).

27 Cui, Y. et al. Chemical functionalization of graphene enabled by phage displayed

peptides. Nano Letters 10, 4559-4565 (2010).

28 Pender, M. J., Sowards, L. A., Hartgerink, J. D., Stone, M. O. & Naik, R. R. Peptide-

mediated formation of single-wall carbon nanotube composites. Nano Letters 6, 40-44

(2006).

29 Zelzer, M. & Ulijn, R. V. Next-generation peptide nanomaterials: molecular networks,

interfaces and supramolecular functionality. Chemical Society Reviews 39, 3351-3357

(2010).

30 Mannoor, M. S., Zhang, S., Link, A. J. & McAlpine, M. C. Electrical detection of

pathogenic bacteria via immobilized antimicrobial peptides. Proceedings of the National

Academy of Sciences of the United States of America 107, 19207-19212 (2010).

31 Kulagina, N. V., Shaffer, K. M., Anderson, G. P., Ligler, F. S. & Taitt, C. R.

Antimicrobial peptide-based array for Escherichia coli and Salmonella screening.

Analytica Chimica Acta 575, 9-15 (2006).

32 Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature 415, 389-395

(2002).

33 Schaad, U. B. Which number of infecting bacteria is of clinical relevance? Infection 11

Suppl 2, S87-89 (1983).

34 Belgrader, P. et al. PCR detection of bacteria in seven minutes. Science 284, 449-450

(1999).

75

35 Li, Y., Cu, Y. T. H. & Luo, D. Multiplexed detection of pathogen DNA with DNA-based

fluorescence nanobarcodes. Nature Biotechnology 23, 885-889 (2005).

36 Ferrari, A. C. et al. Raman spectrum of graphene and graphene layers. Physical Review

Letters 97 (2006).

37 Timlin, J. A., Carden, A., Morris, M. D., Rajachar, R. M. & Kohn, D. H. Raman

spectroscopic imaging markers for fatigue-related microdamage in bovine bone.

Analytical Chemistry 72, 2229-2236 (2000).

38 Monti, P., Taddei, P., Freddi, G., Asakura, T. & Tsukada, M. Raman spectroscopic

characterization of Bombyx mori silk fibroin: Raman spectrum of Silk I. Journal of

Raman Spectroscopy 32, 103-107 (2001).

39 Kim, K. S. et al. Large-scale pattern growth of graphene films for stretchable transparent

electrodes. Nature 457, 706-710 (2009).

40 Kim, S. N. et al. Preferential Binding of Peptides to Graphene Edges and Planes. Journal

of the American Chemical Society 133, 14480-14483 (2011).

41 Chen, L. et al. An antimicrobial peptide with antimicrobial activity against Helicobacter

pylori. Peptides 28, 1527-1531 (2007).

42 Solnick, J. V., Hansen, L. M., Canfield, D. R. & Parsonnet, J. Determination of the

infectious dose of Helicobacter pylori during primary and secondary infection in rhesus

monkeys (Macaca mulatta). Infect Immun 69, 6887-6892 (2001).

43 Beard-Pegler, M. A., Stubbs, E. & Vickery, A. M. Observations on the resistance to

drying of staphylococcal strains. Journal of Medical Microbiology 26, 251-255 (1988).

44 Beard-Pegler, M. A. & Vickery, A. M. Lysogenicity of methicillin-resistant strains of

Staphylococcus aureus. Journal of Medical Microbiology 20, 147-155 (1985).

76

45 Potyrailo, R. A. & Morris, W. G. Multianalyte chemical identification and quantitation

using a single radio frequency identification sensor. Analytical Chemistry 79, 45-51

(2007).

46 Strand, N., Bhushan, A., Schivo, M., Kenyon, N. J. & Davis, C. E. Chemically

Polymerized Polypyrrole for On-Chip Concentration of Volatile Breath Metabolites. Sens

Actuators B Chem 143, 516-523.

47 Phillips, M. Method for the collection and assay of volatile organic compounds in breath.

Anal Biochem 247, 272-278 (1997).

48 Kuzmych, O., Allen, B. L. & Star, A. Carbon nanotube sensors for exhaled breath

components. Nanotechnology 18 (2007).

49 Dixon, M. F. Patterns of inflammation linked to ulcer disease. Bailliere's Best Practice

and Research in Clinical Gastroenterology 14, 27-40 (2000).

50 Megraud, F. Transmission of Helicobacter pylori: Faecal-oral versus oral-oral route.

Alimentary Pharmacology and Therapeutics, Supplement 9, 85-91 (1995).

51 Ong, K. G., Grimes, C. A., Robbins, C. L. & Singh, R. S. Design and application of a

wireless, passive, resonant-circuit environmental monitoring sensor. Sensors and

Actuators, A: Physical 93, 33-43 (2001).

52 Chen, P. J., Rodger, D. C., Saati, S., Humayun, M. S. & Tai, Y. C. Microfabricated

implantable parylene-based wireless passive intraocular pressure sensors. Journal of

Microelectromechanical Systems 17, 1342-1351 (2008).

53 Acuna, J. E., Rodriguez, J. L. & Obelleiro, F. Design of meander line inductors on

printed circuit boards. International Journal of RF and Microwave Computer-Aided

Engineering 11, 219-230 (2001).

77

54 Stojanovic, G., Radovanovic, M., Malesev, M. & Radonjanin, V. Monitoring of water

content in building materials using a wireless passive sensor. Sensors 10, 4270-4280

(2010).

55 Finkenzeller, K. RFID Hand Book, Fundamentals and Applications in Contactless Smart

cards, Radio Frequency Identification and Near-Field Communication. (John Wiley &

Sons, 2010).

56 Lorrain, P. & Corson, D. Electromagnetic Fields and Waves. (W.H. Freeman and

Company, 1970).

57 Loh, K. J., Lynch, J. P. & Kotov, N. A. Inductively coupled nanocomposite wireless

strain and pH sensors. Smart Structures and Systems 4, 531-548 (2008).

58 Chen, P. J., Saati, S., Varma, R., Humayun, M. S. & Tai, Y. C. Wireless intraocular

pressure sensing using microfabricated minimally invasive flexible-coiled LC sensor

implant. Journal of Microelectromechanical Systems 19, 721-734 (2010).

59 Agnew, H. D. et al. Iterative in situ click chemistry creates antibody-like protein-capture

agents. Angewandte Chemie - International Edition 48, 4944-4948 (2009).

60 Vallée-Bélisle, A., Ricci, F. & Plaxco, K. W. Engineering biosensors with extended,

narrowed, or arbitrarily edited dynamic range. Journal of the American Chemical Society

(2011).

61 Feng, L., Chen, Y., Ren, J. & Qu, X. A graphene functionalized electrochemical

aptasensor for selective label-free detection of cancer cells. Biomaterials 32, 2930-2937

(2011).

78

62 Chennamsetty, N., Voynov, V., Kayser, V., Helk, B. & Trout, B. L. Design of therapeutic

proteins with enhanced stability. Proceedings of the National Academy of Sciences of the

United States of America 106, 11937-11942 (2009).

63 Mann, J. A., Rodríguez-López, J., Abruña, H. D. & Dichtel, W. R. Multivalent binding

motifs for the noncovalent functionalization of graphene. Journal of the American

Chemical Society 133, 17614-17617 (2011).

64 Adams, J. J. et al. Conformal Printing of Electrically Small Antennas on Three-

Dimensional Surfaces. Advanced Materials 23, 1335-1340 (2011).

65 Tao, H. et al. Metamaterials on paper as a sensing platform. Adv Mater 23, 3197-3201

(2011).

79

Chapter 3

Antimicrobial Peptides as Molecular Probes

on Bionic Sensors§

3.1 Overview

The development of a robust and portable biosensor for the detection of pathogenic bacteria

could impact areas ranging from water quality monitoring to testing of pharmaceutical products

for bacterial contamination. Of particular interest are detectors which combine the natural

specificity of biological recognition with sensitive, label-free sensors providing electronic read-

out. Evolution has tailored antimicrobial peptides to exhibit broad-spectrum activity against

pathogenic bacteria, while retaining a high degree of robustness. Here, we report selective and

sensitive detection of infectious agents via electronic detection based on antimicrobial peptide-

functionalized microcapacitive electrode arrays. The semi-selective antimicrobial peptide

Magainin I – which occurs naturally on the skin of African clawed frogs – was immobilized on

gold microelectrodes via a C-terminal cysteine residue. Significantly, exposing the sensor to

various concentrations of pathogenic E. coli revealed detection limits of approximately 1

bacterium/µL, a clinically useful detection range. The peptide-microcapacitive hybrid device was

further able to demonstrate both Gram-selective detection as well as inter-bacterial strain

§ The work reported in this chapter is based on the following original publication: Mannoor, M. S.; Zhang, S.; Link,

A. J.; McAlpine, M. C., Electrical detection of pathogenic bacteria via immobilized antimicrobial peptides.

Proceedings of the National Academy of Sciences of the United States of America, 107 (45), 19207-19212 (2010).

80

differentiation, while maintaining recognition capabilities toward pathogenic strains of E. coli

and Salmonella. Lastly, we report a simulated “water-sampling” chip, consisting of a

microfluidic flow cell integrated onto the hybrid sensor, which demonstrates real-time on-chip

monitoring of the interaction of E. coli cells with the antimicrobial peptides. The combination of

robust, evolutionarily tailored peptides with electronic readout monitoring electrodes may open

exciting avenues in both fundamental studies of the interactions of bacteria with antimicrobial

peptides, as well as the practical use of these devices as portable pathogen detectors.

3.2 Introduction

Bacterial infections remain the leading cause of death in developing nations, accounting for an

estimated 40% of deaths 1. For instance, the strain O157:H7 of E. coli is considered to be one of

the most dangerous food borne pathogens 2,3

. In developed countries, bacterial contamination is

also of critical concern, particularly in the pharmaceutical industry. Indeed, the most reliable test

for contamination is the limulus amebocyte lysate (LAL) test, based on the detection of

endotoxins via coagulation of horseshoe crab blood 4. Microbial infections and drug-resistant

supergerms are also a leading cause of military deaths, particularly in soldiers with burn injuries,

and are considered potential biowarfare agents5-7

. While containment strategies – such as

vaccination and “broadband” antibiotic usage in hospitals – have helped reduce the severity of

bacterial infections, these strategies have also inadvertently promoted the emergence of antibiotic

resistance. Thus, the development of a sensor that can detect the presence of an infectious

outbreak from a broad spectrum of pathogenic species would be highly desirable.

Current methods for detecting pathogenic bacteria include enzyme-linked immunoassay

(ELISA), and polymerase chain reaction (PCR) 8,9

. In the former case, the assays exploit

81

antibodies as molecular recognition elements due to their highly specific targeting of antigenic

sites. However, antibodies lack the stability needed to detect pathogenic species under harsh

environments, reducing the shelf-life of antibody functionalized sensors. The high specificity of

antibody-antigen interactions also requires a one-to-one pairing of antibody-based sensors for

each target to be detected. Nucleic acid probe-based techniques such as PCR can reach single-

cell detection limits, yet require the extraction of nucleic acids and are limited in portability.

By contrast, the ease of synthesis and intrinsic stability of antimicrobial peptides (AMPs)

render them particularly interesting candidates for use as molecular recognition elements in

electronic biosensing platforms10,11

. AMPs appear in multiple niches in nature including the skin

of higher organisms and the extracellular milieu of bacteria as the primary line of defense against

bacteria and fungi12

. AMPs are much more stable than typical globular proteins – explaining how

they can be continually exposed to the natural environment – and are exceptionally efficient at

fending off bacterial infection13

. Indeed, some cationic antimicrobial peptides have shown

activity towards pathogenic bacteria under harsh environmental conditions such as thermal

(boiling/autoclaving) and chemical denaturants14,15

. The replacement of current antibody based

affinity probes with more stable and durable AMPs in biological sensors may thus help to

increase the shelf-life of current diagnostic platforms. Lastly, a major potential advantage of

AMPs as recognition elements stems from their semi-selective binding nature to target cells,

affording each peptide the ability to bind a variety of pathogenic cells.

The bioactivity of AMPs towards microbial cells is classified into groups according to

their secondary structures12

. Many AMPs adopt amphipathic conformations which spatially

cluster hydrophobic from cationic amino acids, thereby targeting the negatively charged head

groups of lipids in the bacterial membrane. In contrast, the membranes of plants and animals

82

seclude negative charges to the inner leaflet, and contain cholesterols which reduce AMP activity

11. By aiming at the very foundation of the bacterial cell membrane, and remaining generically

unrecognizable to proteases 16

, AMPs as antibiotics have remained remarkably free of acquired

resistance. Among AMPs, linear cationic peptides such as magainins are particularly attractive

for microbial sensing applications because of their small molecular size and intrinsic stability

17,18. In particular, the positively charged AMP Magainin I

(GIGKFLHSAGKFGKAFVGEIMKS) binds most selectively to the bacterial cell E. coli

O157:H7 as a precursor to bactericidal activity 19

. Magainin I also displays broad spectrum

activity toward other Gram-negative bacteria, which comprise the majority of pathogenic

infection in humans.

A number of methods have been successful at detecting bacteria including

nanomechanical cantilever sensing (NEMS) 20,21

, surface-enhanced Raman spectroscopy (SERS)

22, and quartz crystal microbalance based sensors

23. Similarly, recent attempts have utilized

AMPs as biorecognition elements in fluorescent-based microbial detection with achievable

detection limits of 5 × 104

cells/mL 24,25

. Yet, the development of an “all-in-one” solution which

combines a high degree of portability, robustness, sensitivity, and selectivity toward pathogenic

strains remains challenging. Among the various label-free signal transduction platforms that have

been investigated, impedance spectroscopy is promising due to its simple instrumentation, ease

of device assembly, and adaptability to multiplexed lab-on-a-chip applications 26,27

. A

microcapacitive sensor detects impedance changes in the dielectric properties of an electrode

surface upon analyte binding, where the variation in the impedance is directly proportional to the

activity of analyte binding 28

. Here, we report for the first time a label-free electronic biosensor

based on the hybridization of the antimicrobial peptide Magainin I with interdigitated

83

microelectrode arrays for the sensitive and selective detection of pathogenic bacteria via

impedance spectroscopy. We anticipate that the combination of compact, naturally bioselective

AMPs with microcapacitive sensors may represent a highly robust and portable platform for

fundamental studies of AMP-bacteria interactions, and for portable infectious disease threat

agent signaling.

3.3 Antimicrobial Peptide based Sensitive Detection of Bacteria

As a first step toward the development of an AMP-based label-free, electronic biosensor, the

targeting of microbial cells by Magainin I was investigated using impedance spectroscopy.

Figure 3.1 schematically outlines our sensing platform.

Figure 3.1 AMP-based electrical detection of bacteria. a) Schematic of AMPs immobilized on

an interdigitated microelectrode array, b) Magnified image of the AMP Magainin I in helical

form, modified with a terminal cysteine residue, and with clearly defined hydrophobic and

hydrophilic faces, c) Detection of bacteria is achieved via binding of target cells to the

84

immobilized AMPs, d) Optical image of the interdigitated microelectrode array (Scale bar: 50

µm).

AMPs are first immobilized on microfabricated interdigitated gold electrodes (Fig. 3.1a;

Materials and Methods). Magainin I was acquired with an additional cysteine residue at the C-

terminus (Fig. 3.1b), allowing for facile and site specific covalent attachment to the gold

electrodes. Next, heat-killed bacterial cells were injected and incubated with the AMP-modified

electrodes. If the bacteria are recognized by the AMPs, binding will occur (Fig. 3.1c), leading to

dielectric property changes which can be monitored by a spectrum analyzer. Sensitivity of

microbial detection is a key determinant for utility of sensors. To this end, the sensitivity of the

magainin-functionalized microelectrode array in detecting bacterial cells was first investigated

using impedance spectroscopy. Figure 3.2 shows the results of measurements performed after

incubation of the immobilized AMPs with pathogenic E. coli O157:H7 cells in concentrations

ranging from 103

to 107 CFU/mL.

85

Figure 3.2 Sensitivity of the AMP electronic biosensor. a) Impedance spectra of various

concentrations of E. coli O157:H7 cells (red), of a non-labeled sensor (blue), and of a sensor

with an N-terminal immobilized AMP (purple), b) Impedance spectra of various concentrations

of E. coli with the AMP sensor at 10 Hz. Error bars show standard deviation (N = 3).

86

A “blank” device with no immobilized AMPs was also tested for comparison; the

impedance of the “blank” device without immobilized AMPs is found to change negligibly upon

exposure to various bacterial concentrations (Fig. 3.3).

Figure 3.3 Impedance spectra of various concentrations of E. coli O157:H7 cells after exposure

to a non-labeled “blank” sensor. The inset shows the optical micrograph of the bare sensor after

exposure to E. coli cells of concentration 107 CFU/mL.

Figure 3.2a shows that at low frequencies, the different concentrations of bacterial cells

have the effect of increasing the impedance in proportion to the number of cells present in the

sample for concentrations greater than 102

CFU/mL. As the frequency increases, the contribution

to the impedance from the bacterial cells decreases, leaving only the dielectric relaxation of

small dipoles including water molecules in the buffer solution to affect the measured impedance.

Figure 3.2b thus depicts the impedance change at a fixed frequency of 10 Hz. The variation in

the impedance is directly proportional to the number of bacterial cells bound to the immobilized

AMPs, and manifested in a logarithmic increase with respect to serially diluted bacterial

concentrations. Significantly, the detection limit of response of the hybrid AMP-microelectrode

87

device to E. coli was found to be 103 CFU/mL (1 bacteria/µL), which compares favorably to the

LAL test29

. This lowest limit of detection appears to be limited by the presence of impedance

due to the electrical double layer resulting from the electrode polarization effect at low

frequencies. Importantly, this sensitivity limit is clinically relevant 30

and compares favorably to

AMP based fluorescent assays [5 × 104 CFU/mL

25] and to antibody-based impedance sensors

26.

To gain further insight into the activity of Magainin I towards E. coli, AMPs were

immobilized “upside-down” via incorporation of a cysteine residue at the N-terminus. The

binding affinities of Magainin I immobilized via cysteine residues at the C-terminus and N-

terminus were compared and co-plotted in Figs. 3.2a and 3.2b. Considerably reduced binding

activity was observed for magainin immobilized via the N-terminus compared to C-terminal

immobilization. This reduction in the binding affinity is likely due to the diminished exposure of

the target bacteria to the amine-containing residues near the N-terminus. This observation

supports the hypothesis that the initial interaction of α-helical AMPs with the membranes of the

target bacteria occurs via electrostatic attraction of positively charged amino acids on the AMP

with negatively charged phospholipids in the bacterial membrane 19,31,32

. Indeed, it has been

previously shown that amino acid omissions in the N-terminal region of magainin result in the

complete loss of antimicrobial activity, whereas analogs with omissions in the C-terminal region

exhibited equal or increased activity 33

.

3.4 Effect of AMP Immobilization Density

Finally, the effect of varying the surface density of the immobilized AMPs on the detection of

bacterial cells was investigated (Fig. 3.4). The response of the biosensor towards target cells was

found to increase monotonically with increasing concentration of immobilized magainin. The

88

effect of varying the surface density of the immobilized AMPs on the detection of bacterial cells

was investigated. Different concentrations of C-terminal cysteine labeled Magainin I were

immobilized on the electrode surface. The impedance response resulting from binding of

pathogenic E. coli O157:H7 cells (107 CFU/mL) to different densities of immobilized AMPs

were recorded. The response of the sensor at 10 Hz is plotted in Figure S3. The immobilized

peptide film was also analyzed via fluorescent microscopy by labeling the peptides with

fluorescein isothiocyanate (FITC) 34

. The ability to capture the target bacteria was found to be

strongly dependent on the immobilization density of the magainin on the sensor surface. This

supports the hypothesis that the initial interaction between the cationic AMPs and the target

species occurs through electrostatic interaction 19,31

. This also suggests that the minimization of

diffusion and steric hindrance which usually affect the binding kinetics do not play a significant

role in the immobilized AMP-bacteria interactions.

Figure 3.4 The effect of the surface density of immobilized Magainin I on the binding of

bacterial cells. Scale bar is 20 µm. Error bars show standard deviation (N = 3).

89

3.5 Selectivity Measurements

As a next step, we investigated the selectivity of the AMP-functionalized biosensors toward

various bacterial species. Specifically, the binding behavior of AMPs was probed toward: 1)

gram-negative pathogenic E. coli O157:H7, 2) the non-pathogenic E. coli strain ATCC 35218, 3)

gram-negative pathogenic Salmonella typhimurium, and 4) Listeria monocytogenes, a gram-

positive pathogen. Collectively, these studies elucidate the matrix of selectivity as it depends on

gram-negative vs. gram-positive species, and pathogenic vs. non-pathogenic strains. The

selectivity was first investigated using fluorescent microscopy methods, by staining bacterial

cells and optically mapping their binding density to gold films hybridized with AMPs. Figure 3.5

shows the discriminative binding pattern of immobilized Magainin I to various bacterial cells (all

107 CFU/mL) stained with propidium iodide (PI) nucleic acid stain (see Methods), as well as the

surface density of the bound bacterial cells.

90

Figure 3.5 Optical microscopy of the selectivity of AMPs. (Left panels) Demonstration of

selective binding of the immobilized AMP to various stained bacterial cells (107 CFU/mL),

including a) E. coli O157:H7, b) Salmonella typhimurium, c) E. coli ATCC 35218, and d)

91

Listeria monocytogenes. (Right panels) The corresponding surface densities of bound cells. Scale

bars are 10 µm.

Likewise, Figure 3.6a plots the electrical response of the AMP-biosensor against these

various species as a function of the interrogating frequency, and Figure 3.6b plots the response at

10 Hz. Intriguingly, inspection of the fluorescent images and surface density plots agree

qualitatively with the response of the AMP electrical biosensor and reveal the following insights.

First, Magainin I exhibits clear preferential binding toward the pathogenic, gram-negative

species E. coli and Salmonella, relative to the gram-positive species Listeria, with a two order of

magnitude impedance difference at 10 Hz (Fig. 3.6b)35,36

. This selectivity was particularly

enhanced for pathogenic E. coli, which showed a slightly larger response relative to Salmonella.

Next, inter-bacteria strain differentiation between pathogenic and non-pathogenic bacteria is

demonstrated by the ability of the sensor to selectively detect pathogenic E. coli relative to the

non-pathogenic strain, again with a nearly two order of magnitude impedance difference at 10

Hz. Finally, the response of the sensor to all microbial species was larger than the response of the

“blank” biosensor which was not functionalized with AMP.

92

Figure 3.6 Impedance spectroscopy of the selectivity of AMPs. a) Impedance spectra of the

AMP functionalized microelectrode array after interaction with various bacterial samples (107

CFU/mL). b) Impedance changes associated with various bacterial species at 10 Hz. Error bars

show standard deviation (N = 3).

93

The response of the sensor to a mixture of pathogenic E. coli and Listeria with a total

cellular concentration of 107

CFU/mL similarly revealed dominant E. coli binding (Fig. 3.7).

Figure 3.7. Impedance spectra of the sensor after exposure to a mixture of pathogenic E. coli and

Listeria of total cellular concentration 107 CFU/mL, in comparison to the sensor response to E.

coli cells of concentration 107

CFU/mL, 106 CFU/mL and Listeria cells of concentration 10

7

CFU/mL.

94

Interestingly, this preferential binding is mitigated in a highly basic medium (Fig.3.8).

Figure 3.8. Impact of varying pH (at pH -3, 7.4, 9.5) on the impedance response of the sensor at

10 Hz, after exposure to pathogenic (O157∶H7) and nonpathogenic (ATCC35218) E. coli cells of

concentration 107cfu/mL.

The observed specificity differences can be explained by noting that a balance between

electrostatic and hydrophobic interactions is believed to underlie the mechanism of bacterial cell

binding by AMPs 31,37

. In the case of magainin I, the difference in the membrane structures of

gram-negative vs. gram-positive bacteria may account for the differential selectivity 38

. Gram-

negative bacteria possess an outer membrane with negatively charged lipopolysaccharide (LPS)

– the first site of encounter for AMPs – and a thin peptidoglycan layer. In contrast, Gram-

positive bacteria lack the LPS-containing outer membrane, instead possessing a thick

95

peptidoglycan layer and teichoic acids. Further, although both pathogenic and non-pathogenic E.

coli cell walls contain LPS, the LPS of the pathogenic strain includes O-antigens, which are

hydrophilic branched sugar side chains. These O-antigens form the outermost portion of the

polysaccharide chain and are thought to enhance electrostatic and hydrogen bonding 39-41

. This

ability of Magainin I to selectively prefer Gram-negative species, and pathogenic versus non-

pathogenic strains of E. coli, agrees with several other bacteria adhesion studies 19,42-44

. The

effect of pH and ionic strengths on the binding behavior of cationic AMPs is well-known 44,45

.

Under conditions for non-physiological pHs the AMPs were found to lose their prefenrential

binding ability to pathogenic E. coli cells versus non-pathogenic E.coli, when the medium

became highly basic (see supporting information). However, we do not expect this behavior to

influence the performance of the sensor as most of the water quality monitoring experiments are

performed under neutral conditions or under conditions of constant ionic strengths.

3.6 Real-Time Detection

To simulate the use of the AMP microelectrodes in everyday applications, such as direct water

sampling, the biosensor response was investigated in real time, as shown in Figure 3.9.

96

Figure 3.9 Real-time binding of bacteria to AMP biosensors. a) Digital photograph of the

microfluidic flow cell. b) Optical micrograph of the microfluidic channel with an embedded

interdigitated microelectrode array chip. c) Optical image of the embedded microelectrode array

after exposure to 107 CFU/mL bacterial cells for 30 min. d) Real time monitoring of the

interaction of the AMP functionalized sensor (and an unlabeled control chip) with various

concentrations of E. coli cells.

First, a microfluidic cell was bonded to the interdigitated biosensor chip (Fig. 3.9a), such

that the electrodes were perpendicular to the direction of the sample flow (Fig. 3.9b) 46

. Next,

fluid was injected using a syringe pump connected to the inlet port, and allowed to flow through

97

to the outlet port, at a flow rate of 100 µL/min. The flow cell was first flushed with buffer to

establish a baseline. Various dilutions (104 – 10

7 CFU/mL) of pathogenic E. coli cells in PBS

were then injected to the channel at a reduced flow rate of 5 µL/min for 30 min. For example,

Figure 3.9c shows the microelectrode array after exposure to 107 CFU/mL bacterial cells.

Simultaneously, the impedance response was continuously monitored during the sample flow-

through process (Fig. 3.9d). All samples produced a measureable response relative to the control

sample within 5 minutes, with the highest concentration sample yielding a response within 30

seconds; these responses saturated after ca. 20 min. These results bode well for the

implementation of this sensor in continuous monitoring of flowing water supplies. Yet, it should

be noted that for the same concentration of bacterial cells, the response of the sensor under flow-

through conditions was found to be comparatively lower than the response after static incubation.

We attribute this to the opposing effects of shear and mixing on the binding kinetics, as reduced

binding of AMPs to target cells under flow-through conditions has also been reported in

fluorescent based assays 24

.

3.7 Materials and Methods

3.7.1 Antimicrobial Peptides and Bacterial Cells

Antimicrobial peptide Magainin I (GIGKFLHSAGKFGKAFVGEIMKS), chemically

synthesized to contain a C-terminal cysteine residue via standard N-fluorenylmethoxycarbonyl

(FMOC) solid phase peptide synthesis, was obtained from Anaspec (San Jose, CA). Magainin I

was also synthesized with an N-terminal Cysteine to compare the bacterial binding activity.

Heat-killed pathogenic bacterial cells of E. coli O157:H7, Salmonella typhimurium and Listeria

monocytogenes were purchased from KPL (Gaithersburg, MD). Heat-killed cells of a non-

98

pathogenic strain of E-Coli (ATCC 35218) was obtained from American Type Cell Culture

(ATCC, Manassas, VA) for control experiments. The stock solution of AMP was prepared by the

reconstitution of the lyophilized product in phosphate buffered saline (Sigma-Aldrich, St. Louis,

MO) consisting of 137 mM NaCl, 2.7 mM KCl, 4.4 mM Na2HPO4 and JM 1.4 mM KH2PO4 (pH

7.4) 29,44

. The heat-killed bacterial cells were rehydrated in PBS, according to manufacturer

recommendations.

3.7.2 Interdigitated Microelectrode Array (IMA) and Microfluidic Flow Cell

Interdigitated capacitive electrodes were microfabricated on 4” p-type silicon wafers (boron-

doped, <100>, 10-16 Ω-cm, 550 µm thick). A 1 µm thick silicon dioxide layer was deposited on

the wafer by plasma enhanced chemical vapor deposition (PECVD) to form electrical insulation

between the Si substrate and the capacitive electrodes. S1813 photoresist was patterned using

photolithography, followed by electron-beam evaporation of 10 nm Ti and 300 nm Au. The IMA

was then finally developed by lift-off patterning of the metallic layer in acetone with ultrasonic

activation. The electrode array consisted of 50 pairs of interdigitated capacitive electrodes with

an electrode width and separation of 5 µm. A polydimethylsiloxane (PDMS) microfluidic flow

cell consisting of a detection microchamber with an embedded microelectrode array, inlet and

outlet ports was formed by bonding the IMA chip to the PDMS channel. The PDMS micro-

channel formed on the master mold was partially cured, aligned with the microelectrode array

and bonded by permanently curing at 80 ºC for 2-3 hr. Microfluidic connectors were fixed on to

the inlet and outlet ports through drilled holes.

99

3.7.3 Sensor Surface Functionalization with Magainin

A simple technique for the immobilization of peptides to a gold surface is through the utilization

of native thiol groups present in cysteine residues 47-50

, and cysteine residues can be synthetically

introduced at a specific site of the peptide to form a properly oriented recognition layer 49,51-53

.

Previous studies have revealed that the covalent immobilization of AMPs on gold surfaces via C-

terminal cysteine leads to adsorption at an angle to the surface 43,54

. Prior to the immobilization

procedure, the gold IMA electrodes were cleaned using acetone, isopropanol and DI water. Stock

solutions of the AMPs were prepared in phosphate-buffered saline (PBS), pH 7.4, consisting of

137 mM NaCl, 2.7 mM KCl, 4.4 mM Na2HPO4 and 1.4 mM KH2PO4 29,44

. For the

immobilization of the AMPs, 800 µg/ml (unless otherwise mentioned) of Magainin I in PBS

buffer was injected into the sensing chamber and incubated for 60 min under static conditions.

The functionalized electrodes were then rigorously washed with 1×PBS to remove any unbound

AMPs, rinsed with de-ionized water and dried in liquid nitrogen. Gold surfaces covalently

functionalized with magainins have shown antimicrobial binding activity persisting for at least

six months 54

.

3.7.4 Fluorescent Microscopy

Stock solutions of propidium iodide (PI), nucleic acid stain (Molecular probes, Eugene, OR) was

made from solid form by dissolving PI (MW = 668.4) in deionized water at 1 mg/mL (1.5 mM)

and stored at 4ºC, protected from light. Heat-killed bacterial cells rehydrated in PBS were then

incubated with 3 µM solution of propidium iodide, PI (made by diluting the 1 mg/mL stock

solution 1:500 in buffer) for 10-15 min 55

. After incubation, the cells were pelleted by

centrifugation and removal of the supernatant and were re-suspended in fresh 1×PBS buffer. The

100

samples of stained bacterial cells (E. coli O157: H7, Salmonella, non-pathogenic E. coli and

Listeria, all 107CFU/mL) were then allowed to incubate with the immobilized Magainin for 15-

20 min in the dark. After incubation, the Au surfaces were washed with PBS buffer and dried

under liquid nitrogen. The binding pattern of the different bacterial cells was imaged using a

Zeiss axiovert inverted microscope and recorded with a Zeiss axiocam digital camera. Surface

density of the bound bacterial cells was analyzed and plotted using the ImageJ software package.

3.8 Impedance Spectroscopy Measurement Details

Dielectric property changes due to AMP-bacteria interactions were probed using a Fast-Fourier

Transform (FFT) spectrum analyzer. The dielectric properties were investigated over a frequency

range of 10 Hz to 100 kHz, with 0 V DC bias and 50 mV AC signals using a SRS 785, 2-channel

dynamic signal analyzer. An in-house LabView program routine was used to collect and record

the data through a GPIB interface. An external op-amp amplifier circuit was used to minimize

the noise and a MATLAB program was used to plot the impedance spectra from the analyzer

output (Fig. 3.9). For sensitivity measurements, pathogenic gram-negative E. coli O157:H7

bacterial cells were injected into the microfluidic flow channel at various dilutions, and

incubated with the immobilized magainins for 12-15 min, under static conditions. To ensure the

response of the sensor toward bound bacterial cells, the impedance spectrum was taken after the

removal of unbound cells by thorough washing in PBS. For real-time measurements, the

impedance vs. time data was recorded while buffer solutions or different dilutions of bacterial

solutions flowed through the microfluidic channel. The flow cell was first flushed with 1x PBS

buffer at a flow rate of 100 µL/min to establish a baseline. Bacterial detection measurements

were performed with the sample flowing at a rate of 5 µL/min. The sensor device was

101

regenerated via a cleaning solution containing 1 M NaCl, 100 mM HCl, and 200 mM CHAPS

followed by 1× PBS buffer. The electrodes were then thoroughly flushed with DI water to

remove any salts. The effect of bacterial cells binding to immobilized magainins on the

impedance signal is due to the dielectric property of the cell membrane. All experiments were

repeated three times.

3.8.1 Measurement setup

Figure 3.9a shows a schematic of the measurement setup used. The AC voltage applied to the

electrodes produces both conduction and displacement current through the sample. The real and

imaginary parts of the transfer function are proportional to the conductivity and the

dielectric constant, respectively. The output of the signal analyzer is applied to one of the

capacitive electrodes through R1. The other electrode of the capacitive sensor is connected to the

negative input of the amplifier A2, which holds the electrode at ground potential. As a result, the

current I that flows through the sensor produces a voltage V2 which is equivalent to the product

of I and the sample impedance Z. The value of voltage drop V1 is equal to the product of I and

R2. Thus the transfer function of the system is given by (1),

(1)

where Z is the overall impedance of the sensing system. The purpose of R1 is to provide an upper

limit for the current I as the impedance Z becomes smaller at higher frequencies. The unity gain

amplifier A1 provides buffering so that the input impedance of Channel 2 does not affect the

voltage drop across the sample.

1

2

V

V

21

2

R

Z

V

V

102

3.8.2 Equivalent Circuit

A high density interdigitated microelectrode array was used for the detection of bacterial cells.

Exposure of the magainin functionalized sensor to bacterial cells results in the binding of the

cells on the electrode surface. Binding of bacterial cells causes a change in the impedance

measured across the electrodes. Figure 3.9b shows an equivalent circuit of the microelectrode

solution interface before the binding bacterial cells. CDL represents the capacitance due to the

electrical double layer between the electrode and the buffer solution, CDi represents the dielectric

capacitance, and RBuffer the bulk resistance of the buffer solution 56

. A parasitic capacitance from

the oxide layer between silicon and gold is shown as CPAR. Figure 3.9c shows a simplified circuit

diagram of the system after bacterial binding to the AMP functionalized electrodes. The

modification in the interface impedance due to the bacterial impedance consists mainly of the

capacitance of the cell membrane CCM, the resistance of the membrane RCM, and the resistance of

the cytoplasm RCyt, as shown. The represented model has two parallel branches, a dielectric

capacitance branch and an impedance branch. At high frequencies, the total impedance of the

system Z will be dominated by the dielectric capacitance of the medium, and the contributions

from the electrical double layer capacitance and the bulk medium resistance will be minimized.

At lower frequencies (< 1 MHz), current does not flow through the dielectric capacitance branch,

and the bacterial cells add different impedance components in series with the impedance branch.

103

104

Figure 3.10 a) Schematic of the impedance spectroscopy measurement setup (adapted from

reference 57). b), c) Simplified equivalent circuit of the microelectrode array/electrolyte interface

b) before bacterial binding, and c) after bacterial binding57

.

3.9 Conclusion

In summary, coupling of AMPs with microcapacitive biosensors has resulted in the

implementation of a portable, label-free sensing platform for the detection of infectious agents.

The achievable sensitivity approached 1 bacterium/µL – a clinically relevant limit – and the

AMPs allowed for sufficient selectivity to distinguish pathogenic and gram-negative bacteria,

while retaining broadband detection capabilities. The sensor surface when exposed to a

simulated real life sample, exhibited the ability to selectively bind pathogenic E. coli cells from a

mixture of Gram negative and Gram positive (Listeria) bacterial cells. Finally, real-time sensing

results demonstrated the capability of the relatively simple impedance-based transduction

architecture to directly detect bacteria, suggesting a promising alternative to traditional antibody

based immunoassays. We anticipate these results could provide a significant positive impact on

the use of pathogenic sensors to test and monitor bacteria in reservoir water, or for use as

biological threat agent detection systems. Yet, a number of key challenges remain. First, the

detection of bacteria in real water samples has not yet been studied. Second, although the

selectivity of the magainin functionalized sensors has been demonstrated under specified

conditions, the scenario could be more complex when the concentrations of the target species are

unknown or when there are multiple infectious agents present. Finally, based on previous work

by our group in coupling peptides to silicon nanowire sensors 58,59

, significantly enhanced

sensitivity may be achievable by reducing the sensors down to the nanometer scale.

105

3.10 References

1 Ivnitski, D., Abdel-Hamid, I., Atanasov, P. & Wilkins, E. Biosensors for detection of

pathogenic bacteria. Biosens. Bioelectron. 14, 599-624 (1999).

2 Buchanan, R. L. & Doyle, M. P. Foodborne disease significance of Escherichia coli

O157:H7 and other enterohemorrhagic E. coli. J. Food Technol. 51, 69-76 (1997).

3 Jay, J. M. in Modern Food Microbiology (ed J. M. Jay) (Van Nostrand Reinhold, NY,

1992).

4 Walls, E. A., Berkson, J. & Smith, S. A. The Horseshoe Crab, Limulus polyphemus: 200

Million Years of Existence, 100 Years of Study. Rev. Fish. Sci. 10, 39-73 (2002).

5 Compton, J. A. F. Military chemical and biological agents: Chemical and toxicological

properties. (Telford press, 1987).

6 Malcolm, D. in Biological Warfare in the 21st Century 258 (Brassey's, UK, 1994).

7 D'Avignon, L. C. et al. Contribution of bacterial and viral infections to attributable

mortality in patients with severe burns: An autopsy series. Burns. (January 13, 2010).

8 Daly, P., Collier, T. & Doyle, S. PCR-ELISA detection of Escherichia coli in milk. Lett.

Appl. Microbiol. 34, 222-226 (2002).

9 Johnson, R. P. et al. Detection of Escherichia coli O157:H7 in meat by an enzyme-linked

immunosorbent assay, EHEC-tek. Appl. Environ. Microbiol. 61, 386-388 (1995).

10 Nicolas, P. & Mor, A. Peptides as weapons against microorganisms in the chemical

defense system of vertebrates. Annu. Rev. Microbiol. 49, 277-304 (1995).

11 Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature 415, 389-395

(2002).

106

12 Boman, H. G. Peptide antibiotics and their role in innate immunity. Annu. Rev. Biochem.

13, 61-92 (1995).

13 Tamerler, C. & Sarikaya, M. Genetically designed peptide-based molecular materials.

ACS Nano 3, 1606-1615 (2009).

14 Friedrich, C., Scott, M. G., Karunaratne, N., Yan, H. & Hancock, R. E. W. Salt-resistant

alpha-helical cationic antimicrobial peptides. Antimicrob. Agents Chemother. 43, 1542-

1548 (1999).

15 Rydlo, T., Rotem, S. & Mor, A. Antibacterial properties of dermaseptin S4 derivatives

under extreme incubation conditions. Antimicrob. Agents Chemother. 50, 490-497

(2006).

16 Meng, H. & Kumar, K. Antimicrobial Activity and Protease Stability of Peptides

Containing Fluorinated Amino Acids. J. Am. Chem. Soc. 129, 15615-15622 (2007).

17 Zasloff, M., Martin, B. & Chen, H. C. Antimicrobial activity of synthetic magainin

peptides and several analogues. Proc. Natl. Acad. Sci. U.S.A. 85, 910-913 (1988).

18 Zasloff, M. Magainins, a class of antimicrobial peptides from Xenopus skin: Isolation,

characterization of two active forms, and partial cDNA sequence of a precursor. Proc.

Natl. Acad. Sci. U.S.A. 84, 5449-5453 (1987).

19 Matsuzaki, K., Sugishita, K. I., Harada, M., Fujii, N. & Miyajima, K. Interactions of an

antimicrobial peptide, magainin 2, with outer and inner membranes of Gram-negative

bacteria. BBA-Biomembranes 1327, 119-130 (1997).

20 Ndieyira, J. W. et al. Nanomechanical detection of antibiotic-mucopeptide binding in a

model for superbug drug resistance. Nat. Nanotechnol. 3, 691-696 (2008).

107

21 Burg, T. P. et al. Weighing of biomolecules, single cells and single nanoparticles in fluid.

Nature 446, 1066-1069 (2007).

22 Premasiri, W. R. et al. Characterization of the Surface Enhanced Raman Scattering

(SERS) of bacteria. J. Phys. Chem. B 109, 312-320 (2005).

23 Bao, L., Deng, L., Nie, L., Yao, S. & Wei, W. Determination of microorganisms with a

quartz crystal microbalance sensor. Anal. Chem. Acta. 319, 97-101 (1996).

24 Kulagina, N. V., Shaffer, K. M., Anderson, G. P., Ligler, F. S. & Taitt, C. R.

Antimicrobial peptide-based array for Escherichia coli and Salmonella screening. Anal.

Chem. Acta. 575, 9-15 (2006).

25 Kulagina, N. V., Lassman, M. E., Ligler, F. S. & Taitt, C. R. Antimicrobial peptides for

detection of bacteria in biosensor assays. Anal. Chem. 77, 6504-6508 (2005).

26 Boehm, D. A., Gottlieb, P. A. & Hua, S. Z. On-chip microfluidic biosensor for bacterial

detection and identification. Sens. Actuators, B 126, 508-514 (2007).

27 Gibson, D. M., Coombs, P. & Pimbley, D. W. Automated conductance method for the

detection of Salmonella in foods: Collaborative study. J. Assoc. Off. Anal. Chem. 75, 293-

302 (1992).

28 Berggren, C., Bjarnason, B. & Johansson, G. Capacitive biosensors. Electroanalysis 13,

173-180 (2001).

29 Jarvis, W. R. & Highsmith, A. K. Bacterial growth and endotoxin production in lipid

emulsion. J. Clin. Microbiol. 19, 17-20 (1984).

30 Li, Y., Karlin, A., Loike, J. D. & Silverstein, S. C. A critical concentration of neutrophils

is required for effective bacterial killing in suspension. Proc. Natl. Acad. Sci. U.S.A. 99,

8289-8294 (2002).

108

31 Matsuzaki, K., Murase, O. & Miyajima, K. Kinetics of pore formation by an

antimicrobial peptide, magainin 2, in phospholipid bilayers. Biochemistry 34, 12553-

12559 (1995).

32 Wenk, M. R. & Seelig, J. Magainin 2 amide interaction with lipid membranes:

Calorimetric detection of peptide binding and pore formation. Biochemistry 37, 3909-

3916 (1998).

33 Cuervo, J. H., Rodriguez, B. & Houghten, R. A. The Magainins: sequence factors

relevant to increased antimicrobial activity and decreased hemolytic activity. Pept Res.

1, 81-86 (1988).

34 Jullian, M. et al. N-terminus FITC labeling of peptides on solid support: the truth behind

the spacer. Tetrahedron Lett. 50, 260-263 (2009).

35 Lopez-Solanilla, E., Gonzalez-Zorn, B., Novella, S., Vazquez-Boland, J. A. &

Rodriguez-Palenzuela, P. Susceptibility of Listeria monocytogenes to antimicrobial

peptides. FEMS Microbiol. Lett. 226, 101-105 (2003).

36 Lo̕pez-Solanilla, E., Gonza̕lez-Zorn, B., Novella, S., Va̕zquez-Boland, J. A. &

Rodri̕guez-Palenzuela, P. Susceptibility of Listeria monocytogenes to antimicrobial

peptides. FEMS Microbiol. Lett. 226, 101-105 (2003).

37 Lad, M. D. et al. Antimicrobial peptide-lipid binding interactions and binding selectivity.

Biophys. J. 92, 3575-3586 (2007).

38 Wieprecht, T. et al. Peptide hydrophobicity controls the activity and selectivity of

magainin 2 amide in interaction with membranes. Biochemistry 36, 6124-6132 (1997).

109

39 Jucker, B. A., Harms, H., Hug, S. J. & Zehnder, A. J. B. Adsorption of bacterial surface

polysaccharides on mineral oxides is mediated by hydrogen bonds. Colloids Surf., B 9,

331-343 (1997).

40 Lugtenberg, B. & Van Alphen, L. Molecular architecture and functioning of the outer

membrane of Escherichia coli and other gram-negative bacteria. Biochim Biophys Acta

737, 51-115 (1983).

41 Makin, S. A. & Beveridge, T. J. The influence of A-band and B-band lipopolysaccharide

on the surface characteristics and adhesion of Pseudomonas aeruginosa to surfaces.

Microbiology 142, 299-307 (1996).

42 Gregory, K. & Mello, C. M. Immobilization of Escherichia coli cells by use of the

antimicrobial peptide cecropin P1. Appl. Environ. Microbiol. 71, 1130-1134 (2005).

43 Strauss, J., Kadilak, A., Cronin, C., Mello, C. M. & Camesano, T. A. Binding,

inactivation, and adhesion forces between antimicrobial peptide cecropin P1 and

pathogenic E. coli. Colloids Surf., B 75, 156-164 (2010).

44 Lee, I. H., Cho, Y. & Lehrer, R. I. Effects of pH and salinity on the antimicrobial

properties of clavanins. Infect. Immun. 65, 2898-2903 (1997).

45 Soares, J. W., Kirby, R., Morin, K. M. & Mello, C. M. Antimicrobial peptide preferential

binding of E. coli O157:H7. Protein Peptide Lett 15, 1086-1093 (2008).

46 Kim, D. R. & Zheng, X. Numerical Characterization and Optimization of the

Microfluidics for Nanowire Biosensors. Nano Letters 8, 3233-3237 (2008).

47 Lee, W., Oh, B. K., Lee, W. H. & Choi, J. W. Immobilization of antibody fragment for

immunosensor application based on surface plasmon resonance. Colloids Surf., B 40,

143-148 (2005).

110

48 Shen, Z. et al. Single-chain fragment variable antibody piezoimmunosensors. Anal.

Chem. 77, 797-805 (2005).

49 Ihs, A. & Liedberg, B. Chemisorption of l-cysteine and 3-mercaptopropionic acid on gold

and copper surfaces: An infrared reflection-absorption study. J. Colloid Interface Sci.

144, 282-292 (1991).

50 Dickerson, M. B., Sandhage, K. H. & Naik, R. R. Protein- and peptide-directed syntheses

of inorganic materials. Chem. Rev. 108, 4935-4978 (2008).

51 Baas, T., Gamble, L., Hauch, K. D., Castner, D. G. & Sasaki, T. Characterization of a

cysteine-containing peptide tether immobilized onto a gold surface. Langmuir 18, 4898-

4902 (2002).

52 Kallwass, H. K. W., Parris, W., Macfarlane, E. L. A., Gold, M. & Jones, J. B. Site-

specific immobilization of an L-lactate dehydrogenase via an engineered surface cysteine

residue. Biotechnol. Lett 15, 29-34 (1993).

53 Dubois, L. H. & Nuzzo, R. G. Synthesis, structure, and properties of model organic

surfaces. Annu. Rev. Phys. Chem. 43, 437-463 (1992).

54 Humblot, V. et al. The antibacterial activity of Magainin I immobilized onto mixed thiols

Self-Assembled Monolayers. Biomaterials 30, 3503-3512 (2009).

55 Jepras, R. I., Carter, J., Pearson, S. C., Paul, F. E. & Wilkinson, M. J. Development of a

robust flow cytometric assay for determining numbers of viable bacteria. Appl. Environ.

Microbiol. 61, 2696-2701 (1995).

56 Bard, A. J. & Faulkner, L. R. Electrochemical Methods: Fundamentals and Applications.

2 edn, (Wiley, New York, NY, USA., 1980).

111

57 Prodan, C., Mayo, F., Claycomb, J. R., Miller, J. H. & Benedik, M. J. Low-frequency,

low-field dielectric spectroscopy of living cell suspensions. J. Appl. Phys. 95, 3754-3756

(2004).

58 McAlpine, M. C., Ahmad, H., Wang, D. & Heath, J. R. Highly ordered nanowire arrays

on plastic substrates for ultrasensitive flexible chemical sensors. Nat. Mater. 6, 379-384

(2007).

59 McAlpine, M. C. et al. Peptide-nanowire hybrid materials for selective sensing of small

molecules. J. Am. Chem. Soc. 130, 9583-9589 (2008).

112

Chapter 4

3D Printed Bionic Ears§

4.1 Overview

The ability to three-dimensionally interweave biological tissue with functional electronics could

enable the creation of bionic organs possessing enhanced functionalities over their human

counterparts. However, current electronics are inherently two dimensional, preventing seamless

multidimensional integration with biology. Here, we present a novel strategy for overcoming

these difficulties via additive manufacturing of biological cells with electronic and structural

elements. As a proof of concept, we generated a bionic ear via 3D printing of a cell-seeded

hydrogel matrix in the precise anatomic geometry of a human ear, along with an intertwined

conducting polymer consisting of infused silver nanoparticles. This allowed for the in vitro

culturing of cartilage tissue around an inductive coil antenna in the ear, which subsequently

connects to cochlear-like electrodes. The printed ear exhibits enhanced auditory sensing for radio

frequency reception, and complementary left and right ears can listen to stereo audio music.

4.2 Introduction

The design and implementation of bionic organs and devices that enhance human capabilities,

known as cybernetics, has been an area of increasing scientific interest.1-3

This field has the

§ The work reported in this chapter is based on the following original publication: Mannoor M. S., Z. Jiang, T.

James, Y. L. Kong, K. A. Malatesta, W. O. Soboyejo, N. Verma, D. H. Gracias, M. C. McAlpine, 3D Printed Bionic

Ears. Nano Letters 13, 2634-2639 (2013).

113

potential to generate customized replacement parts for the human body, or even create organs

containing capabilities beyond what human biology ordinarily provides. In particular, the

development of approaches for the direct multidimensional integration of functional electronic

components with biological tissue and organs could have tremendous impact in regenerative

medicine, prosthetics, and human-machine interfaces.4,5

Recently, several reports have described

the coupling of electronics and tissues using flexible and/or stretchable planar devices and

sensors that conform to tissue surfaces, enabling applications such as biochemical sensing and

probing of electrical activities on surfaces of the heart,6 lungs,

7 brain,

8 skin

9 and teeth.

10

However, attaining seamless three dimensionally entwined electronic components with

biological tissues and organs is significantly more challenging.4

Tissue engineering is guided by the principle that a variety of cell types can be coaxed

into synthesizing new tissue if they are seeded onto an appropriate three-dimensional hydrogel

scaffold within an accordant growth environment.11-16

Such cell-hydrogel constructs form tissue

structures having the morphology of the original polymer template following in vivo or in vitro

culture.17

However, a major challenge in traditional tissue engineering approaches is the

generation of cell-seeded implants with structures that mimic native tissue, both in anatomic

geometries and intra-tissue cellular distributions.18

Techniques involving seeding cells into pre-

molded scaffolds have been used to demonstrate the fabrication of three dimensional tissues with

complex geometries. Yet, such techniques suffer from issues such as seeding depth limitations

and non-uniform seeding and hence do not offer the ability to easily create parts (organs or

tissue) with required spatial heterogeneities to meet the shortage of donor organs for

transplantation.19,20

For instance, total external ear reconstruction with autogenous cartilage –

114

with the goal of re-creating an ear that is similar in appearance to the contralateral auricle –

remains one of the most difficult problems in the field of plastic and reconstructive surgery.21

Additive manufacturing techniques such as 3D printing offer a potential solution via the

ability to rapidly create computer-aided design (CAD) models by slicing them into layers and

building the layers upwards using biological cells as inks, in the precise anatomic geometries of

human organs.22-24

Variations of 3D printing have been used as methods of solid freeform

fabrication, although its use has mainly been limited to the creation of passive mechanical

parts.22,25

Extrusion-based 3D printing has been used to engineer hard tissue scaffolds such as

knee menisci and intervertebral discs complete with encapsulated cells.26

Further, this technique

offers the ability to create spatially heterogeneous multi-material structures by utilizing

deposition tools that can extrude a wide range of materials.27

This could allow for the

simultaneous printing of electronic materials and biological cells to yield three dimensionally

integrated cyborg tissues and organs exhibiting unique capabilities.

4.3 Our Approach

Here we introduce a conceptually new approach that addresses the aforementioned challenges by

fully interweaving functional electronic components with biological tissue via 3D printing of

electronic materials and viable cell-seeded hydrogels in the precise anatomic geometries of

human organs. Since electronic circuitry is at the core of sensory and information processing

devices,28

in vitro culturing of the printed hybrid architecture enables the growth of “cyborg

organs” exhibiting enhanced functionalities over human biology. Our approach offers the ability

to define and create spatially heterogeneous multimaterial constructs by extruding a wide range

of materials in a layer-by-layer process until the final stereolithographic geometry is complete.

This concept of 3D printing of living cells together with electronic components and growing

115

them into functional organs represents a new direction in merging electronics with biological

systems. Indeed, such cyborg organs are distinct from either engineered tissue or conformal

planar/flexible electronics and offer a unique way of attaining a three dimensional merger of

electronics with tissue.

4.4 3D Printing of Bionic Ear: Steps

As a proof of concept of this approach, we evaluated the ability of 3D printing to create a viable

ear auricle which also contains electronics that broaden the capabilities of human hearing.

Human organs comprising cartilaginous tissue, such as the ear auricle, represent suitable

prototype candidates to investigate the feasibility of our approach. This is due to 1) the inherent

complexity in the ear’s anatomical geometry, which renders it difficult to bioengineer via

traditional tissue engineering approaches, as well as 2) the simplicity in its tissue level structure

due to the lack of vasculature.21,29

Specifically, we demonstrate 3D printing of a chondrocyte

seeded alginate hydrogel matrix with an electrically conductive silver nanoparticle (AgNP)

infused inductive coil antenna, connecting to cochlear-like electrodes supported on silicone.

Taken together, the result is three dimensional integration of functional electronic components

within the complex and precise anatomic geometry of a human ear (Fig. 4.1).

116

Figure 4.1 Three dimensional interweaving of biological tissue and electronics via additive

manufacturing to generate a bionic ear. (a) cad drawing of the bionic ear. (b) (top) various

functional materials, including biological (chondrocyte cells), structural (silicone), and electronic

(agnp-infused silicone) used to form the bionic ear. (bottom) 3d printer used for the printing

process. (c) Illustration of the 3d printed bionic ear.

The following steps are involved in the process. First, a CAD drawing of the bionic ear

(Fig. 4.1a) is used to prescribe the anatomic geometry and the spatial heterogeneity of the

various functional materials. As described above, three materials comprise the three functional

constituents (structural, biological, and electronic) of the bionic ear. These materials are fed into

a syringe extrusion based Fab@Home 3D printer (The NextFab Store, Albuquerque, NM) (Fig.

4.1b). The printed bioelectronic hybrid ear construct is then cultured in vitro to enable cartilage

tissue growth and fusing together to form a “cyborg ear,” with expanded auditory senses of radio

frequency (RF) reception provided by an inductive coil acting as a receiving antenna (Fig. 4.1c).

To demonstrate our approach, we printed the bionic ear construct. For the scaffold, we pre-

seeded an alginate hydrogel matrix with viable chondrocytes at a density of ~60 million cells/mL

(Materials & Methods). Alginate matrix is three dimensionally stable in culture, non-toxic, pre-

seeding and extrusion compatible, and a suitable cell delivery vehicle because crosslinking can

be initiated prior to deposition.30

Chondrocytes used for the printing were isolated from the

articular cartilage of one month old calves (Astarte Biologics, Redmond, WA). A CAD drawing

of a human ear auricle in stereolithography format (STL) with an integrated circular coil antenna

connected to cochlear electrodes was used to define the print paths by slicing the model into

layers of contour and raster fill paths. Crosslinking was initiated in the alginate hydrogel matrix

pre-seeded with viable chondrocytes, which was then 3D printed along with conducting (AgNP-

117

infused) and non-conducting silicone solutions. Together, this method produced the biological,

electronic and structural components of the bionic organ in a single process.

4.5 Growth and Viability of the Bionic Ear

Figure 4.2a shows the 3D printed bionic ear immediately after printing. Notably, it is found to

faithfully reproduce the CAD drawing, in the precise material spatiality for each material as

dictated by the design. The printed ear construct was immersed in chondrocyte culture media

containing 10% or 20% fetal bovine serum (FBS), which was refreshed every 1-2 days

(Materials& Methods). The hybrid ear showed good structural integrity and shape retention

under culture (Fig.4.2b). Over time, the construct gradually became more opaque; this was most

apparent after four weeks of culture, and is grossly consistent with developing an extracellular

matrix (ECM)

118

Figure 4.2 Growth and viability of the bionic ear. (a) Image of the 3D printed bionic ear

immediately after printing. (b) Image of the 3D printed bionic ear under in vitro culturing. Scale

bars in (a) and (b) are 1 cm. (c) Chondrocyte viability at various stages of the printing process.

Error bars show standard deviation with N=3. (d) Variation in the weight of the printed ear over

119

time in culture, where the ear consists of chondrocyte-seeded alginate (red) or only alginate

(blue). Error bars show standard deviation with N=3. (e) Histologic evaluation of chondrocyte

morphology using H&E staining. (f) Safranin O staining of the neocartilaginous tissue after 10

weeks of culture. (g) Photograph (top) and fluorescent (bottom) images showing viability of the

neocartilaginous tissue in contact with the coil antenna. (h) Photograph (top) and fluorescent

(bottom) images of a cross section of the bionic ear showing viability of the internal

cartilaginous tissue in contact with the electrode. Top scale bars are 5 mm; bottom are 50 μm.

4.5.1 Viability of the Printing Process

Viability was tested immediately before and during the various stages of the printing process.

Initial viability of cells was determined after culturing using a Trypan blue cell exclusion assay

(Corning Cellgrow, Mediatech, VA) and was found to be 96.37 ± 1.71% (Fig. 2C) ((Materials&

Methods).). The printed cell-seeded alginate ear was also tested with a LIVE/DEAD® Viability

Assay (Molecular Probes, Eugene, OR) and exhibited a cell viability of 91.26 ± 3.88% with

homogeneous chondrocyte distribution. This result suggests that the printing process, including

cell encapsulation and deposition, does not negatively impact chondrocyte viability.

Notably, this approach of printing a pre-seeded hydrogel matrix eliminates the major

problems associated with seeding depth limitations and non-uniform seeding in traditional

methods for seeding premolded 3D scaffolds. Seeding chondrocytes into a bioabsorbable

alginate matrix and shaping it via 3D printing localizes the cells to a desired geometry, allowing

for new ECM production in defined locations when cultured in nutritive media. As tissue

develops, the polymer scaffold is reabsorbed (Fig. 4.2d), so that new tissue retains the shape of

the polymer in which the cells were seeded. The biodegradable scaffolding provides each cell

with better access to nutrients and more efficient waste removal.

120

The gross morphology of the bionic ear after 10 weeks of in vitro culture is shown in

Figure 4.3.

Figure 4.3 Gross morphology of the 3D printed bionic ear after 10 weeks of in vitro culture.

Scale bar is 1 cm.

4.6 Histologic Characterization

Next, histologic evaluation was used to compare the morphology of chondrocytes in the

neocartilage of the bionic ear to that of the native cartilaginous tissue. Hematoxylin and eosin

(H&E) staining revealed uniform distribution of the chondrocytes in the constructs (Fig. 4.2e)

(Materials & Methods). Histology of the of the ear tissue with Safranin O staining indicated

relatively uniform accumulation of proteoglycans in the cultured ear tissue (Fig. 4.2f). These

biochemical data are consistent with the development of new cartilage.31

Finally, fluorescent

measurements were used to ascertain the viability of the 3D printed bionic ear tissue after 10

weeks of in vitro growth culture. Figures 4.2g and 4.2h show the tissue covering the coil antenna

and the internal tissue that is in contact with the electrode that runs perpendicular through the

121

tissue, respectively. In both cases, the grown cartilage exhibited excellent morphology and tissue

level viability. Notably, this approach of culturing cells which fuse into tissue in the presence of

abiotic electronic materials could minimize the immune response of the grown tissue.

4.7 Biochemical and Biomechanical Characterization

We then characterized the mechanical properties of the cartilage at various stages of growth, as

ECM development correlates strongly with the developing tissue’s mechanical properties.32

First,

extensive biochemical and histologic characterizations were performed ((Materials& Methods).

Samples were removed from cultures containing 10% and 20% FBS at 2, 4, 6, 8 and 10 weeks

and frozen to measure DNA content of the neocartilage and for biochemical evaluation of the

ECM. ECM accumulation in the constructs was evaluated by quantifying the amount of two

important components of ECM: 1) hydroxyproline (HYP) as a marker of collagen content, and 2)

sulfated glycosaminoglycan (GAG) as a marker of proteoglycans. By week 10, HYP content

increased to 1.24 ± 0.10 μg/mg and 1.43 ± 0.15 μg/mg for cultures containing 10% and 20%

FBS, respectively (Fig. 4.4a). The corresponding values of GAG content for week 10 were 10.63

± 0.56 μg/mg and 12.24 ± 0.98 μg/mg (Fig. 4.4b).This increase in GAG and HYP content

indicates that chondrocytes are alive and metabolically active in culture.

122

Figure 4.4 Biomechanical characterization of the 3D printed neocartilage tissue. (a) Variation of

HYP content over time in culture with 20 % (red) and 10 % (blue) FBS. (b) Variation of GAG

content over time in culture with 20 % (red) and 10 % (blue) FBS. (c) Variation of Young’s

modulus of 3D printed dog bone constructs over time in culture with 20 million (blue) and 60

million (red) cells/mL. Error bars for parts a-c show standard deviation with N=3. (d) Various

anatomic sites of the ear auricle, with corresponding hardness listed in Table 1. Scale bar is 1 cm.

4.7.1 Tensile Testing 3D Printed Cartilage Dog bones

123

Next, tensile properties were analyzed by testing 3D printed chondrocyte-alginate dogbone

samples at various points in culture, in which the dogbones contained the same cell densities and

identical culturing conditions as the ear (Materials & Methods). Evaluation of the mechanical

properties indicated that the Young’s modulus of the dogbones increased with time from 14.16

kPa to 111.46 kPa at week 10 (Fig. 4.4c). Dogbones of a lower chondrocyte density of 20 million

cells/mL were also tested under similar conditions to understand the effect of the initial

chondrocyte density in the mechanical properties of the grown tissue. These were found to

possess a lower Young’s modulus of 73.26 kPa at week 10.

4.7.2 Hardness Testing of 3D Printed Neocartillage

Next, the hardness of the grown cartilaginous tissue of the 3D printed auricle was characterized

using nanoindentation measurements. The indentations were performed at the various anatomic

sites of the auricle (Fig. 4.4d). As shown in Table 1, these hardness values were found to be

relatively uniform, ranging from 38.50 kPa to 46.80 kPa.33

4.8 Electrical Characterization

To demonstrate the enhanced functionalities of the 3D printed bionic ear, we performed a series

of electrical characterizations. First, the resistivity of the coil antenna was measured using four

point probe measurements and found to be dependent on the volumetric flow rate used for

printing the conducting AgNP-infused silicone (Materials & Methods). At the optimum flow

rate, the resistivity of the printed coil was found to be 1.31 × 10-6

Ω·m, which is only two orders

of magnitude higher than pure silver (1.59 × 10-8

Ω·m). Next, we performed wireless radio

frequency reception experiments. To demonstrate the ability of the bionic ear to receive signals

beyond normal audible signal frequencies (in humans, 20 Hz to 20 kHz), we formed external

connections to the cochlea of the bionic ear (Fig. 4.5a). The ear was then exposed to sine waves

124

of frequencies ranging from 1 MHz to 5 GHz. The S21 (forward transmission coefficient)

parameter of the coil antenna was analyzed using a network analyzer and was found to transmit

signals across this extended frequency spectrum (Fig. 4.5b).

125

Figure 4.5 Electrical characterization of the bionic ear. (a) Image of the experiment used to

characterize the bionic ear. The ear is exposed to a signal from a transmitting loop antenna. The

output signal is collected via connections to two electrodes on the cochlea. Scale bar is 1 cm. (b)

126

Response of the bionic ear to radio frequencies in terms of S21, the forward power transmission

coefficient. (c) (top) Schematic representation of the radio signal reception of two

complementary (left and right) bionic ears. (bottom) Photograph of complementary bionic ears

listening to stereophonic audio music. (d) Transmitted (top) and received (bottom) audio signals

of the right (R) and left (L) bionic ears.

4.8.1 Bionic Ears: Listening of Stereo Music

Most importantly, as a demonstrative example of the versatility in modifying the final organ by

modifying the CAD design, we printed a complementary left ear by simply reflecting the original

model (Materials & Methods). Left and right channels of stereophonic audio were exposed to the

left and right bionic ear via transmitting magnetic loop antennas with ferrite cores (Fig. 4.5c).

The signals received by the bionic ears were collected from the signal output of the dual cochlear

electrodes and fed into a digital oscilloscope and played back by a loud speaker for auditory and

visual monitoring. Excerpts of the transmitted and received signals of duration 1 ms for both the

right and left bionic ears are shown in Figure 4.5d and are found to exhibit excellent

reproduction of the audio signal. Significantly, the played back music (Beethoven’s “Für Elise”)

from the signal received by the bionic ears possessed good sound quality.

4.9 Materials and Methods

4.9.1 Chondrocyte Culturing

Chondrocytes isolated from the articular cartilage of one month old calves were obtained from

Astarte Biologics (Redmond, WA). The cells were cultured in Dulbecco’s Modified Eagle

Medium (DMEM) with 10% fetal bovine serum for 6 to 8 days. A 1% antibiotic-antimycotic

solution consisting of 10,000 U/mL penicillin G sodium, 10,000 μg/mL streptomycin sulfate, and

127

25 μg/mL amphotericin B in 0.85% saline was also added to prevent contamination. The cells

were cultured at 37 °C and 5% CO2.34

Once the initial seeding density of the cells was reached,

the viability was determined using a Trypan blue (Corning Cellgrow, Mediatech, VA) cell

exclusion assay. The chondrocytes were diluted in acid azo exclusion medium of the dye into a

1:1 solution of the cell suspension in 0.4% Trypan blue dye. The cells were incubated in the

medium for less than 5 minutes. The nonviable cells that stained blue were then counted under a

microscope and found to be 96.58 ± 1.64%. The chondrocytes were then suspended in phosphate

buffered saline and pelleted by centrifugation.

4.9.2 Alginate Formulation and Chondrocyte Seeding

To make the hydrogel matrix, low-viscosity, high G-content non-medical grade alginate protanal

LF10/60 alginate (FMC Biopolymer, Drammen, Norway) was dissolved at a concentration of 30

mg/mL, removed of clumps by passing through a 0.22 μm filter, and mixed with the cell pellet

by gentle stirring. The alginate-cell suspension was vortexed and mixed in a 2:1 ratio with

autoclaved 5 mg/mL CaSO4 in PBS to achieve the desired final cell seeding density (60 × 106

cells/mL for the printed ears and 20 × 106 cells/mL for comparison of mechanical properties).

4.9.3 3D Printing

A CAD file of the bionic ear in STL format was used to define the print paths by slicing the

model into layers of contour paths and raster fill paths. Each of the functional materials used for

the creation of the bionic ear, including cells, conducting polymers (Silicone Solutions,

Twinsburg, OH), and structural polymers (RTV silicone, 3M, St. Paul, MN) were then loaded

into the deposition tool and printed in the spatial heterogeneity determined by the CAD.

128

The efficiency of the 3D printing process in printing the biological cells was assessed by

comparing the viability of the chondrocytes before and after the printing process. The efficiency

of the 3D printing process in printing the electronic material was characterized by comparing the

resistivity of the printed coil geometry at various volumetric flow rates (Fig. 4.6).

Figure 4.6 Resistivity measurements. (a) Image of the four point probe measurement of

conducting traces of AgNP-infused silicone printed at various volumetric flow rates. (b) Print

efficiency at various volumetric flow rates. Error bars show standard deviation with N=3.

4.9.4 Culturing Conditions

To aid the comparison, ‘print efficiency’ was arbitrarily defined as the ratio of the theoretical

resistivity of the material to the resistivity of the 3D printed material. Resistivity was measured

using a 4 point probe apparatus to negate contact resistance. Print efficiencies for various

printing speeds were calculated and compared to identify the optimum printing conditions.

The 3D printed bionic ear was then cultured in the same medium as above containing

10% or 20% fetal bovine serum (FBS) at 37 °C and 5% CO2 (Figs. 4.7 and 4.8).

129

Figure 4.7 Images of the 3D printed ear auricle cultured in 10% FBS at various stages of growth.

(a) As printed, (b) after 5 weeks in culture, and (c) after 10 weeks in culture. Scale bars are 1 cm.

Figure 4.8 Image of neocartilage growth of the 3D printed ear under culture containing 20%

FBS, showing bulbous outgrowth on the surface as indicated by the arrows. Scale bar is 1 cm.

The chondrocyte to feed medium ratio was kept below 1.7 million cells/mL per day to

ensure sufficient nutritional supply to the cells. To demonstrate the versatility of our approach in

modifying the final organ by modifying the CAD design, we printed a complementary left ear by

130

simply reflecting the original model and following the same culturing conditions as before (Fig.

4.9).

Figure 4.9 Images of the 3D printed left bionic ears at various stages of growth. (a) As printed,

(b) after 6 weeks in culture, and (c) after 10 weeks in culture. Scale bars are 1 cm.

The media after incubation was tested for bacterial contamination with 100 μM BacLight

Green stain (Molecular Probes, Eugene, OR). To verify the effect of the culturing on the

electronic properties of the AgNP-infused conducting silicone, the resistance of the coil antenna

and the cochlear-like electrodes were measured at various points in culture using a 4 point probe

measurement apparatus. The resistance was found to be constant over time in culture, because

the silicone covering the outer surface of the conducting traces forms an insulating and water-

proof coating. This was confirmed via SEM micrographs of the 3D printed conducting traces

(Fig. 4.10).

131

Figure 4.10 Electrical resistance of the coil antenna in culture. (a) SEM image of the coil

antenna surface. Scale bar is 100 µm. (b) Resistance per unit length of the coil antenna over time

in culture. Error bars show standard deviation with N=3.

4.9.5 Cellular and Tissue Viability

The effect of the 3D printing process on the viability of the chondrocytes was analyzed by

measuring the viability before and immediately after the printing process (Fig. 4.11). First, the

viability of the chondrocytes after mixing with the alginate hydrogel was tested with a

LIVE/DEAD® Viability Assay (Molecular Probes, Eugene, OR). The samples were stained with

0.15 μM calcein AM and 2 μM ethidium homodimer-1 (EthD-1) for approximately an hour at

room temperature. The viability of the cells in the printed ear construct was also measured by

taking specimens from various locations. The stained samples were analyzed under a fluorescent

microscope (Olympus BX60) with dual band (FITC-Texas red) filter and the viability was

calculated as the average of the ratios of live over total cells in a given field.

132

Figure 4.11 LIVE/DEAD® assay of chondrocytes. (a) Trypan Blue exclusion assay and (b)

calcein AM and ethidium homodimer-1 LIVE/DEAD® assay immediately after 3D printing.

Scale bars are 50 μm.

Tissue level viability of the bionic ear was assessed using fluorescent staining at various

sites in the neocartilage tissue of the ear. Specifically, the chondrocytes were stained with 2

μg/mL fluorescein diacetate (FDA) and 0.1 mg/mL propidium iodide (PI). The viability of the

cartilage tissue that was in contact with the coil antenna was examined at various locations. To

examine the viability of the tissue at the interface of the cartilage tissue and the electrode, a cross

section of the ear was taken and assay was performed on the cells that were in contact with the

electrode.

4.9.6 Biochemical Analyses

Biochemical analysis was performed on the 3D printed ear under various stages of culture to

determine the cell proliferation and ECM characterization. Samples were removed from the ear

under culture, weighed and kept frozen. Samples were then digested in 1 mL of papain digest

133

buffer (0.1 M sodium phosphate, 10 mM sodium EDTA, 10 mM cysteine hydrochloride, and 3.8

U/mL papain, all from Sigma Aldrich, St. Louis, MO) at 65 °C for 24 h.

Chondrocyte proliferation in the bionic ear under culture was determined by measuring

the DNA content of the samples. In short, the DNA content was quantified by measuring the

amount of fluorescence (358/458 nm) after exposing to Hoechst 33258 dye.35

To convert the

obtained fluorescence from the samples to a quantified value in terms of weight, the fluorescence

was compared with a standard curve created with calf thymus DNA (Fig. 4.12).

Figure 4.12 DNA content standard curve obtained from calf thymus DNA.

Hoechst 33258 dye was kept as a stock solution of 1 mg/mL in distilled water and stored

in a foil-wrapped container at 4 °C. A working solution was diluted to 0.1 μg/mL in 10 mM Tris,

1 mM Na EDTA, 0.1 mM NaCl, pH 7.4, immediately before use and was dispensed by minimal

exposure to light. Calf thymus DNA was made to 100 μg/ml in PBS and stored frozen. Next, the

emission and excitation spectra (358/458 nm) were measured for the Hoechst 33258 dye alone

134

and in the presence of calf thymus DNA and papain digested chondrocytes from the samples

(Fig. 4.13).

Figure 4.13 DNA content in the 3D printed ear at various stages during culture with 20% FBS

(red) and 10% FBS (blue). Error bars show standard deviation with N=3.

Analysis of the contents of the extracellular matrix (ECM) was performed to evaluate the

metabolic profile of the 3D printed ear under culture. The amount of collagen content – a major

component of the extracellular matrix secreted by the chondrocytes under culture – was

determined from the measurement of HYP in the digest.36

The amount of HYP in the sample was

determined from the absorbance measured using a standard curve created from L-

Hydroxyproline (Sigma Aldrich) (Fig.4.14). The samples were first hydrolyzed in 6 N HCl at

110 ºC for 18 h in a test tube. The volume of the sample containing an estimate of 0.2-6 μg HYP

135

was brought to 2 mL. The pH of the solution was also adjusted to the pH of chloramine-T

reagent. The 2 mL sample was then mixed in a test tube with 1 mL chloramine-T solution both

having a temperature of about 20 ºC and kept for about 15 minutes. 1 mL of the

aldehyde/perchloric acid solution was then added and mixed thoroughly. The temperature of the

sample was then kept at 60 ºC by immersing in to a hot water bath for about 20 minutes. The test

tube was then cooled to room temperature under running water and absorbance was detected at

560 nm with a spectrophotometer.

Figure 4.14 Hydroxyproline standard curve obtained from L-Hydroxyproline.

The proteoglycan content in the ECM was evaluated by the quantification of the total

sulfated GAGs in the sample. The GAG assay was performed by the spectrophotometric

detection of the chromatic changes that occur when 1,9-dimethylmethyline blue (DMB), a

136

cationic dye, binds to the sulfate and carboxyl groups present in the GAG chain. To eliminate

interference from the binding of DMB with the carboxyl groups in the alginate, the assay was

performed at a pH of 1.5, which has been shown to block the effect from the alginate. Stable

solutions of the DMB dye were prepared according to standard protocols 37

and mixed with the

sample digest, and the absorbance at 595 nm was read. A standard curve was created using

Chondroitin-6-Sulfate (C-6-S) from shark cartilage (Sigma Aldrich) and used to obtain the

quantitative values of the GAG content from the observed absorbance (Fig. 4.15).

.

Figure 4.15 GAG standard curve obtained from Chondroitin-6-Sulphate.

4.9.7 Histologic Evaluation of the Bionic Ear

We performed basic histological analysis for the general assessment of cell and tissue

morphology and distribution using the hematoxylin-eosin stain.38

For the histologic evaluation of

137

the cartilage tissue formed in the bionic ear under culture, specimens were removed from the ear

after 6 weeks and 10 weeks of in vitro culture and kept frozen. The specimens were then fixed in

10% unbuffered formalin supplemented to 0.1 M in CaCl2 for about 24 hours and then embedded

in paraffin. The specimens were coarsely sectioned using a razor blade, followed by a

microtome. For hematoxylin-eosin staining, the sections were first rinsed with 3 changes of 5

minutes in xylene followed by rinsing in 95% ethanol for 2 minutes. The sections were then

immersed in Mayer’s hematoxylin solution (1 g potassium ammonium, 1 g Hematoxylin, 0.2 g

sodium iodide and 1 g citric acid in 1000 mL distilled water) for 10 minutes. The sections taken

out of the staining solution were rinsed in running tap water and in 95% ethanol. The sections

were then immersed in 0.25% Eosin Y solution (250 mL of Eosin Y stock solution and 5 mL of

glacial acetic acid in 800 mL of 80% ethanol) for a minute followed by 3 changes in 100%

alcohol. Finally, the sections were mounted on a glass slide and rinsed with xylene.

The sections were also stained with Safranin O to visualize the amount of proteoglycan

content. The sections were deparaffinized and hydrated using distilled water followed by

immersing in Weigert’s Iron Hematoxylin for 5 minutes. The sections were washed gently in

distilled water few times until the excess dye is removed. The tissue was then differentiated in

1% acid- alcohol (100 mL of 70% ethanol and 1 mL of glacial acetic acid) solution for 5 seconds

and rinsed thoroughly in distilled water. The sections were subsequently immersed in 0.02% Fast

green (0.05 g of Fast green in 25 mL distilled water) for approximately 1 minute, followed by

1% acetic acid for 30 seconds. The staining was completed via immersing in 1% Safranin O

(2.5g Safranin O in 250 mL of distilled water) for 10 minutes. The sections were then gently

rinsed in 95% ethanol and slowly dehydrated by 2 changes in 95% ethanol and 2 changes in

100% ethanol. The stained sections were mounted on cover slips and rinsed with xylene.

138

The stained sections were examined with transmitted light microscopy on a Nikon

Eclipse 50i microscope (Nikon, Melville, N.Y.). All images were recorded with a DXM 1200F

Nikon color digital camera.

4.9.8 Biomechanical Characterization

To characterize the tensile properties of the neocartilage tissue in the 3D printed bionic ear,

dogbone samples were 3D printed in the same chondrocytes density (~60 million cells/mL) as

the bionic ear and at a lower density (20 million cells/mL) for comparison, and cultured under

similar conditions for 10 weeks. Samples from various points in the culture were retrieved and

uniaxial tensile testing was performed with an Instron 5848 Microtester (Instron, Canton, MA) 39

(Fig. 4.16). Prior to testing, the dimensions of the sample in the gauge area were measured using

a digital caliper. The samples were then clamped between serrated grips. A pre-load (< 0.5 N)

was applied to ensure proper seating of the sample. The samples were then extended at a strain

rate of 0.1% of their gauge length per second until failure occurred. Stiffness of the samples was

determined from the linear region of the load-elongation curves. Young’s moduli were calculated

using the measured cross sectional area and gauge length.

139

Figure 4.16 Tensile testing of 3D printed dog bone samples. (a) 3D model of the dog bone

sample. (b) Image of the 3D printed dog bone sample under culture and (c) out of culture. (d-e)

Tensile testing of the dog bone sample using Instron 5848 microtester. (f) Image of the sample

after failure. (g) Representative load-elongation curves for the dog bone samples. Scale bars are

1 cm.

Hardness of the cartilage tissue after 10 weeks of culturing was determined by

nanoindentation measurements at various anatomic sites of the ear auricle. Samples were tested

on a Hysitron TriboIndenter (Hysitron Inc., Minneapolis, MN) using a 100 μm radius of

140

curvature conospherical diamond probe tip40

(Fig. 4.17). The round tip was chosen instead of a

sharp tip to allow for better conformity during the contact to the tissue sample. A standard

trapezoidal loading profile with a loading rate of 20 μN/s, a peak load of 200 μN, and a hold

period of five seconds was applied in three repetitions to ten sites in each sample. The method of

Oliver and Pharr was used to obtain reduced modulus (Er) and hardness (H) from the unloading

curves.41

The reduced modulus is related to Young’s modulus, E, by 1/Er = (1-ν12)/E1 + (1-

ν22)/E2, where subscript 1 refers to the indenter material, subscript 2 refers to the indented

material, and ν is Poisson’s ratio. The ideal spherical tip function was used to calculate the

projected contact area at the maximum load.

Figure 4.17 Hardness measurement of the 3D printed ear cartilage. (a) Image of the

nanoindentation setup using a Hysitron TriboScope. (b) Representative load-displacement curves

for the 3D printed cartilage.

141

4.10 Conclusion

Our strategy of fabricating designer “cyborg ears” represents a proof of principle of

combining the versatility of additive manufacturing techniques with novel tissue engineering

concepts. The cellular self-assembly into tissues draws on the principles of developmental

biology to offer three dimensional intertwining of biology and electronics. The result is the

generation of bona fide bionic organs in both form and function, as validated by tissue

engineering benchmarks and electrical measurements, with the latter demonstrating

“superhuman” capabilities. This concept of co-3D printing interlaced biological, structural, and

electronic components thus represents a new, general strategy in merging electronics with

biological systems. Such hybrids are distinct from either engineered tissue or planar/flexible

electronics and offer a unique way of attaining a seamless integration of electronics with tissues

to generate “off-the-shelf” cyborg organs. Finally, future work will explore the incorporation of

other classes of materials, such as piezoelectrics, for acoustic-to-electric signal transduction.

142

4.11 References

1 Lavine, M., Roberts, L. & Smith, O. If I Only Had a... Science 295, 995 (2002).

2 Craelius, W. The bionic man: restoring mobility. Science 295, 1018-1021 (2002).

3 Vogel, G. Part man, part computer: researcher tests the limits. Science 295, 1020 (2002).

4 Tian, B. et al. Three-dimensional, flexible nanoscale field-effect transistors as localized

bioprobes. Science 329, 830-834 (2010).

5 Green, D. W. Tissue bionics: examples in biomimetic tissue engineering. Biomed. Mater.

3, 034010 (2008).

6 Timko, B. P. et al. Electrical recording from hearts with flexible nanowire device arrays.

Nano Lett. 9, 914-918 (2009).

7 Nguyen, T. D. et al. Piezoelectric nanoribbons for monitoring cellular deformations. Nat.

Nanotech. 7, 587-593 (2012).

8 Viventi, J. et al. Flexible, foldable, actively multiplexed, high-density electrode array for

mapping brain activity in vivo. Nat. Neurosci. 14, 1599-1605 (2011).

9 Kim, D. H. et al. Epidermal electronics. Science 333, 838-843 (2011).

10 Mannoor, M. S. et al. Graphene-based wireless bacteria detection on tooth enamel. Nat.

Commun. 3, 763 (2012).

11 Pampaloni, F., Reynaud, E. G. & Stelzer, E. H. K. The third dimension bridges the gap

between cell culture and live tissue. Nat. Rev. Mol. Cell Biol. 8, 839-845 (2007).

12 Langer, R. & Vacanti, J. P. Tissue engineering. Science 260, 920-926 (1993).

13 Langer, R. & Vacanti, J. P. Artificial organs. Sci. Am. 273, 130-133 (1995).

143

14 Jayawarna, V. et al. Nanostructured Hydrogels for Three-Dimensional Cell Culture

Through Self-Assembly of Fluorenylmethoxycarbonyl–Dipeptides. Adv. Mater. 18, 611-

614 (2006).

15 Lee, M. Y. et al. Three-dimensional cellular microarray for high-throughput toxicology

assays. Proc. Natl. Acad. Sci. USA 105, 59-63 (2008).

16 Mapili, G., Lu, Y., Chen, S. & Roy, K. Laser-layered microfabrication of spatially

patterned functionalized tissue-engineering scaffolds. J. Biomed. Mater. Res. B Appl.

Biomater. 75, 414-424 (2005).

17 Marler, J. J., Upton, J., Langer, R. & Vacanti, J. P. Transplantation of cells in matrices for

tissue regeneration. Adv. Drug Delivery. Rev. 33, 165-182 (1998).

18 Shieh, S. J., Terada, S. & Vacanti, J. P. Tissue engineering auricular reconstruction: in

vitro and in vivo studies. Biomaterials 25, 1545-1557 (2004).

19 Chang, S. C., Tobias, G., Roy, A. K., Vacanti, C. A. & Bonassar, L. J. Tissue engineering

of autologous cartilage for craniofacial reconstruction by injection molding. Plast.

Reconstr. Surg. 112, 793-799; discussion 800-791 (2003).

20 Khademhosseini, A., Langer, R., Borenstein, J. & Vacanti, J. P. Microscale technologies

for tissue engineering and biology. Proc. Natl. Acad. Sci. USA 103, 2480-2487 (2006).

21 Cao, Y., Vacanti, J. P., Paige, K. T., Upton, J. & Vacanti, C. A. Transplantation of

chondrocytes utilizing a polymer-cell construct to produce tissue-engineered cartilage in

the shape of a human ear. Plast. Reconstr. Surg. 100, 297-302; discussion 303-294

(1997).

144

22 Symes, M. D. et al. Integrated 3D-printed reactionware for chemical synthesis and

analysis. Nature Chem. 4, 349-354 (2012).

23 Jones, N. Science in three dimensions: the print revolution. Nature 487, 22-23 (2012).

24 Reiffel, A. J. et al. High-fidelity tissue engineering of patient-specific auricles for

reconstruction of pediatric microtia and other auricular deformities. PLoS One 8, e56506

(2013).

25 Yeong, W. Y., Chua, C. K., Leong, K. F. & Chandrasekaran, M. Rapid prototyping in

tissue engineering: challenges and potential. Trends Biotechnol. 22, 643-652 (2004).

26 Cohen, D. L., Malone, E., Lipson, H. & Bonassar, L. J. Direct freeform fabrication of

seeded hydrogels in arbitrary geometries. Tissue Eng. 12, 1325-1335 (2006).

27 Malone, E., Berry, M. & Lipson, H. Freeform fabrication and characterization of Zn-air

batteries. Rapid Prototyping J. 14, 128-140 (2008).

28 Someya, T. et al. A large-area, flexible pressure sensor matrix with organic field-effect

transistors for artificial skin applications. Proc. Natl. Acad. Sci. USA 101, 9966-9970

(2004).

29 Bichara, D. A. et al. The tissue-engineered auricle: past, present, and future. Tissue Eng.

Part B Rev. 18, 51-61 (2012).

30 Marijnissen, W. J. C. M. et al. Alginate as a chondrocyte-delivery substance in

combination with a non-woven scaffold for cartilage tissue engineering. Biomaterials 23,

1511-1517 (2002).

31 Dobratz, E. J., Kim, S. W., Voglewede, A. & Park, S. S. Injectable Cartilage Using

Alginate and Human Chondrocytes. Arch. Facial Plast. Surg. 11, 40-47 (2009).

145

32 Kelly, D. J. et al. Biochemical markers of the mechanical quality of engineered hyaline

cartilage. J. Mater. Sci. Mater. Med. 18, 273-281 (2007).

33 Li, C., Pruitt, L. A. & King, K. B. Nanoindentation differentiates tissue-scale functional

properties of native articular cartilage. J. Biomed. Mater. Res. A 78, 729-738 (2006).

34 Hott, M. E., Megerian, C. A., Beane, R. & Bonassar, L. J. Fabrication of tissue

engineered tympanic membrane patches using computer-aided design and injection

molding. Laryngoscope 114, 1290-1295 (2004).

35 Young-Yo, K., Sah, R. L. Y., Doong, J. Y. H. & Grodzinsky, A. J. Fluorometric assay of

DNA in cartilage explants using Hoechst 33258. Anal. Biochem. 174, 168-176 (1988).

36 Stegemann, H. & Stalder, K. Determination of hydroxyproline. Clin. Chim. Acta 18, 267-

273 (1967).

37 Enobakhare, B. O., Bader, D. L. & Lee, D. A. Quantification of sulfated

glycosaminoglycans in chondrocyte/alginate cultures, by use of 1,9-dimethylmethylene

blue. Anal. Biochem. 243, 189-191 (1996).

38 Schmitz, N., Laverty, S., Kraus, V. B. & Aigner, T. Basic methods in histopathology of

joint tissues. Osteoarthr. Cartilage 18, S113-S116 (2010).

39 Baker, B. M., Nathan, A. S., Huffman, G. R. & Mauck, R. L. Tissue engineering with

meniscus cells derived from surgical debris. Osteoarthr. Cartilage 17, 336-345 (2009).

40 Ebenstein, D. M. & Pruitt, L. A. Nanoindentation of biological materials. Nano Today 1,

26-33 (2006).

146

41 Oliver, W. C. & Pharr, G. M. Improved technique for determining hardness and elastic

modulus using load and displacement sensing indentation experiments. J. Mater. Res. 7,

1564-1580 (1992).

147

Chapter 5

3D Printed Bionic Leaves for Photosynthetic

Bioelectricity

5.1 Overview

This chapter presents preliminary results on our attempts to use 3D printing to create a bionic

leaf, by assembling isolated thylakoid photosynthetic functional units with graphene nanoribbon

electronics into a leaf-shaped hierarchical structure for harvesting photosynthetic bioelectricity

(Fig. 5.1).

148

Figure 5.1. Schematic illustration of the bionic leaf architecture showing the hierarchical

integration of functional components via the 3D printing process and the generation of

photosynthetic bioelectricity.

5.2 Introduction

Leaf is a plant’s photosynthetic organ, serving as reaction centers for food energy production.

The whole structure of a natural leaf is evolutionarily tailored to efficiently perform the various

photosynthetic tasks such as - efficient light harvesting by the lens like epidermal cells,

photolytic water splitting by high surface area thylakoid cylindrical stacks (granum) in

chloroplast and the transport of water and photosynthates through the network of vascular

bundles, to and from chloroplasts, the organelles that perform photosynthesis within the

mesophyll cells1-3

. The vascular bundle consists of two conducting channels, xylem and phloem,

respectively for conduction of the primary photosynthetic raw material -water-towards the

chloroplasts and the carrying away of the photosynthetic outputs for storage and consumption at

various sites.

Natural leaf is thus a hierarchical arrangement of several functional components into a

highly efficient photosynthetic machinery4. The creation of an efficient bionic system by

integrating functional components that mimic the photosynthetic machinery of natural leaf for

efficient capture of sunlight photons, oxidation of water to generate high energy electrons and its

conduction away from the reaction centers, would be a major development in biomimetic ways

for energy harvesting. Chloroplasts enclose thylakoid membranes which are the centers of light

dependent reaction of photosynthesis, suspended in the chloroplast stroma, where ultimately the

production of food is taken place via Calvin cycle. Specifically, the photosynthetic unit

149

assembled in the thylakoid membrane consists of antenna pigments and reaction centers

involving two photosystems (I & II). The absorption of light causes an electron to be ejected

from the Chlorophyll reaction centers and is then transferred vectorially via a pathway consisting

of various mediators through a series of redox reactions from the inner to the outer section of the

membrane5. The photosynthetic reaction centers present in the thylakoids are able to use the

absorbed light energy to split H2O and generate O2, H+, a pH gradient and high energy electrons

(e-) with a quantum efficiency of nearly 100% (ie.one quantum of light yields to one electron

transfer). The energy from the pH gradient is subsequently used to produce sugars and

polysaccharides in the Calvin cycle of the photosynthesis11

.

The unmatched quantum efficiency boasted by the natural photosynthetic process has

attracted a lot of interests in the recent years for energy conversion applications. There have been

attempts for harvesting the biomass stored as polysaccharide for the production of bioelectricity

by utilizing microbial fuel cell systems. However, the maximum efficiency for converting the

absorbed solar energy in to polysaccharides by a photosynthetic organism is a only 27%6-11

.

Also, conversion of biomass in to a form of energy that can be utilized and stored easily will

involve additional steps which will in turn reduce the overall efficiency. As an alternative, the

extraction of high energy electron from the photosynthetic electron transport chain before they

are used to fix CO2 in the Calvin cycle could lead to light energy conversion with higher

efficiency. Interestingly, the study done with nanoelectrodes inserted into Chlamydomonas

reinhardtii unicellular algal cells demonstrated the feasibility of this concept for direct extraction

of photosynthetic electrons11

.

However, the utilization of whole photosynthetic cells (Mesophyll, cyanobacterial or

algal cells) for this purpose suffers from the drawbacks of having respiration competing with

150

photosynthesis in sharing the electron transfer pathways and also for providing nutrients to

sustain12,13

. In addition for direct light–electricity conversion applications, it is preferable to use a

higher order plant based system that uses only water as the electron donor such as PSII, rather

than isolated PSI complexes, which require an alternate electron donor. However, when isolated

plant photosynthetic systems directly immobilized on electrodes were used, they suffered from

degeneration of the biomolecules and poor electrical communication14,15

. On the other hand,

utilization of thylakoids, the photosynthetic organelles that performs the light dependent reaction

and houses the reaction center complexes, offers the advantages of high individual protein

stability, fairly simpler immobilization procedures and multiple electron transfer routes. Recent

study involving immobilized thylakoid membranes on multi-walled carbon nanotubes proved the

feasibility of the electron transfer from oxygen evolving complex (OEC) sites to the electrode

achieved via various points in the electron transfer pathway, in addition to a direct transfer from

PSII13

. Therefore using thylakoids as photo-biocatalysts should offer the potential for high

photo-electrochemical activity as well as high stability for energy conversion applications16

.

All of the previous attempts in mimicking the photosynthetic energy conversion of the

natural leaves focused only on replicating the functionality but did not pay attention to the

geometrical architecture. However, complete utilization of photosynthetic functionality of the

thylakoid functional components and efficient collection of bioelectricity, demands that the

bionic system that we engineer to both have similar geometrical architecture for the efficient

performance of primary photosynthetic reaction and analogous functional modules, which could

i) transport the primary photosynthetic raw material, water to the thylakoid lumen and ii)

transfer the electron generated via photolysis of water away from the reaction center to minimize

151

the wasteful recombination reactions, while synergistically, incorporating the thylakoid based

natural photosynthetic engines.

5.3 3D Printing of Bionic Leaf

Here we describe a novel approach that involves the construction of a Bionic leaf using 3D

printing techniques by copying the complex architecture of leaves, with synergistically

engineered essential functional modules to realize generation and harvesting of photosynthetic

bioelectricity from the natural thylakoid membranes. Specifically, we propose to assemble

isolated thylakoid photosynthetic functional units with graphene nanoribbon based electronic

interfacing material into a leaf-shaped hierarchical structure consisting of functional modules

analogous to the vascular bundles of natural leaf to realize harvesting of light energy for

photosynthetic current generation and conduction (Fig. 5.2 A).

Figure 5.2. 3D Printed Bionic Leaf for Energy (A) Conceptual design of the Bionic Leaf (B)

(top) Fresh spinach leaves and isolated thylakoids. (bottom) Dry GNRs and mixed with

PEDOT:PSS conductive matrix. (C) 3D printing of the thylakoids and electronic conductive

matrix. (D) Image of the Bionic Leaf showing vascular bundles for water input and electrodes for

current output.

152

Electrical interfacing of the thylakoid membranes with organic polymer conduction

medium with dispersed graphene nanoribbons will allow for the collection of photosynthetic e-

from various points in the electron transport pathways (such as plastoquinone (PQ, acceptor side

of PSII) pool or reduced ferredoxin (Fd, acceptor side of PSI)) and conduct them away through

an external circuit (Fig. 5.2B). This will enable generation of photosynthetic bioelectric current

before being utilized for the production of sugars and polysaccharides in the Calvin cycle.

Further, the incorporation of a 3D printed vasculature network that resembles the vascular

bundles in natural leaves consisting of a “bionic xylem” made of cellulose microchannels will

allow for supplying water to the thylakoid lumen and a “bionic phloem” made of Ag electrode

networks will enable transporting the bioelectrons produced for storage (Fig. 5.2 C and D).

Additionally, by using the entire thylakoid membranes instead of isolated photosystem

complexes in our design, we will be able to make electrical interface at various sites of the

electron transfer pathways using conducting polymer/graphene nanoribbon matrix to possibly

achieve high electron transfer flux for photo-current generation.

5.4 Thylakoid Isolation and Characterization

Our preliminary experiments involving the isolation, characterization and 3D printing of

thylakoids have yielded promising results. Thylakoids were isolated from fresh organic spinach

leaves according to previously reported procedures17

. In brief, the cleaned, deveined spinach

leaves were homogenized in a chilled blender. The homogenate was then filtered through four

layers of cheese cloth

153

Figure 5.3. Isolation of Thylakoids (A) Deveining of spinach leaves (B) Fresh spinach leaves

being homogenized in a cold blender (C) Filtration of the homogenate. (D) Image of the filtered

solution (E) Isolated thylakoids after centrifugation.

and the thylakoid membranes are then subsequently isolated from crude cell debris and other

subcellular components after multiple steps of centrifugation at various speeds. The isolated

thylakoids were characterized via optical and fluorescent microscopy using Nile red dye (Acros

organics) (Fig 5.4).

154

Figure 5.4. (A) Optical microscopic image of the isolated thylakoids. (B) Fluorescent

microscopic images of Nile red labelled thylakoids.

5.4.1 Determination of the Chlorophyll Content in Isolated Thylakoids

The chlorophyll concentration in the isolated thylakoids is then determined by mixing with 80%

acetone and filtering through Whatman # 4 filter paper followed by measuring the absorbance at

663nm and 645nm in a spectrophotometer. The chlorophyll concentration was then calculated

using Beer-Lambert law to be 2.799 mg/mL (Fig.5.5).

155

Figure 5.5. Determination of the Chlorophyll content in the isolated thylakoids (A)

Determination of Chlorophyll contents of various isolation methods (B) UV-Vis spectrum of the

isolated thylakoids.

5.5 Photosynthetic Electron Generation: Hill Reaction

Next, the photosynthetic functionality- light induced electron transport via photolysis of water- at

the isolated thylakoid membranes was verified using Hill reaction by using

dichlorophenolindophenol (DCPIP) as the Hill reagent18

as shown below.

156

Figure 5.6. Hill Reaction using DCPIP. (A) and (B) Experimental procedure showing the

exposure of the thylakoids + DCPIP to light before measuring the OD (C) Image of cuvette

containing thylakoids alone (D) image of cuvette containing DCPIP alone (E) image of cuvette

containing thylakoids + DCPIP and (F) image of cuvette containing thylakoids + DCPIP after the

exposure to light.

Illumination of DCPIP mixed with thylakoids was found to be readily reduced rendering

a bright blue solution to a pale color, whereas the control measurement done in the absence of

light was found to have no significant change in color, verifying the creation of photosynthetic

electron by the thylakoids in the presence of light (Fig 5.6). Further, to quantitatively measure

157

the change in color of the Hill reagent upon reduction with the photosynthetic electrons, we

measured the absorbance of light in a spectrophotometer.

Figure 5.7. Change in absorbance of the sample containing thylakoids +DCPIP after the

exposure to light. Measurement of absorbance without the exposure to light is used as a control.

A sample of thylakoids mixed with Hill reagent was exposed to light every 30 seconds

and the OD was measured in between. The experiment was conducted for up to 12 minutes (Fig.

5.7). The reduction of the percentage absorbance of the light clearly indicates the reduction of

the Hill reagent and indirectly verifies the production of photosynthetic electrons by the isolated

thylakoids upon the exposure to light. A sample containing thylakoids mixed with the Hill

reagent, where the OD is measured at the same intervals, however not being exposed to light in

between was used as a control and did not show significant change in the percentage absorbance

158

over time. This verifies that the reduction of the DCPIP occurred as a result of the production

photosynthetic electrons by the thylakoid membranes.

5.6 Electronic Conduction Medium- Formulation and Characterization

Next to enable the conduction of the photosynthetic electron away from the reaction center, an

electronic conduction medium was formulated (Fig.5.8)16

.

Figure 5.8. Schematic illustration of the interfacing of organic conduction medium with

thylakoid membranes to conduct the photosynthetic electrons away.

Graphene nano ribbons (GNRs) has been used as conducting material for a number of

application due to its excellent electronic properties. However, the dry powdery nature of the

GNRs does not allow for easy use in an extrusion based 3D printing process. On the other hand,

organic polymer based conducting inks have been widely used as a printable conduction

medium. But, it suffers from low conductivity when compared to solid state electronic

conductors. We therefore formulated our conduction medium from a dispersion of GNRs in a

solution of poly (3,4-ethylenedioxythiophene)/ poly(styrene sulfonate) (PEDOT/PSS) based

electrically conductive polymer (Fig 5.9).

159

Figure 5.9. Formulation of the electronic conduction medium (A) Image of the GNR powder

(B) image of GNR mixed with PEDOT:PSS at concentration of 0.3 wt%. (C) plot of conductivity

Vs. concentration of GNRs in wt% in the PEDOT:PSS/GNR mixture.

The electrical conductivity of the dispersion of GNRs in PEDOT:PSS at various weight

percentages was characterized to find out the optimum composition (Fig.5.9). A formulation of

0.2 weight percentage of GNRs in PEDOT:PSS showed a dramatic change in conductivity and

any further increase in the concentration of GNRs did not change the conductivity significantly19

.

This indicated that the percolation threshold of the GNRs in PEDOT:PSS was reached by a

concentration of ~0.2 weight percentage of GNRs. We therefore decided to use a concentration

of ca 0.3 weight percentage of GNRs dispersed in PEDOT:PSS as a conductive matrix to ensure

maximumm conductivity.

5.6.1 Characterization of Electronic Conduction Medium

Next, the electronic properties of the conduction medium were characterized using X-ray

diffraction (XRD) and Raman spectroscopy20

.

160

Figure 5.10. XRD characterization of the electronic conduction medium. (bottom) XRD

spectrum of pure PEDOT:PSS (middle) XRD spectrum of GNRs alone and (top) XRD spectrum

of GNRs in PEDOT:PSS.

Figure 5.10 shows the XRD patterns of pure PEDOT:PSS, pure GNRs and PEDOT:PSS

with dispersed GNRs. There was no sharp peaks in the pure PEDOT:PSS films indicative of its

predominantly amorphous nature. The pure GNRs showed a sharp peak around 2Θ value of

around 25.6o. The GNR/PEDOT:PSS mixture showed the characteristic peaks of both the pure

GNRs and PEDOT:PSS but no other additional bands21

. This implies the absence of any covalent

interaction between the dispersed GNRs and PEDO:PSS conducting ink.

161

Figure 5.11. Raman spectroscopy of the electronic conduction medium (bottom) Raman

spectrum of GNRs alone. (middle) Raman spectrum of pure PEDOT:PSS. (top) Raman spectrum

of the conducting ink with GNRs in PEDOT:PSS.

Raman spectra of pure GNRs, pure PEDOT:PSS films and the PEDOT:PSS with

dispersed GNRs is shown in Figure 5.11. The Raman spectra of pure PEDOT:PSS showed peaks

at 1143/1097 cm-1

(C-C in-plane bending), 1256 cm-1

(C-C in-plane symmetric stretching), 1365

cm-1

(C-C stretching deformations), 1421 cm-1

(Cα=Cβ symmetric vibrations) and 1521

cm−1

(Cα=Cβ asymmetric vibrations). The Raman spectrum of GNRs /PEDOT:PSS mixture

showed similar peaks to pure PEDOT:PSS with possibly a widened (shoulder) peak between

1250 and 1300 cm-1

. This indicates a certain physio-chemical interaction between the GNRs and

PEDOT:PSS conducting polymer.

162

5.7 Photosynthetic Material

The biomimetic photosynthetic material was then synthesized from the isolated thylakoid

membranes and the PEDOT:PSS/GNR conductive matrix at various mixing ratios (Fig.5.12).

Figure 5.12. Formulation of the photosynthetic material as a mixture of the isolated spinach

thylakoids and the conductive ink containing dispersed GNRs in PEDOT:PSS organic

conducting polymer.

Characterization of the photosynthetic material for uniform dispersion of thylakoid membranes

was performed using fluorescent microscopy, SEM and TEM imaging. Figure 5.13 A. shows the

fluorescent microscopic images of the thylakoid membranes uniformly dispersed in the

conducting matrix. Fig.5.13 B and C shows the SEM the TEM of the photosynthetic material

respectively.

163

Figure 5.13. Characterization of the photosynthetic material. (A) Fluorescent image of the

photosynthetic material showing fluorescein labelled thylakoids. (B) SEM of the photosynthetic

material with white arrow indicating the embedded thylakoid membrane. (C) TEM of the

photosynthetic material.

5.8 Electrical Testing of the Photosynthetic Material

Next, the changes in the electrical conductivity of the thylakoid/PEDOT:PSS+GNR

photosynthetic matrix in response to light was measured at various mixing ratios. The

photosynthetic material was deposited on to Au interdigitated microelectrodes of separation

150μm and the change in current with the exposure to light was measured at applied bias

voltages of +/- 20V using a probe station. Figure 5.14 shows the percentage change in current

after the exposure to light of the thylakoid/PEDOT:PSS+GNR matrix with increasing weight

percentage of the conducting matrix. Thylakoids alone without the addition of any conducting

medium was observed to show about 5% increase in current after the exposure to light. Current

in matrix consisting of conducting medium without any thylakoids was found to decrease after

being illuminated by light, possibly due to the light induced degradation of the PEDOT: PSS

matrix. It was found that a minimum concentration of about 10 weight percentages of thylakoids

is required to have a positive change in the current after the exposure to light. A concentration of

164

about 40 percentage by weight of thylakoids in PEDOT:PSS was found to show the maximum

change in current up on light illumination.

Figure 5.14. Change in current observed from the mixture of photosynthetic material biased at

constant voltages of +/-20V, as a result of the exposure to light, versus weight percentage of the

GNR/PEDOT conductive ink in the mixture.

5.9 3D Printable Bionic Leaf Architecture

A CAD drawing of the bionic leaf was created by drawing analogy to the hierarchical

structure of a natural leaf, consisting of the essential functional modules including

Thylakoid/PEDOT:PSS+GNR bionic photosynthetic material, nanocellulose based bionic xylem

to transport water to the thylakoid lumen, Ag electrode base bionic phloem to transport the

bioelectrons produced for storage and a silicone based transparent epidermis layer (Fig. 5.15).

165

We also performed preliminary 3D printing experiments, to test the ability of the 3D printer to

faithfully create leaf architecture with thylakoid/PEDOT:PSS active photosynthetic matrix.

Figure 5.15. CAD of the bionic leaf architecture (A) CAD of the conceptual design of the

Bionic Leaf (B) the design of bionic leaf broken down in to various hierarchical functional

structures.

5.10 Conclusion

In summary, the creation of a bionic leaf structure using additive manufacturing techniques by

mimicking the complex hierarchical structure of native leaves, for the direct harvesting of

photosynthetic bioelectricity was proposed and preliminary experiments were conducted.

Specifically, the possibility of electrical integration of isolated thylakoid membranes with an

organic polymer conductive matrix with dispersed graphene nanoribbons was investigated for

the generation and collection of photosynthetic electrons from the light induced water splitting

reactions in the thylakoid membranes. This enabled the harvesting of photosynthetic

166

bioelectricity before being utilized for the food production in the Calvin cycle of the natural

photosynthesis. Further, the possibility of 3D printing of vasculature network that resembles the

vascular bundles in natural leaves consisting of a “bionic xylem” made of nanocellulose

microchannels to provide water to the thylakoid lumen and a “bionic phloem” made of Ag

electrode networks for transporting the bioelectrons produced for storage were also studied.

Future work will address the possibility of incorporating a cathode layer separated by a nafion

based ion permeable membrane for the creation of a full electrochemical cell for light induced

generation of bioelectricity.

5.11 References

1 Nikolopoulos, D., Liakopoulos, G., Drossopoulos, I. & Karabourniotis, G. The

relationship between anatomy and photosynthetic performance of heterobaric leaves.

Plant Physiology 129, 235-243 (2002).

2 Shimoni, E., Rav-Hon, O., Ohad, I., Brumfeld, V. & Reich, Z. Three-dimensional

organization of higher-plant chloroplast thylakoid membranes revealed by electron

tomography. Plant Cell 17, 2580-2586 (2005).

3 Smith, W. K., Vogelmann, T. C., DeLucia, E. H., Bell, D. T. & Shepherd, K. A. Leaf

form and photosynthesis: Do leaf structure and orientation interact to regulate internal

light and carbon dioxide? BioScience 47, 785-793 (1997).

4 Zhou, H. et al. Artificial inorganic leafs for efficient photochemical hydrogen production

inspired by natural photosynthesis. Advanced Materials 22, 951-956 (2010).

5 Szabó, I., Bergantino, E. & Giacometti, G. M. Light and oxygenic photosynthesis:

Energy dissipation as a protection mechanism against photo-oxidation. EMBO Reports 6,

629-634 (2005).

167

6 Chaudhuri, S. K. & Lovley, D. R. Electricity generation by direct oxidation of glucose in

mediatorless microbial fuel cells. Nature Biotechnology 21, 1229-1232 (2003).

7 Heyndrickx, M., De Vos, P. & De Ley, J. H2 production from chemostat fermentation of

glucose by Clostridium butyricum and Clostridium pasteurianum in ammonium- and

phosphate limitation. Biotechnology Letters 12, 731-736 (1990).

8 Min, B., Cheng, S. & Logan, B. E. Electricity generation using membrane and salt bridge

microbial fuel cells. Water Research 39, 1675-1686 (2005).

9 Park, D. H. & Zeikus, J. G. Electricity generation in microbial fuel cells using neutral red

as an electronophore. Applied and Environmental Microbiology 66, 1292-1297 (2000).

10 Rosenbaum, M., Schröder, U. & Scholz, F. Utilizing the green alga Chlamydomonas

reinhardtii for microbial electricity generation: A living solar cell. Applied Microbiology

and Biotechnology 68, 753-756 (2005).

11 Ryu, W. et al. Direct extraction of photosynthetic electrons from single algal cells by

nanoprobing system. Nano Letters 10, 1137-1143 (2010).

12 Scherer, S. Do photosynthetic and respiratory electron transport chains share redox

proteins? Trends in Biochemical Sciences 15, 458-462 (1990).

13 Calkins, J. O., Umasankar, Y., O'Neill, H. & Ramasamy, R. P. High photo-

electrochemical activity of thylakoid-carbon nanotube composites for photosynthetic

energy conversion. Energy and Environmental Science 6, 1891-1900 (2013).

14 Mershin, A. et al. Self-assembled photosystem-I biophotovoltaics on nanostructured TiO

2 and ZnO. Scientific Reports 2 (2012).

15 Esper, B., Badura, A. & Rögner, M. Photosynthesis as a power supply for (bio-)hydrogen

production. Trends in Plant Science 11, 543-549 (2006).

168

16 Bedford, N. M., Winget, G. D., Srikoundinya, P. & Steckl, A. J. Immobilization of stable

thylakoid vesicles in conductive nanofibers by electrospinning. Biomacromolecules 12,

778-784 (2011).

17 Izawa, S. & Good, N. E. Effect of salts and electron transport on the conformation of

isolated chloroplasts. I. Light-scattering and volume changes. Plant Physiol. 41, 533-543

(1966).

18 Hill, R. Oxygen evolved by isolated chloroplasts [5]. Nature 139, 881-882 (1937).

19 Angelo, P. D., Cole, G. B., Sodhi, R. N. & Farnood, R. R. Conductivity of inkjet-printed

PEDOT:PSS-SWCNTs on uncoated papers. Nordic Pulp and Paper Research Journal

27, 486-495 (2012).

20 Zhou, J. & Lubineau, G. Improving electrical conductivity in polycarbonate

nanocomposites using highly conductive PEDOT/PSS coated MWCNTs. ACS Applied

Materials and Interfaces 5, 6189-6200 (2013).

21 Li, J., Liu, J. C. & Gao, C. J. On the mechanism of conductivity enhancement in

PEDOT/PSS film doped with multi-walled carbon nanotubes. Journal of Polymer

Research 17, 713-718 (2010).

169

Chapter 6

Conclusions and Future Outlook

6.1 Summary of Main Conclusions

This thesis presented our explorations for the creation of multidimesionally integrated bionic

systems via the general approach of nanomaterials engineering paired with additive

manufacturing techniques, for applications in energy and biomedical sciences. Specifically, we

presented design development and study, of bionic nanosensors for biointegrated ubiquitous

sensing of pathogenic contaminants, bionic organs with enhanced functionalities by using the

example of a bionic ear with three dimensionally integrated electronics and lastly bionic leaves

for generation and harvesting of photosynthetic bioelectricity.

Chapter 1 introduced the general concept of bionic systems, addressed the design

challenges and discussed general strategies to overcome these challenges. First, a closer look on

the biological components and their features are presented, especially paying attention to their

dichotomies with the functional engineered systems and materials. Next, nanomaterials

engineering as a general strategy to overcome the disparities is introduced, featuring the general

properties and functionalities of most commonly used electronic and structural engineering

nanomaterials. Finally, a library of bio-orthogonal processes and methods are introduced, that

enable a synergistic integration between the fundamental biological functional modules and

nanoscale electronic and structural building blocks.

170

Chapter 2 presented our work on the design, development and testing of bionic

nanosensors. Specifically, direct integration of graphene based highly sensitive bionic

nanosensors with biological materials such as tooth enamel, skin, muscle tissue, and food

materials for in situ first order monitoring and detection of pathogenic bacteria is presented. The

key functionalities of the graphene/silk hybrid sensing elements are derived from a synergistic

integration of the individual materials properties and components. Bioselective detection

pathogenic bacteria including H. pylori and S. aureus were demonstrated at clinically relevant

concentrations via self-assembly of antimicrobial peptides onto graphene. Further, the

incorporation of a resonant coil eliminated the need for onboard power and external connections.

Chapter 3 presented the detailed work on the study on antimicrobial peptides (AMP) as a

biomolecular probe on electronic biosensing platforms for the detection of pathogenic bacterial

contaminants. Specifically, peptide sequence corresponding to Magainin II, an antimicrobial

peptide isolated from the skin of African clawed frog, Xenopus Laevis, were immobilized on to

the gold electrodes of an interdigitated capacitive sensor via a C-terminal Cysteine residue. The

AMP functionalized sensors were able to demonstrate semi-selective detection of pathogenic

bacteria including E. coli O157:H7 and S. typhimurium against gram positive bacteria (L.

monocytogens) and non-pathogenic strains of E. coli via impedance spectroscopic measurements.

Further, real-time detection of bacteria was demonstrated using AMP functionalized sensors in a

flow through microchannel for applications such as water quality monitoring.

Chapter 4 presented the work on the creation of a bionic ear with three dimensionally

entwined electronics and cochlear like electrodes using nanomaterials engineering and additive

manufacturing assisted bio-fabrication technique. Specifically, we generated a bionic ear via 3D

printing of a cell-seeded hydrogel matrix in the anatomic geometry of a human ear, along with an

171

intertwined conducting polymer consisting of infused silver nanoparticles. This allowed for in

vitro culturing of cartilage tissue around an inductive coil antenna in the ear, which subsequently

enables readout of inductively-coupled signals from cochlea-shaped electrodes. The result is the

generation of bona fide bionic organs in both form and function, as validated by tissue

engineering benchmarks and electrical measurements. The printed ear exhibits enhanced

auditory sensing for radio frequency reception. Overall, this approach suggests means to

intricately merge biological and nanoelectronic functionalities via 3D printing based additive

manufacturing.

Lastly, chapter 5 presented the design, development and characterization of a bionic leaf

enabled by assembling isolated thylakoid photosynthetic functional units with graphene

nanoribbon electronics into a leaf-shaped hierarchical structure for harvesting photosynthetic

bioelectricity. The approach involved the construction of a bionic leaf using 3D printing, by

replicating the complex architecture of leaves, and incorporating engineered essential functional

modules to realize generation and direct harvesting of photosynthetic bioelectricity from

thylakoid membranes. Specifically, we assembled isolated thylakoid photosynthetic functional

units from spinach leaves with interlaced graphene nanoribbons into a leaf-shaped hierarchical

structure containing vascular networks for water flow, to realize harvesting of light energy for

photosynthetic e- generation and conduction. Electrical interfacing of the thylakoid membranes

with organic polymers containing dispersed graphene nanoribbons enables the harvesting of

photosynthetic bioelectricity before being utilized for the food production in the Calvin cycle of

the natural photosynthesis. Ongoing work explores the incorporation of 3D printed vasculature

network that resembles the vascular bundles in natural leaves consisting of a “bionic xylem”

172

made of cellulose microchannels to provide water to thylakoid lumen and a “bionic phloem”

made of Ag electrode networks for transporting the bioelectrons produced for storage.

6.2 Outlook

Overall, this thesis has presented considerable advances in the design and development of bionic

systems for applications in biomedical and energy related areas. The approach that we developed

via the pairing of various bio-orthogonal processes that fall under the broader umbrella of

bionanotechnology with additive manufacturing techniques can serve as a general strategy to

overcome the dichotomies between biological systems and functional engineered components,

allowing for a seamless merging and integration between the two.

With regards to graphene based wireless “tooth tattoo sensor”, our results only represent

a prototype, ‘first generation’ platform for biointerfaced graphene nanosensors. Owing to the

semiselective nature of the interaction of AMPs with pathogenic bacteria, differentiation of

multiple species of pathogenic bacteria has not been achieved. Future work could explore the

strategies to improve this selectivity via investigations into multi-ligand and aptamer-based cap-

ture agents, and antibody-based biorecognition molecules with improved stability to provide

stringent discrimination between species of pathogenic bacteria. Exploring alternative strategies

for covalent and non-covalent functionalization of graphene sensors will be also interesting.

Finally, future challenges in the sensor development will involve further miniaturization of the

wireless coil for integration onto a smaller footprint (such as a human tooth) and testing of the

platform on in vivo systems, including tissue and teeth in living animals and humans. Overall

such approaches for the direct interfacing of biosensors onto the human body could enable

applications such as on-body health quality monitoring and adaptive threat detection.

173

With regards to the development of bionic organs, our work on bionic ear opens up new

avenues for the development of similar autonomous chimeric bionic systems with advanced

capabilities than their native counter parts. Such method produces biological, electronic and

structural components of a Bionic Organ in a single process in the precise spatial heterogeneity

for each material as prescribed by the CAD design in to the complex anatomic geometry of the

final system. Since electronic circuitry is at the core of sensory and information processing

devices, fully interweaving functional electronic components with biological tissue via 3D

printing enables the growth of “cyborg organs” exhibiting enhanced functionalities over human

physiology. Future work could explore the incorporation of other novel nanoscale functional

building blocks such as graphene nanoribbons (GNR), quantum dots (QD), ferroelectrics,

peizoelectrics and magnetostrictive materials to expand the opportunities to enable versatile

bottom-up assembly of macroscale components possessing tunable functionalities.

With regards to the creation of bionic leaves, our work opens up new opportunities for

the development of bioinspired and biomimetic materials and systems for energy generation and

harvesting. Future work could include the incorporation of a cathode layer, separated by an ion

permeable membrane material such as nafion to the current bionic leaf architecture to create a

complete electro-voltaic cell for photosynthetic current generation without the application of an

external EMF ( Electro motive force). The scaling of the process to create multiple leaves

stacked up in to a tree like architecture would be interesting. Further, through such approaches,

we anticipate to develop fundamental understanding on the possibilities of direct integration of

functional nanomaterials with the natural leaves and plants to explore the opportunities of direct

energy harvesting from green plants.

174

Another on-going work in this direction involves the interfacing of nanoengineered

functional devices with biological microorganisms such as motile bacteria to create

multifunctional self-propelling bacterial-nanobionics§. Nano-scale science and engineering in

general, enables the creation of functional devices and structures with precise geometry by

controlling matter at the atomic scales. Such nano-structured objects can be engineered to

possess a wide variety of functionalities such as mechanical, electronic, plasmonic, magnetic,

optical and sensing by exploiting novel properties and phenomena exhibited at these size scales.

However, much of the potential of such nano-objects made possible by the miniaturization

techniques is limited by the challenges in enabling propulsion or actuation to them.

Nanoscale engineering and biological systems are two fields that can mutually benefit

from each other; with nanoscale engineering providing tools to control and modify biological

processes, while biology provides the systems and materials to enable higher functionalities for

nano-engineered tools. A synergistic integration of biological components with abiotic systems

enables ways to design and create hybrid devices with some of the amazing capabilities exhibited

by living systems. For instance, living system composing of biological components possesses the

astounding ability to produce mechanical motion from chemical energy making them an

attractive means to provide actuation and motility to functional abiotic components. For

example, micro-organisms such as bacteria possess a unique ability to move at small length

scales with very high efficiency. In addition bacteria are ubiquitous, which makes their

machinery easily accessible in almost all kinds of environments. Therefore, a direct interfacing

of engineered functional nano objects with motile bacterial cells enables the creation of multi-

functional ‘nanostructure-bacteria hybrid devices’. Significantly, such bacteria-nanostructure

175

hybrid devices will be self-propelling in addition to exhibiting the engineered functionality of the

interfaced nanostructures.

In addition to the above mentioned potentially revolutionary technological applications,

our work and oncoming future developments in this direction will broaden our fundamental

understanding of the mechanisms employed in the signal transduction across the biology-

technology interface, i.e., at the interface of biological systems with the novel multi-functional

abiotic materials. For attaining a seamless three dimensional merging of biological systems with

electronic or mechanical components, an efficient transduction of biological events into readily

measurable outputs and the transduction of electronic or optical signals into biologically relevant

actions are required. This demands synthesis and characterization of novel materials with

engineered functionalities to allow for an efficient communication between the biotic/abiotic

interfaces.

In summary, the advances in the seamless integration of functional electronics and

mechanical elements with viable biological systems described in this thesis are indicative of the

immense potential of this technology. Regardless of the directions future work may take, the

results presented in this thesis provide a strong motivation for continued and expanded efforts in

the design and development of bionic systems along this line.

___________________________________

§ The work reported in this section is based on the following manuscript in preparation: T. James et al, Remote

Control of Bacteria using Plasmonic Nanoantenna (manuscript in preparation)+.