MASTER’S THESIS - TU Dresden · 2020. 9. 3. · Dresden, Germany. 31. August. 2015 . II Acknowledgments I would like to thank Prof. Gianaurelio Cuniberti for giving me the opportunity

I

Technische Universität Dresden

Biotechnologisches Zentrum (BIOTEC)

Master Program: Molecular Bioengineering

MASTER’S THESIS

to achieve the academic degree

“Master of Science” (MSc)

Highly efficient carbon-nanostructures-based gas sensors for

biomedical and other applications

presented by

Luis Antonio Panes Ruiz

First Supervisor: Prof. Dr. Gianaurelio Cuniberti

Second Supervisor: Prof. Dr. Carsten Werner

Completed at: Chair of Material Science and Nanotechnology

Max Bergmann Center of Biomaterials Dresden

Submitted on: August 31st, 2015

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I

Statement of Academical Honesty

I, hereby declare that this thesis is entirely the results of my own work except

where otherwise indicated. I have only used the resources given in the list of

reference.

Dresden, Germany. 31. August. 2015

II

Acknowledgments

I would like to thank Prof. Gianaurelio Cuniberti for giving me the opportunity

to do my master thesis at the Chair of Material Science and Nanotechnology, TU

Dresden. Also, I would like to express my deep gratitude to my co-supervisors Dr.

Viktor Bezugly and Dr. Vyacheslav Khavrus and to my colleagues Yangxi, Ye and

Simon for all their support during this project and the interesting scientific

discussions.

I would like to express my warm thanks to the CONACYT-DAAD scholarship

program for the financial and academic support I received during my master

studies.

I want to thank my new friends from the master course for all the experiences we

shared during these 2 years and last but most importantly, I want to thank my

family, specially my parents Ernesto Panes and Raquel Ruiz and my brother

Eduardo Panes for their unconditional love and support.

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Content

Acknowledgments .............................................................................................. II

Abstract .............................................................................................................. V

List of Figures ................................................................................................... VI

List of Tables ...................................................................................................... X

Abbreviations .................................................................................................... XI

1. Introduction ..................................................................................................... 1

1.1 Motivation ................................................................................................ 1

1.2 Gas Sensors ............................................................................................ 3

1.2.1 Electrical Gas Sensors ...................................................................... 5

1.2.1.1 Gas Sensors Based on Metal Oxide Semiconductors ................ 5

1.2.1.2 Gas Sensors Based on Polymers ............................................... 6

1.2.1.3 Gas Sensors Based on Nanomaterials ....................................... 6

1.2.1.3.1 Carbon Nanomaterials-based Gas Sensors.............................. 7

1.2.1.3.1.1 Gas Sensors based on CNTs ................................................ 7

1.2.1.3.1.2 Gas Sensors based on rGO ................................................... 9

1.3 Gas Sensing Properties ......................................................................... 10

1.4 Applications of Ammonia Detection ....................................................... 11

1.4.1 Medical Applications........................................................................ 11

1.4.2 Environmental Gas Analysis ........................................................... 12

1.4.3 Automotive Industry......................................................................... 12

1.4.4 Chemical Industry............................................................................ 12

2. Methods ......................................................................................................... 13

2.1 Materials used for Gas Sensing Device Fabrication .............................. 13

2.1.1 Fabrication of Interdigitated Metallic Electrodes .............................. 13

2.1.2 Nanomaterial Synthesis .................................................................. 13

2.1.2.1 Pristine Carbon Nanotubes .......................................................... 14

2.1.2.2 Boron doped single wall carbon nanotubes ................................. 14

2.1.2.3 Nitrogen doped Single Wall Carbon Nanotubes ........................... 14

2.1.3 Nanomaterial Dispersions ............................................................... 15

2.1.3.1 Dispersion Dilutions and Deposition Experiment ......................... 15

2.2 Gas Sensing Device Fabrication ............................................................ 16

2.2.1 Cleaning Process ............................................................................ 17

IV

2.2.2 Drop Casting Process ......................................................................17

2.3 Gas Sensor Characterization .................................................................18

2.3.1 Electrical Characterization ...............................................................18

2.3.2 SEM Characterization ......................................................................20

2.4 Ammonia Exposure Experiment .............................................................20

2.4.1 Gas Chamber Description ...............................................................20

2.4.1.1 The Gas Delivery System .............................................................21

2.4.1.2 The Gas Exposure Chamber ........................................................21

2.4.1.3 Control and Read-Out Electronics ................................................23

2.4.2 Determination of Ammonia Concentration .......................................25

2.4.3 Resistance Response under Ammonia Exposure ...........................26

3. Results and Discussion ................................................................................28

3.1 Dispersion Dilutions and Deposition Experiments ..................................28

3.2 Gas Sensors Characterization ...............................................................32

3.2.1 Electrical Characterization ...............................................................32

3.2.2 SEM Characterization ......................................................................37

3.3 Ammonia Exposure Experiment .............................................................47

3.3.1 Resistance Response ......................................................................47

3.3.2 Recovery Properties of Gas Sensors ..............................................57

4. Conclusions ...................................................................................................60

Outlook and Future Work .................................................................................61

Bibliography ......................................................................................................62

V

Abstract

In recent years, gas sensors have been a hot topic of research due to their wide

range of applications going from environmental studies to medical diagnosis.

Carbon based nanomaterials have emerged as a promising class of materials

allowing to reach high sensitive, simple and low cost sensors due to outstanding

nanoscale characteristics. Therefore, more investigation is needed in order to

improve existing gas sensing technologies.

Gas sensing devices based on semiconducting carbon nanotubes (SC-CNTs),

pristine carbon nanotubes (P-CNTs), boron doped carbon nanotubes (B-CNTs),

nitrogen doped carbon nanotubes (N-CNTs) and reduced graphene oxide (rGO)

were fabricated, characterized and exposed to different concentrations of ammonia

in order to compare their performance. Moreover, the recovery properties after 3

weeks in controlled conditions of temperature and humidity were also investigated.

Electrical and SEM characterization were performed on all devices and it was

discovered that the amount and quality of distribution of nanomaterial deposited

and the electric and sensing properties of the gas sensors are closely related. In

order to compare the sensitivity to ammonia of the different carbon nanomaterial-

based sensors, the devices were exposed to 1.5, 2.5, 5, 10 and 20 ppm of

ammonia. SC-CNTs based gas sensors achieved the best results to all ammonia

concentrations and the lowest recovery after 3 weeks in storage followed by N-

CNTs, P-CNTS and finally B-CNTs. In addition, quite similar results in sensing

response to all ammonia concentrations were observed for P-CNTs, B-CNTs and

N-CNTs suggesting that ammonia molecules interact mainly with carbon atoms of

CNTs, rather than with dopant atoms.

VI

List of Figures

Figure 1.1: Schematic representation of a gas sensor…………………….

4

Figure 1.2: Schematic representation of resistivity-based SWCNTs gas sensor……………………………………………………………...

8

Figure 2.1: (a) Gas sensing bare device dimensions. Red circle: Interdigitated area for nanomaterial deposition. (b) SEM image of interdigitated area.....................................................

13

Figure 2.2: Schematic representations of gas sensing devices with carbon based nanomaterial bridging the electrodes of the interdigitated area: (a) Sensors based on pristine CNTs, B-CNTs, N-CNTs and rGO. (b) Sensors based on SC-CNTs. (c) Illustration of the sensor layout with contact pads. (d) Microstructure of an IDE array with deposited carbon-based nanomaterial………………………………………………………

18

Figure 2.3: (a) Optical microscope image and (b) camera picture of the tungsten needleds contacting the device during electrical characterzation……………………………………………………

19

Figure 2.4: Diagrams for measuring source-drain IV curves (a) and transfer characteristics (b) of gas sensor……………………….

19

Figure 2.5: Schematic configuration of the gas flow apparatus……………

21

Figure 2.6: CAD model of the gas measurement chamber………………...

22

Figure 2.7: Stainless steel 6-way-cross with different functions…………...

22

Figure 2.8: (a) Gold sample holder surrounded by Teflon parts. (b) Teflon part and three spring contact pins that allow device fixation and electric measurement……………………………………….

23

Figure 2.9: Data readout. (a) Flow chart of data acquisition process. The grey area specifies the steps executed by means of the MATLAB program. (b)The Keithley is controlled by MATLAB via RS-232 connection. The four sensors are connected to the Keithley using BNC connector and contact pins for contacting the sensor…………………………………………….

24

Figure 2.10: Diagram of gas mixing process before entering the gas chamber with maximum flow rates allowed by the MFCs. Based on literature………………………………………………..

25

VII

Figure 3.1: SEM images of 5 µl of rGO diluted dispersions deposited on Silicon wafers. (a,b: original dispersion, c,d: 75 % v/v, e,f: 50% v/v, g,h: 25% v/v)…………………………………………...

29

Figure 3.2: SEM images of 5 µl of B-CNTs dilutions deposited on Silicon wafers. (A: 50% v/v, B: 25 % v/v, C: 20% v/v, D: 10% v/v, E: 5% v/v and F: 2% v/v)……………………………………………

30

Figure 3.3:

Source-drain IV curves of bare devices used for the gas sensors based on pristine CNTs, B-CNTs, N-CNTs and rGO..

32

Figure 3.4: IV source-drain curves after nanomaterial deposition of gas sensing devices based on SC-CNTs (DS-1 and DS-2), Pristine CNTs (DP-1 to DP-3), B-CNTs (DB-1 to DB-6), N-CNTs (DN-1 to DN-6) and rGO (DR-1 to DR-3)……………….

34

Figure. 3.5: IV source-gate curves after nanomaterial deposition of gas sensing devices based on SC-CNTs (DS-1 and DS-2), Pristine CNTs (DP-1 to DP-3), B-CNTs (DB-1 to DB-6), N-CNTs (DN-1 to DN-6) and rGO (DR-1 to DR-3)………………..

35

Figure 3.6: SEM images of gas sensing devices based on SC-CNTs. (A-D device DS-1, E and F device DS-2). A higher number of small groups of SC-CNTs bridging the electrodes were observed in device DS-1 compared to device DS-2…………………………………………………………………….

38

Figure 3.7: SEM images of gas sensing devices based in Pristine-CNTs. (A and B: Device DP-1, C and D: Device DP-2, E and F device DP-3)………………………………………………………

39

Figure 3.8: SEM images of gas sensing device DB-1 based on B-CNTs. Big agglomerates of around 200 µm can be observed bridging the IDE……………………………………………………………..

40

Figure 3.9: SEM images of gas sensing device DB-2 based on B-CNTs. A big agglomerate of approximately 300 µm was present on the lower part of the IDE………………………………………….

40

Figure 3.10: SEM images of gas sensing device DB-3 based on B-CNTs… 41

Figure 3.11: SEM images of gas sensing device DB-4 based on B-CNTs. Two big agglomerates of around 500 µm were present on the IDE…………………………………………………………………

41

Figure 3.12: SEM images of gas sensing device DB-5 based on B-CNTs. Big size agglomerate was observed on the right side of the IDE. (C) and (D) Small groups of B-CNTs bridging the IDE…..

42

VIII

Figure 3.13: SEM images of gas sensing device DB-6 based on B-CNTs. (A) SEM image of contamination particle on the IDE. (C) SEM image after electrodes removal………………………………….

42

Figure 3.14: SEM images of gas sensing device DN-1 based on N-CNTs… 43


Figure 3.16: SEM images of gas sensing device DN-3 based on N-CNTs. (B) SEM image after unsuccessful electrodes removal……….

44




Figure 3.20: SEM images of gas sensing devices DR-1 (A,B), DR-2 (C,D), DR-3 (E,F) based on reduced graphene oxide………………..

46

Figure 3.21: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DS-1 and DS-2 based on SC-CNTs. (a) and (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). In addition, no recovery under pure N2 flow was present. (c) and (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration……………………………………………………..

50

Figure 3.22: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DP-2 and DP-3 based on Pristine-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). In addition, a slight and incomplete recovery is exhibited by DP-2 and not by DP-3 under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration……………………………………

53

Figure 3.23. Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DB-5 and DB-6 based on B-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). Moreover, an incomplete recovery is also exhibited by both sensors under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration…………………………………….

54

IX

Figure 3.24: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DN-5 and DN-6 based on B-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). Moreover, an incomplete recovery is also exhibited by both sensors under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration…………………………………….

55

Figure 3.25: Source-drain current (ISD) response of device DR-1 to different ammonia concentrations. Due to the noisy signal it is difficult to determine that changes in current (ISD) were due to interactions between NH3 and rGO…………………………..

56

X

List of Tables

Table 2.1: Ammonia concentrations and flow rates used in this study………………26

Table 2.2: Exposure times to different ammonia concentrations for each group of gas sensor sorted by material……………………………..…………………......27

Table 3.1: Sensing response to different ammonia concentrations of gas sensors based on SC-CNTs, Pristine CNTs, B-CNTs and N-CNTs………..……..48

Table 3.2: Gas sensing response of first and second ammonia exposure experiments of gas sensors based on SC-CNTs, Pristine CNTs, B-CNTs and N-CNTs…………………………………………………………………………...58

XI

Abbreviations

B-CNT Boron doped Carbon Nanotube

CNT Carbon Nanotube

DB Device based on B-CNTs

DI Deionized

DN Device based on N-CNTs

DWCNT Double-Walled Carbon Nanotube

DP Device based on P-CNTs

DS Device based on SC-CNTs

EPD Electrophoretic Deposition

FET Field Effect Transistor

GO Graphene Oxide

IDE Interdigitated Electrodes

IUPAC International Union of Pure and Applied Chemistry

LPG Liquefied Petroleum Gas

LVSEM Low Voltage Scanning Electron Microscopy

MWCNT Multi-Walled Carbon Nanotube

N-CNT Nitrogen doped Carbon Nanotube

NMP N-Methyl-2-Pyrrolidone

P-CNT Pristine Carbon Nanotube

rGO Reduced Graphene Oxide

SC-CNT Semiconducting Carbon Nanotube

SEM Scanning Electron Microscope

SiNW Silicon Nanowire

SWCNT Single-Walled Carbon Nanotube

VOC Volatile Organic Compounds

XPS X-ray Photoelectron Spectroscopy

1

1. Introduction

1.1 Motivation

In recent years, the development of highly efficient gas sensing technologies

has been the focus of modern research worldwide due to their application in

different areas. In industry, gas sensors are used to monitor volatile organic

compounds (VOCs) in food quality control1 and to prevent potential accidents due

to LPG2, methane3 or H2 leaks4; in environmental studies, to preserve air quality by

monitoring potential harmful gases like formaldehyde5 or CO2 6; and in biomedical

applications, for the diagnosis of specific diseases7.

A new non-invasive and potentially inexpensive route for diagnosis relies on the

analysis of exhaled breath samples to detect volatile organic compounds specific

for a certain disease8. This is a very young field of investigation, but with

outstanding long-term benefits. For instance, the possibility to perform early and

precise diagnosis of mortal cancers8–11 as well as to distinguish between primary

cancer and metastases, would increase the probability of patient survival as the

correct medical treatment can be administered on time. In addition, the health

professionals would have a reliable tool to monitor the therapy success and to

detect an early recurrence in an annual routine survey.11

This new approach is not exclusively applied for cancer diagnosis but also for other

diseases like liver cirrhosis, kidney failure12 and Helicobacter pylori infections13. All

of them can be identified by abnormal breath ammonia (NH3) levels. This particular

molecule does not only play a role as an indicator of infectious diseases but also

is the most common gas found in industrial processes and most importantly, it is

the most dangerous. Therefore, there is a special interest in ammonia detection in

all fields.

In order to achieve the extremely high sensitivity necessary for the applications

described above, researchers have focused their attention on the investigation of

nanomaterials as the active elements of gas sensors.

Due to particular nanoscale features like increased surface area to volume ratio,

nanomaterial-based sensors can potentially offer a high ratio of detection

2

sensitivity to cost, a considerably increased speed of response and most

importantly, they can operate with low power demands14. Several nanomaterials

like nanostructured metal oxides, metal particles, metal complexes, organic

polymers and carbon-based materials have been already described in the literature

as active elements of gas sensors.

On one hand, nanostructured metal oxide based gas sensors have demonstrated

efficient detection of low concentrations of CO and methane, however their high

operational temperatures (up to 200°C)15 and complicated or expensive fabrication

techniques often limit their application. On the other hand, sensors based on

organic polymers are cheap and very sensitive to several analytes like acetone or

methanol, nevertheless they have shown low stability regarding time and

temperature.16

Carbon nanomaterials have emerged as a promising alternative to solve these

drawbacks. In contrast to polycrystalline materials, quasi-one dimensional carbon

materials like CNTs avoid grain boundary poisoning which improves the long-term

stability of sensors, also their surface chemistry is, in principle, easier to

understand and therefore the sensing mechanisms can be studied. Moreover, their

high quality crystal lattices have shown high carrier mobility (ballistic charge

transport) and low noise, necessary to ensure good transduction properties.17 Even

more importantly, the possibility to functionalize carbon nanostructures extends the

possibilities to achieve high selectivity to specific target molecules according to the

application.

Recently, a new set up for testing nanomaterials-based gas sensors has been

developed at the Chair of Material Science and Nanotechnology and results from

sensors based on Si nanowires have been obtained, a sensing response of 0.14%

to 20 ppm of ammonia during 90 seconds of exposure was achieved. On the other

hand, different carbon based nanomaterials have been synthesized also at the

Chair and can be potentially used as active elements for highly efficient gas

sensors.

The aim of this thesis is to fabricate gas sensing devices based on reduced

graphene oxide (rGO), semiconducting carbon nanotubes (SC-CNTs), Boron-

doped carbon nanotubes (B-CNTs), Nitrogen-doped carbon nanotubes (N-CNTs)

3

as well as pristine carbon nanotubes (P-CNTs) and compare their performance

under different ammonia concentrations. The main tasks of this thesis include:

Elaboration of nanomaterial dispersions.

Fabrication of gas sensing devices.

Electrical and SEM characterization of the sensors.

Exposure of sensors to different concentrations of ammonia.

1.2 Gas Sensors

Different chemical substances exist in gaseous states as part of the surrounding

atmosphere, some of them are essential for life and some others are potential

health-treats at certain concentrations. Therefore, it is important to have a tool to

precisely measure different gas concentrations for several applications. This action

is accomplished by gas sensors.

The first registered attempt to detect hazardous gases was achieved in mining.

Methane, a colorless and odorless gas, is naturally produced during the conversion

of organics to coal and a potential health threat at high concentrations. Thus,

workers used a canary as a “gas sensor” to protect themselves from the mortal

methane effects at the mines. The method of detection was simple, the canaries

were brought inside the mines and if the methane concentration at the working

place reached dangerous levels, the bird would exhibit the toxic effects before they

became harmful to workers. Nowadays, due to the advances in technology,

detection of gases can be more precisely achieved.18

A gas sensor is a special type of chemical sensor. A chemical sensor is defined by

the IUPAC as a “device that transforms chemical information, ranging from the

concentration of a specific sample component to total composition analysis, into

an analytically useful signal”.19 They are composed of two basic units: a receptor

and a transducer. The receptor is the part where the chemical or physical

interactions with the gas molecules/analyte occur and the transducer is responsible

for transforming these interactions into a useful analytical signal (Fig. 1.1).

4

1Figure 1.1: Schematic representation of gas a gas sensor.

Gas sensors can be classified according to the operating principle of the transducer

in: Mass sensitive, magnetic, thermometric, optical, electrochemical and

electrical.19

1. Mass sensitive sensors transform the mass change (accumulation of analyte)

at a specially modified surface into a change of a property of the support

material. Two main types are included in this group: piezoelectric devices and

surface acoustic wave devices.

2. Magnetic devices are based on the change of paramagnetic properties of the

studied gas.

3. Thermometric devices are based on the heat effects resulting from the

interaction (adsorption or chemical reaction) of the gas molecules and the

receptor. Generally, these heat effects are measured using thermistors.

4. Optical sensors detect changes in the properties of the radiation resulting from

the interaction of electromagnetic waves and the analyte. This interactions are

possible due to the use of optical fibers in various configurations. The main

properties studied are absorbance, reflectance, luminescence, fluorescence,

refractive index and light scattering.

5. Electrochemical sensors detect the effect of the electrochemical interactions

between the receptor and the analyte leading to a signal generation from the

5

transducer, according to the measuring principle this signal can be a change in

electric current (amperometric devices), voltage (potentiometric or

voltammetric) or conductivity (conductometric devices).

6. Electrical sensors are based on surface interactions with the gas molecules that

modify the electrical properties of another material. A sub-classification of this

group can be made according to the material used: metal oxide

semiconductors, polymers, moisture absorbing materials and carbon

nanotubes. Same measuring principles as in electrochemical sensors are

applied for this type of devices.

1.2.1 Electrical Gas Sensors

1.2.1.1 Gas Sensors Based on Metal Oxide Semiconductors

Nowadays one of the most common sensing materials for different applications are

metal oxide semiconductors due to their high sensitivity. The sensing principle is

based on the oxidation states formed on the surfaces of the metal oxides which

interact with target gas molecules through redox reactions resulting in an electronic

variation of the oxide surface. This variation is then transduced into a measurable

electrical signal. 20

Many metal oxides have already been studied as active elements for gas sensors.

For example, tungsten trioxide (WO3) has shown good responses towards H2 and

NO but low for NH3. 21 In order to enhance sensitivity to ammonia Au and MoO3

were used as additives and good responses to 1 ppm were successfully achieved,

however the devices had to be operated at 400°C 22. Similarly, tin dioxide (SnO2),

the most widely used oxide metal semiconductor, has shown good sensitivity to

LPG, CH4, CO and other reducing gases23 but high operation temperatures were

also needed. Thus, the working temperatures of devices based on metal oxide

semiconductors range from 50 °C to 500 °C 15 and the detection limits from 1 to

1000 ppm 22. The demand for high temperatures requires more cost and

complicated configurations which restrict the application and development of this

type of sensors.

6

1.2.1.2 Gas Sensors Based on Polymers

Gas sensors based on polymers are most frequently used for detecting a range of

volatile organic compounds (VOC) or solvent vapors like alcohols, aromatic or

halogenated compounds. The working principle of polymer-based gas sensors is

based on interactions mainly by induced dipole/induce dipole interactions and

hydrogen bonds with the gas molecules on the surface on the polymer which

results in a change in its physical properties. According to these changes polymers

used for gas sensing can be classified in: conducting polymers and non-conducting

polymers.23

The most common conducting polymers used are polypyrrole (PPy), polyaniline

(Pani), polythiophene (PTh) and their derivatives24. However, the conductivity of

the polymers itself is not high enough for them to be used as gas sensors. Thus,

the polymers should be doped, generally by redox reactions, in different

proportions in order to increase their conductivity. On the other hand, non-

conductive polymers are mainly used for being coated onto different gas sensor

devices.

The main advantage compared to metal oxide based sensors is that they can

operate at room temperature. Nevertheless, they also exhibit some disadvantages

such as poor selectivity, irreversibility and long-time instability.23

1.2.1.3 Gas Sensors Based on Nanomaterials

Due to the disadvantages of the two main materials currently used for gas sensing

and discussed above, researchers have put more effort in discover new materials

that can enhance the gas sensitivity. One of these alternatives is the use of

nanomaterials. Due to nanoscale features, like high surface area to volume ratios,

high crystallinity and the possibility of being functionalized according to the

application, nanomaterial-based sensors can potentially offer a high ratio of

detection sensitivity to cost, a considerably increased speed of response,

miniaturized size sensors and most importantly they can operate with low power

demands.14 25

7

1.2.1.3.1 Carbon Nanomaterials-based Gas Sensors

In past years, a great interest has arisen in studying carbon nanomaterials in the

application of gas sensing. One of the main reasons is that they do not exhibit the

instabilities of other nanomaterials as a result of the very high activation barriers to

their structural arrangements. Consequently, they are highly stable even in their

unfunctionalized forms. In addition, they exhibit common organic chemistry even

though there is a wide range of possible carbon nanomaterials.17 Two of the most

common carbon nanomaterials for gas sensing technology are carbon nanotubes

(CNTs) and reduce graphene oxide (rGO).

1.2.1.3.1.1 Gas Sensors based on CNTs

Carbon nanotubes were discovered in 1991 26 and appeared to have extraordinary

electrical, mechanical, optical, thermal and chemical properties. Carbon nanotubes

are hollow cylinders of one or more layers of graphene known as single-wall

(SWCNTs) and multiwall (MWCNTs). Diameters of SWCNTs and MWCNTs are

typically 0.8 to 2 nm and 5 to 20 nm, respectively27. Despite structural similarity

with a single sheet of graphene, SWCNTs can be either metallic or semiconducting

depending on their diameter and chirality (how graphene sheets are rolled to form

CNTs) and even most important, CNTs have the largest surface to volume ratio

among all carbon nanomaterials28. This particular characteristic allows them to be

used as nanoscale electronic devices such as field effect transistors, single-

electron transistors and nanoscale p-n junctions.29

Moreover, it has also been shown that SWCNTs are promising materials for

chemical sensors30. Single-wall carbon nanotubes field effect transistors (SWCNT-

FETs) were first fabricated by Dr. Dekker31 and Dr. Avouris32 back in 1998 and the

group of Dr. Liming Dai33 was the first to demonstrate that the conductivity of

semiconducting single-wall carbon nanotubes change rapidly when exposed to

nitrogen dioxide and ammonia, therefore, acting as a sensitive chemical sensors

at ambient temperature. Since then, several studies have exhibited the high

sensitivity of carbon nanotubes to different gases like NH3, NO2, H2, CH4, CO, SO2,

H2S, and O2.34 Moreover, compared to metal oxide semiconductors which require

microfabrication techniques, power supply and specific electronics when utilized

8

as sensing material, CNTs possess good corrosion resistance and a better

bandwidth.23

Although there are different configurations for CNTs based gas sensors like

chemicapacitive and field-effect transistor (FET), a chemiresistor approach, which

is based on a resistance change as output, is the most common type of

configuration of a gas sensor array because it can be manufactured easily and with

low cost, as well as having many applications.35 (Fig. 1.2) The working principle is

based on the interactions between gas molecules and CNTs which leads to a

decrease or increase in conductivity/resistance depending on the nature of the gas

molecules which can either be charge donors or acceptors to the nanotubes.

Pristine CNTs exhibit a p-type behavior meaning that the majority of charge carriers

are holes, thus when CNTs based sensors are exposed to electron-withdrawing

gases the electrical resistance decreases because the Fermi levels are shifted to

the valance band generating more holes. On the other hand, when an electron-

donating gas is involved the number of holes decreases and the resistance

increases.36

Figure 1.2: Schematic representation of resistivity-based SWCNTs gas sensor. 37

A big advantage of CNTs as a sensitive material for gas sensing is the possibility

of chemical functionalization to cover different applications. Metal particles like Pt

can be incorporated to increase CNTs sensitivity to hydrogen38, also

oligonucleotides (DNA and RNA) can be integrated to increase the response to

some specific analytes39. Moreover, a quite new approach is the substitutional

functionalization of C atoms for impurity atoms like boron (B) or nitrogen (N). Both

are mainly used for this type of functionalization due to important factors: Firstly,

9

they are neighbors of carbon in the periodic table which means that both have

atomic radii similar to carbon. Second, B-CNTs and N-CNTs can either be p-type

or n-type CNTs, similar to the normal semiconducting materials. Third, doping

atoms produce chemically active sites on the CNTs walls which can enhance the

binding energy and so the sensitivity to certain gas molecules.40 For N doping,

taking into account that nitrogen has one extra electron compared to carbon, it can

be inferred that the N-CNTs would show an n-type behavior. However, due to its

size, nitrogen can also generate defects in the curved nanotube structure resulting

in a rearrangement of atoms and an n-type behavior cannot be directly assumed.

It has been reported that N-doping creates pyridinic configurations on the surface

of CNTs which would lead to a better interaction environments for certain gas

molecules.41

1.2.1.3.1.2 Gas Sensors based on rGO

Another carbon material utilized in the fabrication of gas sensors is reduced

graphene oxide (rGO). Its study as active material for gas sensing technology is

derived from the relatively weak interaction between intrinsic graphene and most

molecules. Researchers found that the introduction of chemically active defects

could improve these interactions. Thus, graphene oxide (GO) and reduced

graphene oxide (rGO) emerged as derived materials from graphene but with

enhanced sensing properties. In contrast to GO, rGO is highly conductive42,

resulting in sufficient signal-to-noise ratio to detect low concentrations of target

gases. Its sensitivity to high concentrations (100ppm) of NH3 and NO2 has been

already reported43,44. However its response to very low concentrations necessary

for biomedical applications has not yet been described.

10

1.3 Gas Sensing Properties

In order to characterize the sensor performance for its further application in several

fields, a group of parameters are used. The most important are listed below: 45

Sensitivity is a change in measured signal per analyte concentration unit.

Sometimes confused with the detection limit.

Selectivity refers to characteristics that determine wheatear a sensor can

respond selectively to a group of analytes or even specifically to a single

analyte.

Stability is the ability of a sensor to provide reproducible results for a certain

period of time. This includes retaining the sensitivity, selectivity, response and

recovery time.

Detection limit is the lowest concentration of the analyte that can be detected

by the sensor under a given conditions, particularly at a given temperature.

Dynamic range is the analyte concentration range between the detection limit

and the highest limiting concentration.

Linearity is the relative deviation of an experimentally determined calibration

graph from an ideal straight line.

Resolution is the lowest concentration difference that can be distinguished by

the sensor.

Response time is the time required for a sensor to respond to a step

concentration change from zero to a certain concentration value.

Working temperature is usually the temperature that correspond to maximum

sensitivity.

Life cycle is the period of time over which the sensor will continuously operate.

All parameters are used to characterize the properties and performance of gas

sensors of a particular material. An ideal sensor would achieve high sensitivity,

dynamic range, selectivity and stability, low detection limit, good linearity, short

response time and a long life cycle. However, investigations usually focus only on

achieving some of these characteristics due to the fact that not all of them are

required for a specific application.45

11

1.4 Applications of Ammonia Detection

Ammonia is a natural gas that is present throughout the entire atmosphere but can

potentially be hazardous at certain concentrations. Therefore, it is important to be

able to precisely detect its presence for different applications. According to

literature there are four major areas of interest for measuring ammonia:46

1.4.1 Medical Applications

As previously described, high concentrations of ammonia are a threat to human

health. Human perception to ammonia is around 50 ppm, however even lower

levels can irritate the respiratory system, skin and eyes.47 Therefore the long term

working conditions have been set to 20 ppm. Exposures to 500 ppm produce sever

irritation of nose and throat, 1000 ppm or more cause pulmonary oedema and

extremely high concentrations, 5000 to 10000 ppm, are lethal within 5 or 10

minutes.46 Therefore, monitoring the presence of ammonia at low concentrations

is important to generate safe working conditions.

Ammonia is naturally produced in the human body and its concentration depends

on several factors. Based on this, researches have focused their attention on the

development of ammonia sensors that can precisely measure ammonia levels in

the human breath in order to identified certain diseases.48 Liver cirrhosis, kidney

failure12 and Helicobacter pylori infections12 could be detected using this approach.

In the particular case of liver cirrhosis ammonia concentration of 0.745 ppm was

correlated to cirrhotic patients and 0.997 ppm to patients with hyperammonemia.

On the other hand, ammonia levels on blood are also relevant for sports medicine.

During physical activity the ammonia levels in breath can be correlated to the work

load. For this particular application a detection range from 0.1 to 10 ppm is

needed.47

12

1.4.2 Environmental Gas Analysis

The levels of ammonia near agricultural areas are much higher than usual, around

10 ppm,49 and the accumulation in confined places like stables can reach even

more dangerous concentrations. Thus, the actual application determines the

concentration levels of interest and the response times. For environmental

analysis, extremely high sensitivities and fast detectors are not really needed. On

the other hand, for confined places like stables shorter response times are

required.

1.4.3 Automotive Industry

In the automotive industry there is a high concern about monitoring the levels of

contaminants emitted to the environment. For instance, the ammonia emissions

have been measured up to 8 ppm in exhaust gases.50 Moreover, the air quality in

the passenger’s compartment is also an important issue for this industry. Modern

cars are equipped with air conditioning systems that can take air from the outside

in order to control the temperature and humidity levels in the cabin. When the air

quality from outside decreases, the system should be able to detect this changes

and stop the air flow, therefore a response time in the range of seconds is needed.

For indoor air quality monitors, the detection limit for ammonia should be around

20 ppm.46

1.4.4 Chemical Industry

Most of the ammonia produced nowadays is used for fertilizers in the agricultural

sector, in chemical processes and in refrigeration systems. Since all these

applications use mainly pure ammonia, a leak in the systems can derive in risk

situations. Thus, the applications dealing directly with ammonia should be

equipped with alarms sensitive to 20 ppm and exhibit response times in the order

of minutes.46

13

2. Methods

2.1 Materials used for Gas Sensing Device Fabrication

2.1.1 Fabrication of Interdigitated Metallic Electrodes

The fabrication of interdigitated electrodes (IDE) was performed by Dr. Merhdad

Shaygan at the Chair of Material Science and Nanotechnology. IDEs with a finger

width of 5 µm and gap size of 3 µm and 4 µm were built on p-type Si substrates

with a layer of 300 nm of SiO2 using photolithography followed by deposition of 15

nm of chromium and 100 nm of gold via thermal evaporation and lift off process.

The final chips had a total area of 2 cm2 with two contact pads of 18 mm2 and a

comb-like structure area of 1 mm2 for nanomaterial deposition51 (Fig. 2.1).

a) b)

2Figure 2.1: (a) Gas sensing bare device dimensions. Red circle: Interdigitated area for nanomaterial deposition. (b) SEM image of interdigitated area.

2.1.2 Nanomaterial Synthesis

The nanomaterial synthesis described in the following sections was performed by

Dr. Vyacheslav Khavrus and M.Sc. Ye Liu at the Chair of Material Science and

Nanotechnology, TU Dresden.

14

2.1.2.1 Pristine Carbon Nanotubes

Pristine carbon nanotubes were purchased from OCSiAl Company (TUBALL, Lot-

Nr. 47-14112014). The material is a mixture of SWCNTs and DWCNTs nanotubes

with an outer mean diameter between 1.4 nm to 2.2 nm and a minimum length of

5 µm containing at least 75% of SWCNTs according to producer specifications.

2.1.2.2 Boron doped single wall carbon nanotubes

B-SWCNTs were synthesized using a substitution reaction described elsewhere52.

Pristine TUBALL tubes were mixed with B2O3 powder with weight ratio of 1:5 and

then heated to 1250 °C for 2 hours in a high temperature oven with flowing argon

at atmospheric pressure. After the cooling down process, the product was

dispersed, washed and separated three times using excess boiling deionized water

to remove residual B2O3. Finally, the obtained material was freeze-dried in order to

avoid agglomerations and to keep the spacious structure of doped material for its

further efficient dispersion. The XPS analysis showed 2.2-3% of boron content in

the obtained B-CNTs samples.

2.1.2.3 Nitrogen doped Single Wall Carbon Nanotubes

Pristine SWCNTs were oxidized by refluxing in concentrated HNO3 (ca. 67%) at

120 °C during 48 hours. Then, they were washed using excess of DI water until

neutral pH. The nitrogen doping was performed by hydrothermal treatment

described elsewhere53 during 10 hours at 140°C and a ratio between ox-SWCNTs

and (NH4)2CO3 of 1:50. After cooling down, the product was washed using DI water

to remove (NH4)2CO3 residues. Finally, the obtained material was freeze-dried in

order to avoid agglomerations and to keep the spacious structure of doped material

for its further efficient dispersion. The XPS analysis showed ~2.2% of nitrogen

content in the obtained N-CNTs samples.

2.1.2.4 Reduced Graphene Oxide

First, synthesis of graphene oxide flakes was performed based on the procedure

described elsewhere54 but using double the amount of pristine materials. Then, the

reduction of obtained GO was achieved by the use of Al powder as a reducting

agent and following the protocol described in the literature.55 The XPS analysis

showed 8% of oxygen content in the obtained rGO samples.

15

2.1.3 Nanomaterial Dispersions

Nanomaterial dispersions were produced according to the protocol developed by

colleagues at the Chair of Nanotechnology and Material Science. The same

protocol was applied to obtain reduced graphene oxide, B-CNTs, N-CNTs and

pristine CNTs dispersions.

First, 10 mg of nanomaterial was weighted using an analytical balance and then

mixed with 10 ml of N-methyl 2 pyrrolidone (NMP, Sigma Aldrich) under the fume

hood. After, the mixture was sonicated using a tip sonicator (Sonoplus, Bandelin)

for 2 hours applying 30% of the total power inside an ice bath in order to avoid

overheating of the dispersion. The ice bath was renewed every 30 minutes. After

sonication, the dispersion was immediately centrifuged for 2 hours at 14000 rpm

(Eppendorf, 5417R) to remove bundles and agglomerates.

Finally, the supernatant was collected into 15 ml glass bottles. The dispersions

were labeled and stored at room temperature for their further use. The expected

dispersion concentration was 1 mg/ml for each nanomaterial before centrifugation.

2.1.3.1 Dispersion Dilutions and Deposition Experiment

The nanomaterial dispersions with high concentrations obtained as described in

the last section were not suitable for further gas sensing device fabrication. As

previously reported56, the device sensing properties are closely related to the

quality of the thin nanomaterial layer between the electrodes. Therefore, it was

necessary to determine the optimal dispersion concentration to achieve the best

nanomaterial deposition. For this, diluted dispersions were investigated by means

of drop-deposition experiments and SEM analysis.

Since the precise dispersion concentration is unknown after the centrifugation step,

the diluted dispersions are defined in terms of volume percent concentration (v/v

%) using the following formula:

𝑣

𝑣% = (

𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑑𝑖𝑠𝑝𝑒𝑟𝑠𝑖𝑜𝑛

𝑇𝑜𝑡𝑎𝑙 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛) ∗ 100 (1)

16

Dispersion dilutions in NMP of 75%, 50%, 25 % and 50%, 25% 20% 10%, 5%, 2%

v/v were prepared from the obtained rGO and B-CNTs dispersions, respectively.

The Si wafers of 0.8 cm x 0.8 cm were cleaned by bath sonication in acetone for 5

minutes, in isopropanol for 5 minutes and finally blow dried with a nitrogen gun.

Then, 5 µl of each diluted dispersion were drop-casted on the silicon wafers and

heated to a final temperature of 100 °C for 10 minutes. After NMP evaporation, the

wafers were cooled down at room temperature and mounted in sample holders for

further SEM analysis. (Section 2.3.2)

2.2 Gas Sensing Device Fabrication

In this work, 20 gas sensing devices were fabricated and labeled as follows: 2

based on semiconducting CNTs (DS-1 and DS-2), 3 on Pristine CNTs (DP-1 to

DP-3), 6 on B-CNTs (DB-1 to DB-6), 6 on N-CNTs (DN-1 to DN-6) and 3 on rGO

(DR-1 to DR-3).

The fabrication of gas sensors based on pristine CNTs, N-doped CNTs, B-doped

CNTs and rGO was achieved in two stages. First, bare devices with interdigitated

metallic electrodes were cleaned following a protocol based on previous reports

from colleagues at the Chair of Material Science and Nanotechnology. Second, the

corresponding nanomaterial was drop casted on the surface of the bare devices

for further electrical and SEM characterization. Drop casting technique for

nanomaterial deposition was chosen to achieve local material deposition in the

area of the IDE.

The gas sensing devices based on semiconducting CNTs were fabricated by Dr.

Khavrus and Dr. Shaygan. First, silicon wafers with a SiO2 layer of 300 nm were

drop-coated with a NMP dispersion of sorted–out semiconducting SWCNTs

(Sigma Aldrich, 98% semiconducting SWCNTs). Then, the wafers were subjected

to a lithography process and metal deposition to obtain gas sensing devices with

the same configuration as previously described (Section 2.1.1).

17

2.2.1 Cleaning Process

Bare devices described in section 2.1.1 were cleaned right before nanomaterial

deposition in order to remove polar and non-polar material that could potentially

affect further electrical measurement. The devices were first rinsed with acetone

and left in an acetone bath for 5 minutes. Then, the same process was applied

using isopropanol. Bath sonication was not included in this process because it was

found that it damages the electrodes microstructure. Finally the devices were

carefully blow dried with a nitrogen gun.

2.2.2 Drop Casting Process

Immediately after cleaning, the deposition of nanomaterial on bare devices was

accomplished by a drop casting method applying the following protocol:

1. The device was heated to 100°C for 5 minutes to remove residual solvents and

then cooled down again to room temperature.

2. The nanomaterial dispersions were sonicated in a bath sonicator for 5 minutes

and 100 μl dispersion dilutions of 2 % v/v for pristine CNTs, B-CNTs, N-CNTs

and 50 % v/v for rGO were prepared.

3. 3 μl of the corresponding dilution were directly deposited on the interdigitated

electrode area of the device using a P10 pipette.

4. The device was heated to 50 °C for 5 minutes and then to 100 °C for 10 minutes

on a hot plate to evaporate NMP under a fume hood.

5. Finally, the device was cooled down to room temperature and subjected to

further characterization.

18

a) b)

c) d)

Figure 2.2. Schematic representations of gas sensing devices with carbon based nanomaterial bridging the electrodes of the interdigitated area: (a) Sensors based on pristine CNTs, B-CNTs, N-CNTs and rGO. (b) Sensors based on SC-CNTs. (c) Illustration of the sensor layout with contact pads. (d) Microstructure of an IDE array with deposited carbon-based nanomaterial.

2.3 Gas Sensor Characterization

2.3.1 Electrical Characterization

Electrical characterization was carried out using an established probe station

setup. It is composed of an optical microscope powered by a highlight 3100

(Olympus Europe), two micro-positioners with tungsten needles, an electrically

contactable sample holder (chuck), a vacuum pump, a measuring system Keithley

2604B SourceMeter and a computer running a custom made program

implemented in MATLAB. The tungsten needles were used for applying and

measuring source-drain voltage and current, whereas the chuck was used for gate

voltage and gate current data acquisition (Fig. 2.3).

19

a) b)

Figure 2.3: (a) Optical microscope image and (b) camera picture of the tungsten needles contacting the device during electrical characterization.

The electrical characterizations of devices was performed in two stages; before

and after drop casting process. Source-drain IV curves were obtained applying a

source-drain voltage (VSD) from -10 V to 10 V for bare devices and from -2 V to 2

V after nanomaterial deposition. (Fig. 2.4, a)

Even though the gas sensors can only be used as chemiresistors and not as FET

within the gas chamber, the transfer characteristics were also investigated to

identify current leakage through the back gate. The transfer characteristics were

obtained by ranging the back gate voltage (VG) from -10 V to 10 V and keeping a

constant source-drain voltage (VSD) of 0.5 V. (Fig. 2.4, b)

Figure 2.4. Diagrams for measuring source-drain IV curves (a) and transfer characteristics (b) of gas sensors.

20

2.3.2 SEM Characterization

Scanning electron microscopy was used for investigating the quality of the

nanomaterial films deposited to the gas sensors interdigitated area. A spot size of

3, working distances around 6 mm and different magnifications were the

parameters applied for this purpose in a FEI XL30 ESEM equipped with a field

emission cathode.

In the case of SWCNTs, due to their small diameters (1-2nm) and the SEM

resolution limit of ~ 5 nm, CNTs were not easily observed under the scanning

electron microscope. However, as previously reported57 low voltage scanning

electron microscopy (LVSEM) can help to improve the CNTs imaging. Therefore,

1kV was used for characterization of devices based on Pristine CNTs, B-doped

CNTs, N-doped CNTs and semiconducting CNTs.

2.4 Ammonia Exposure Experiment

2.4.1 Gas Chamber Description

The information presented in this subsection was taken from Dr.-Ing Cindy

Schmädicke doctoral Thesis18 where a more detailed description of the apparatus

can be found.

The apparatus comprises 4 different systems: the gas delivery system, the gas

exposure chamber, the read-out electronics and the temperature control. All is

controlled by a personal computer using integrated programs with graphical user

interfaces (Fig. 2.5). In this work, all measurements were carried out at room

temperature, thus the temperature control system is not discussed.

21

Figure 2.5. Schematic configuration of the gas flow apparatus.18

2.4.1.1 The Gas Delivery System

This system controls the gas flow concentration inside the chamber. It is composed

of two individual channels connected to the pressure reduced gas cylinders

(NH3/N2) or to the pure nitrogen supply. The flow rates are accurately controlled by

SLA5850 (Brooks Smart DDE) mass flow controllers (MFC) operated by a

computer software (Brooks Smart DDE) and a specially developed Microsoft Excel

user interface. The maximum flow rate of the MFC for N2 and NH3/N2 is 2 L/min

and 0.02 L/min, respectively. Both flows mix right after the MFCs and enter the

measurement chamber with a known NH3 concentration. (Fig. 2.5)

2.4.1.2 The Gas Exposure Chamber

The gas chamber was fabricated of stainless steel which is inert to many chemical

compounds and has a small volume that ensures short gas exchange times and

highly laminar flow. This allows the reliable reproducibility of exposure

experiments. The core of the measurement system consist of a 6-way-cross of

stainless steel where the devices are located. The complete apparatus is attached

to a mounting profile (ITEM industrie-technik GmbH) and hermetically sealed using

vacuum components because used gases are potentially harmful for the user. Two

stainless steel pipes with a diameter of 6 mm are used for gas inlet and outlet to

suppress a back diffusion of gases. (Fig. 2.6)

22

Figure 2.6. CAD model of the gas measurement chamber.18

The six ports of the 6-way-cross have different functions: 2 viewports (front and

back) made out of borosilicate glass for adjusting the contact pins during device

installation and 2 electrical feedthroughs: A Bayonet Neil-Concelman (BNC)

connected to the measuring instrument and a 9 pin SUB-D feedthrough connected

to the controller box, thus the operating voltage and the signal for the sensor’s

control are provided. (Fig. 2.7)

Figure 2.7. Stainless steel 6-way-cross with different functions.18

Within this core element a thermal controlled sample holder composed of a gold

coated cooper block (10x10x20 mm) is located with space for 4 different devices.

Surrounding the cooper block, four parts of Teflon/Macor ceramics, an efficient

heat and electricity insulator, are fixed with sleeve nuts. The distance between the

chips and the ceramics is 1mm. Three spring contact pins per chip (high

23

temperature resistant) allow the contacting of sensors for resistance signal

measurements and the chip fixation. (Fig. 2.8)

a) b)

Figure 2.8. (a) Gold sample holder surrounded by Teflon parts. (b) Teflon part and three spring contact pins that allow device fixation and electric measurement.

The multiplexer electronics is located inside the lower part of the chamber, it

consists of four relays responsible for switching between the chips. The devices

are connected to the control electronics by spring contact pins as can be seen in

figure 2.8, (a). So, no cohesive connection takes place and the sensors are

reusable. The stainless steel pipe is located in the middle of the control electronics

leading the gas mixture to the devices bounded by the Macor ceramics through

four different holes.

2.4.1.3 Control and Read-Out Electronics

In this particular apparatus, the gas sensing properties of the devices can only be

studied as chemiresistors and not as FETs. The sensors’ resistivity is determined

using a Keithley 2602 System SourceMeter connected to the chamber through the

right port by a BNC feedthrough and to the computer via a RS-232 interface. The

readout is controlled by a custom made program implemented in MATLAB (The

MathWorks). The electronic layout ensures the quiasi-simultaneously time-

resolved determination of the resistance of four gas sensing devices independently

of each other. For this, a voltage range between 100nV and 40 V can be applied

to the devices and adjusted to ideal parameters for different nanomaterials.

24

The four devices can be exposed to the gas of interest simultaneously, however

only one value of current can be read out at a time. Therefore, the control box is

responsible for switching between the sensors signal and allows the user to select

the correct value. The switch between the sensors is limited by the relay switching

time of 10 ms. thus the four sensors can be measured per second, which was

enough for this work purposes. Figure 2.9 depicts the reading out process of

different sensor responses and the data acquisition.

Figure 2.9. Data readout. (a) Flow chart of data acquisition process. The grey area specifies the steps executed by means of the MATLAB program. (b)The Keithley is controlled by MATLAB via RS-232 connection. The four sensors are connected to the Keithley using BNC connector and contact pins for contacting the sensors.18

25

2.4.2 Determination of Ammonia Concentration

Before ammonia exposure experiments, a relation between the ammonia

concentration and MFCs’ flow rates had to be established in order to accurately

vary the exposure parameters.

Figure 2.10. Diagram of gas mixing process before entering the gas chamber with maximum flow rates allowed by the MFCs. Based on literature18.

As can be seen in figure 2.10, the total flow (Q Total) is the sum of diluted NH3 and

pure N2 flows (Q test + Q carrier) with a final ammonia concentration (C Total). In this

work, the flow rate of carrier gas was kept constant to the maximum flow rate

allowed by the MFC (2 L/min). Thus, the ammonia concentration of the total flow

was only dependent on the diluted ammonia flow rate. This dependency was

calculated using the following formula:58

𝐶𝑡𝑜𝑡𝑎𝑙 = 𝐶𝑡𝑒𝑠𝑡 𝑄𝑡𝑒𝑠𝑡

𝑄𝑡𝑜𝑡𝑎𝑙 (2)

Where C Test is the volumetric concentration of ammonia in N2, which in this case

was 0.5 % (Air Liquid).

In addition, the minimum and maximum ammonia concentrations of the total flow

were calculated using Eq. (2) and the lowest (0.6 ml/min) and highest (20 ml/min)

flow rates allowed by the MFC according to manufacturer. This was done in order

to select suitable parameters for the device exposure experiments. The maximum

and minimum concentrations technically available were 50 ppm and 1.5 ppm,

respectively.

26

2.4.3 Resistance Response under Ammonia Exposure

After electrical and SEM characterization, the 20 devices based on pristine CNTs,

semiconducting CNTs, N-doped CNTs, B-doped CNTs and rGO were tested, one

at the time, under ammonia exposure in a chemiresistor configuration to study their

gas sensing response. For this purpose, ammonia concentrations of 1.5, 2.5, 5, 10

and 20 ppm were selected. The concentration in relation to diluted ammonia flow

rates used for this experiment are summarized in table 2.1. All experiments were

carried out at room temperature.

Table 2.1: Ammonia concentrations and flow rates used in this study.

Before exposure experiments, the MFC responsible for the NH3 flow was purged

with pure N2 for 20 minutes to avoid contamination. The devices were gently

cleaned using a nitrogen gun to remove dust particles and then carefully placed

inside the gas chamber. The chamber was tightly sealed in order to avoid gas

leakage and a constant flow of 2 L/min of N2 was established.

A voltage of 0.1 V was applied between source and drain electrodes under

constant pure N2 flow and the source-drain current signal was monitored per

second. The constant flow inevitably modifies the gas sensor environmental

temperature, therefore the source-drain current signal was allowed to stabilize for

at least one hour before ammonia exposure experiments.

Different exposure times were selected for each nanomaterial-based sensor

according to their response in preliminary experiments. The ammonia exposures

were performed continuously and separated by 15 minutes of recovery time in pure

Ammonia Concentration

(ppm)

Flow rate (ml/min)

1.5 0.6

2.5 1

5 2

10 4

20 8

27

N2 flow. The exposure times for each type of gas sensor are summarized in Table

2.2.

Table 2.2: Exposure times to different ammonia concentrations for each group of gas sensor sorted by material.

Material Exposure time (min)

1.5 ppm 2.5 ppm 5 ppm 10 ppm 20 ppm

SC-CNTs 10 15 5 5 5

Pristine CNTs 15 15 15 5 5

B-CNTs 10 5 5 5 5

N-CNTs 10 5 5 5 5

rGO 15 15 15 15 15

In addition, the repeatability and stability of the sensors were also investigated. For

this, the devices were stored under controlled conditions of temperature and

humidity for 3 weeks after their first ammonia exposure. Then, a second exposure

experiment with the same parameters was performed.

The normalized sensing response was defined as the relative resistance change

and was calculated using the following formula59:

𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 (%) =∆𝑅(𝑡)

𝑅𝑜=

𝐼𝑜−𝐼(𝑡)

𝐼(𝑡) 𝑥 100 (3)

Where ΔR(t) is the difference in resistance before and during NH3 exposure, Ro and

I0 are the values of resistance and current before NH3 exposure and I(t) the current

during ammonia exposure.

28

3. Results and Discussion

3.1 Dispersion Dilutions and Deposition Experiments

The aim of this experiment was to determine the ideal nanomaterial dispersion

dilution for gas sensing device fabrication by means of SEM analysis. The dilutions

were prepared as described in section 2.1.3.1.

The first approach taken in this work was based on increasing the temperature to

the boiling point of NMP (200°C). Thus, the dispersion droplet would immediately

evaporate leaving a uniform layer of nanomaterial. However, it was observed that

the droplet did not dry instantly but hovered around the surface instead. This result

can be explained by the Leidenfrost effect which states that an insulating vapor film

is formed between the droplet and the hot substrate when the temperature is near

the boiling point of the liquid, keeping it from instantly evaporating.60 Therefore, a

different method was investigated.

A fixed temperature of 100 °C was established to allow a gradual evaporation

process of the 5 µl dispersion dilution during 10 minutes. The results are depicted

in figures 3.1 and 3.2.

29

Figure 3.1: SEM images of 5 µl of rGO diluted dispersions deposited on Silicon wafers. (a,b: original dispersion, c,d: 75 % v/v, e,f: 50% v/v, g,h: 25% v/v )

30

Figure 3.2: SEM images of 5 µl of B-CNTs dilutions deposited on Silicon wafers. (A: 50% v/v, B: 25 % v/v, C: 20% v/v, D: 10% v/v, E: 5% v/v and F: 2% v/v)

In all experiments, reduced graphene oxide and CNTs, the so called “coffee-stain

effect“ was present (Fig. 3.1, a, c, e, g and Fig. 3.2, A,C,E). This phenomena is

caused by the different evaporation rates between the edge and the center of the

dispersion droplet, since the volume at the center evaporates slower than at the

edge a concentration gradient is induced. This gradient produces a convective flow

that carries the dispersed nanomaterial from the center to the edge of the droplet

and therefore ring-like patterns are formed.61

31

In the case of reduce graphene oxide, highly concentrated ring-like structures from

the original dispersion and 75% v/v dilutions can be observed in contrast to the

25% dilution. However, in both cases most of the nanomaterial was either

deposited in these circular arrangements or in small agglomerations (Fig. 3.1 a, c,

g). On the other hand, results from the 50% v/v dilution showed less concentrated

ring-like patterns and a uniform layer of nanomaterial of approximately 0.25 µ2 was

observed (Fig. 3.1 e, f).

Regarding B-CNTs, highly dense agglomerations were observed from 50%, 25%,

20% and 10% v/v dilutions. Moreover, no individual or small groups of B-CNTs

could be imaged (Fig. 3.2 A, B, C, D). In contrast, 5% and 2% v/v dilutions

presented fewer and less dense nanomaterial agglomerations, thus small BCNTs

bundles and individual BCNTs were imaged using a voltage of 1kV (Fig. 3.2, E and

F).

According to these results, a 50% v/v dilution was selected for the fabrication of

gas sensor based on rGO and a 2% v/v dilution for the fabrication of pristine CNTs,

B-CNTs and N-CNTs.

32

3.2 Gas Sensors Characterization

3.2.1 Electrical Characterization

Electrical characterization of bare devices was necessary to ensure that the

electrical properties of gas sensors after nanomaterial deposition were defined by

the nanomaterial bridging the electrodes and not coming from contamination or

damaged metal contacts.

Figure 3.3: Source-drain IV curves of bare devices used for the gas sensors based on pristine CNTs, B-CNTs, N-CNTs and rGO.

Theoretically, if the device fabrication was successfully performed, no material

should be present between the IDE and therefore no current should be generated

during electrical characterization of bare devices. However, as can be seen in

figure 3.3, a constant curve shape in the nanoamper range is shown by all bare

devices used in this work. This behavior was previously investigated and the

presence of this considerable low current was attributed to a capacitor effect of the

circuit itself using alternating voltage. Therefore, the slower the voltage was

changed the lower the current was measured. Moreover, punctual measurements

were performed applying a fixed voltage of 0.1 V and current values in the range

of pico amperes (pA) were obtained, which is in the range of noise for this particular

probe station setup, confirming the absences of any material connecting the

electrodes of the interdigitated area.

For characterization purposes a device that exhibited the type of curve depicted in

figure 3.3 was considered as appropriate for gas sensing device fabrication.

-10 -8 -6 -4 -2 0 2 4 6 8 100

2

4

6

8

So

urc

e-d

rain

cu

rre

nt

I SD (

nA

)

Source-drain voltage VSD

(V)

rGO

B-CNTs

N-CNTs

Pristine CNTs

-10 -8 -6 -4 -2 0 2 4 6 8 10

33

-2 -1 0 1 2-10.0

-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DS-1

-2 -1 0 1 2-0.50

-0.25

0.00

0.25

0.50

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DS-2

-2 -1 0 1 2-200

-100

0

100

200

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DP-1

-2 -1 0 1 2-15

-10

-5

0

5

10

15

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DP-2

DP-3

-2 -1 0 1 2

-300

-200

-100

0

100

200

300

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DB-1

DB-2

DB-3

DB-4

DB-5

-2 -1 0 1 2-15

-10

-5

0

5

10

15

DB-6

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

-2 -1 0 1 2

-400

-200

0

200

400

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DN-1

DN-2

DN-3

DN-4

-2 -1 0 1 2

-20

-10

0

10

20

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DN-5

DN-6

34

Figure 3.4: IV source-drain curves after nanomaterial deposition of gas sensing devices based on SC-CNTs (DS-1 and DS-2), Pristine CNTs (DP-1 to DP-3), B-CNTs (DB-1 to DB-6), N-CNTs (DN-1 to DN-6) and rGO (DR-1 to DR-3). Note that different ranges of ISD were used in IV-curves.

After nanomaterial deposition the conductivity of devices based on the same type

of CNTs or rGO was not reproducible even though they were fabricated following

the same protocol and applying the same dispersion concentration. Some of them

differ in up to one order of magnitude in source-drain current values (Fig. 3.4).

Similar results can be found in the literature where low CNTs dispersion

concentrations were also used for gas sensor fabrication. The authors state that

the yield and reproducibility, measured by the consistency of sensor performance

from devices to devices, would decrease with decreased dispersion concentrations

or increased microelectrode spacing.62 This statement deals also with the gas

sensing properties of the devices that will be discussed in section 3.4.

The main reason for the difference in electrical properties is possibly the variation

in the amount of nanomaterial deposited on each device and the quality of its

distribution along the IDE. This disparity could be the consequence of two main

events in the gas sensor fabrication process: First, the particular drying mechanism

for each device during the drop casting process as described in section 3.1.

-2 -1 0 1 2

-0.10

-0.05

0.00

0.05

0.10

So

urc

e-d

rain

cu

rren

t I S

D (

mA

)


(V)

DR-1

DR-2

DR-3

35

Fig. 3.5: IV source-gate curves after

nanomaterial deposition of gas sensing

devices based on SC-CNTs (DS-1 and

DS-2), Pristine CNTs (DP-1 to DP-3), B-

CNTs (DB-1 to DB-6), N-CNTs (DN-1 to

DN-6) and rGO (DR-1 to DR-3).

And secondly, the long term stability of nanomaterial dispersions. Thus, the greater

the amount of deposited nanomaterial the higher the sensor’s conductivity. This

statement is further discussed in section 3.2.2.

Since the device configuration for ammonia detection investigated in this work was

as a chemiresistor and not as a FET, the transfer characteristics of gas sensors

are not discussed. Nevertheless, it was important to ensure a constant current flow

through the source and drain electrodes in order to accurately study the gas

-10 -5 0 5 10-20

-10

0

10

20

VSD

= 0.5 V

So

urc

e-g

ate

cu

rre

nt

I G (

nA

)

Source-gate voltage VG (V)

DS-1

DS-2

-10 -5 0 5 10-1

0

1

2

3

4

VSD

= 0.5 V

So

urc

e-g

ate

cu

rre

nt

I SD (

nA

)

Source-gate voltage VG

(V)

DP-1

DP-2

DP-3

-10 -5 0 5 10-1

0

1

2

3

4

VSD

= 0.5 VSo

urc

e-g

ate

cu

rre

nt

I G (

nA

)


DB-1

DB-2

DB-3

DB-4

DB-5

DB-6

-10 -5 0 5 10-2

-1

0

1

2

3

4

VSD

= 0.5 VSo

urc

e-g

ate

cu

rre

nt

I G (

nA

)


DN-1

DN-2

DN-3

DN-4

DN-5

DN-6

-10 -5 0 5 10

-2

0

2

4

6

So

urc

e-g

ate

cu

rre

nt

I G (

nA

)


DR-1

DR-2

DR-3VSD

= 0.5 V

36

sensing properties of carbon based nanomaterials. Therefore, the gate-source

current (IG), equivalent to the leakage current, was measured applying a constant

source-drain voltage (VSD) of 0.5 V.

According to figure 3.5, devices based on all carbon materials presented a source-

gate current in the range of nanoamperes. These results confirm the absence of

significant current leakage through the back gate and ensures that the source-drain

current changes measured during ammonia exposure experiments are cause

strictly by the interactions between nanomaterials and gas molecules.

37

3.2.2 SEM Characterization

The hypothesis relating the amount of nanomaterial deposited and the conductivity

of the gas sensing devices proposed in section 3.2.1 was supported by the results

from SEM characterization. A non-uniform layer and different amounts of rGO can

be observed on devices DR-1, DR-2 and DR-3 (Fig. 3.21). In case of SC-CNTs,

both sensors appeared to have a uniform distribution of nanomaterial underneath

the IDE (figure 3.6 A and E). However, a higher number of SWCNT were identified

in device DS-1 compared to device DS-2 explaining its higher conductivity. (Figure

3.6 B, C, D and F). In this particular case, the intrinsic characteristics of the

nanotubes (semiconducting or metallic) may also play a role. The same result was

present in devices based on pristine CNTs where a thicker network of nanotubes

was observed on device DP-1 compared to DP-2 and DP-3 (Figure 3.7).

The effect of the distribution quality of deposited nanomaterial became clearer in

devices based on B-CNTs and N-CNTs. As can be seen in figures 3.8 to 3.12 for

B-CNTs and 3.14 to 3.19 for N-CNTs, a layer of CNTs spread along the IDE was

not achieved in contrast to devices based on SC-CNTs and pristine CNTs. Instead,

dense and localized bundles, with a length of around 200 µm, were deposited. In

the specific case of devices DB-6 and DN-3, contamination particles were found

on the interdigitated area, these particles could lead to a misleading interpretation

of results in further ammonia exposure experiments producing a shortcut in the

sensor, therefore the electrodes connected to them were removed using the

tungsten needles of the probe station. However, successful removal of

microelectrodes was achieved for device DB- 6 only (Figure 3.13 A and C).

Drop coating is one of the most common casting methods for CNTs sensors due

to its low complexity, cost and because it allows for a local deposition of

nanomaterials onto a defined small area. These are the reasons why it was

selected for this investigation. However the main disadvantages are the difficult

control of the uniformity and thickness of the nanomaterial layer as demonstrated

in this study. Therefore, other casting methods like spray coating63 or

electrophoretic deposition (EPD)64 should be explored in order to improve the

quality of gas sensing devices.

38

Figure 3.6: SEM images of gas sensing devices based on SC-CNTs. (A-D device DS-1, E and F device DS-2). A higher number of small groups of SC-CNTs bridging the electrodes were observed in device DS-1 compared to device DS-2.

39

Figure 3.7: SEM images of gas sensing devices based in Pristine-CNTs. (A and B: Device DP-1, C and D: Device DP-2, E and F device DP-3)

40

Figure 3.8: SEM images of gas sensing device DB-1 based on B-CNTs. Big agglomerates of around 200 µm can be observed bridging the IDE.

Figure 3.9: SEM images of gas sensing device DB-2 based on B-CNTs. A big agglomerate of approximately 300 µm was present on the lower part of the IDE.

41

Figure 3.10: SEM images of gas sensing device DB-3 based on B-CNTs.

Figure 3.11: SEM images of gas sensing device DB-4 based on B-CNTs. Two big agglomerates of around 500 µm were present on the IDE.

42

Figure 3.12: SEM images of gas sensing device DB-5 based on B-CNTs. Big size agglomerate was observed on the right side of the IDE. (C) and (D) Small groups of B-CNTs bridging the IDE .

Figure 3.13: SEM images of gas sensing device DB-6 based on B-CNTs. (A) SEM image of contamination particle on the IDE. (C) SEM image after electrodes removal.

43

Figure 3.14: SEM images of gas sensing device DN-1 based on N-CNTs.


44

Figure 3.16: SEM images of gas sensing device DN-3 based on N-CNTs. (B) SEM image after unsuccessful electrodes removal.


45



46

Figure 3.20: SEM images of gas sensing devices DR-1 (A,B), DR-2 (C,D), DR-3 (E,F) based on reduced graphene oxide.

47

3.3 Ammonia Exposure Experiment

3.3.1 Resistance Response

Gas sensing devices were exposed to 1.5, 2.5, 5, 10 and 20 ppm of

ammonia at room temperature following the methodology described in section 2.4

and their normalized sensing response was obtained using Eq. 3. In addition, the

long term stability after 3 weeks under controlled storage conditions was

investigated. Due to the fact that different exposure times were used as described

in table 2.2, in order to compare the sensors based on different nanomaterials the

sensing response was calculated using Eq. (3) fixed to 10 minutes exposure for

1.5 ppm of ammonia and 5 minutes for the rest of the concentrations. Since the

effects from previous exposures and recoveries are inevitable when the sensor do

not return to its baseline current (full recovery)65, this calculation only takes into

account the current right before and after every ammonia exposure. The results

are summarized in table 3.1 and 3.2. Results from devices based on rGO are

presented and discuss separately from CNTs-based sensors due to its low sensing

response.

A decrease in current (ISD) was observed upon ammonia exposure in all tested

sensors to all ammonia concentrations (Fig. 3.21-3.24 and Table 3.1). However,

the sensing response differed among devices based on the same type of

nanomaterial. These results can be correlated to the amount and quality of

nanomaterial distribution discussed in section 3.2. It was noticed that devices with

higher values of resistance (low conductivity) exhibited a higher response to

ammonia for all SWCNT based sensors. (Table 3.1).

In the case of SC-CNTs, sensor DS-2 with less CNT bundles and a value of

resistance of 15KΩ showed almost twice the response to all ammonia

concentrations compared to device DS-1 with less CNT bundles and resistance

around 270 Ω. For pristine CNTs, devices DP-2 and DP-3 had a better performance

compared to device DP-1 which had a resistance value of 12 Ω and more CNT

bundles on the IDE. Regarding B-CNTs and N-CNTs, devices DB-6, DN-5 and DN-

6 presented the highest sensitivity and also the highest resistance values (between

48

100 Ω and 200 Ω) compared to the rest of devices which had more CNT bundles

and agglomerates. (Table 3.1).

Table 3.1. Sensing response to different ammonia concentrations of gas sensors based on SC-CNTs, Pristine CNTs, B-CNTs and N-CNTs.

(-) : No reliable sensing response.

ΔR/Ro

Material

Device

Ro (Ω)

1.5 ppm

(600s)

2.5 Ppm

(300s)

5 Ppm

(300s)

10 Ppm

(300s)

20 Ppm

(300s)

sc-CNTs

DS-1

268

1.52

1.08

1.41

2.07

3.07

DS-2

15000

3.21

2.26

3.02

3.72

4.96

Pristine CNTs

DP-1

12

-

-

0.13

0.43

0.74

DP-2

250

0.48

0.46

0.67

1.07

1.45

DP-3

174

0.22

0.22

0.35

0.55

1.03

BCNTs

DB-1

8

-

0.05

0.09

0.15

0.24

DB-2

8.5

0.05

0.04

0.05

0.06

0.09

DB-3

7.5

0.37

0.21

0.23

0.28

0.33

DB-5

6.8

0.03

0.03

0.05

0.09

0.13

DB-6

160

0.21

0.26

0.47

0.77

1.06

NCNTs

DN-1

7.3

-

0.03

0.04

0.08

0.1

DN-2

9

-

0.02

0.02

0.02

0.03

DN-5

112

0.70

0.51

0.86

1.14

1.5

DN-6

186

0.37

0.3

0.59

1.04

1.58

49

According to these results, the quality of the nanomaterial film on the IDE not only

determines the electrical characteristics of the sensors, as was demonstrated from

device characterization, but also their sensitivity to ammonia. This close relation

has been previously studied for SWCNTs and it was suggested that increasing the

density of SWCNTs films would reduce the resistance of the sensor and therefore

its ammonia sensitivity.56 This effect is most probably due to the continuous current

paths created inside the SWCNT bundles and agglomerates which are not affected

by interaction with ammonia molecules in contrast to the most outer layer of

nanotubes. Consequently, the effect of this interaction is not strong enough to

disturb the continuous current flow and a low response to ammonia is exhibited.

Among all tested devices, sensor DS-2 based on SC-CNTs presented the highest

response to all ammonia concentrations (Table 3.1). Up to 3.2% for 1.5 ppm during

an exposure time of 10 minutes and almost 5% for 20 ppm during 5 minutes

exposure. In terms of the detection limits, these values are remarkably better than

previous investigations for solid-state-based gas sensors where detection ranges

from 30-1000 ppm for sensors based on doped ZnO nanotetrapods66 and 50-500

ppm for polycrystalline WO3 nanofibers67 were reported. In addition, 8% of

response was achieved by DS-2 to 5 ppm during an exposure time of 1000

seconds compared to previous studies63 using SC-CNTs whereby only 2% was

achieved for the same parameters. These results demonstrate the strong potential

of SC-SWCNTs to be used as ammonia sensors for its high sensitivity and fast

response. It is natural to expect that concentrations of ammonia below 1 ppm could

be reliably detected with such sensors, which is relevant for the diagnosis of some

diseases by analyzing the breath of patients. However, the setup used in current

experiments did not allow to reduce NH3 concentrations below 1.5 ppm.

The high sensing response of SC-SWCNTs might be explained by the ordered

nanomaterial layer under the IDE in comparison to the rest of the devices which

enhance the surface area for ammonia interactions.68 (See section 3.2.2).

However, the nature of these interaction suggest a strong attachment of the NH3

molecules to the SC-SWCNT due to the fact that no recovery was observed during

the 15 minutes of pure N2 flow between ammonia exposures (Fig. 3.21). Same

result was reported previously in the literature, where times up to 12 hours at room

temperature or alternative heat treatment were necessary for desorption of

50

ammonia. Chemisorption of NH3 molecules due to site defects on the sidewall of

the CNTs was suggested.59 Such defects could be produced during lithography

process after SC-CNT deposition.

a)

b)

c)

d)

Figure 3.21: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DS-1 and DS-2 based on SC-CNTs. (a) and (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). In addition, no recovery under pure N2 flow was present. (c) and (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration.

0 2000 4000 6000 80003.2x10

-4

3.4x10-4

3.6x10-4

3.8x10-4

N2

N2

N2

N2

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DS-11.5 ppm

2.5 ppm

5 ppm

10 ppm

20 ppm

N2

0 2000 4000 60005.0x10

-6

5.5x10-6

6.0x10-6

6.5x10-6

7.0x10-6

N2

N2

N2

N2

N2

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DS-21.5 ppm

2.5 ppm

5 ppm

10 ppm

20

ppm

0 2000 4000 6000

0

5

10

15 20

ppm

10

ppm

5

ppm

2.5

ppm

1.5

ppm

R

/Ro

(%

)

Time (s)

DS-1

0 2000 4000 6000

0

5

10

15

20

25

30

R

/Ro

(%

)

Time (s)

DS-2

1.5

ppm

2.5

ppm

5

ppm

10

ppm

20

ppm

51

The most viable explanation coming from computational69–71 and experimental72,73

approaches states that there are small binding energies of ammonia molecules

which induced to its physical adsorption on SWCNTs without defects and a

mechanism based on a small charge transfer from NH3 to SWCNTs leads to a

reduction in conductivity.

In contrast to SC-CNTs based sensors, a slow recovery after each ammonia

exposure can be observed in devices DP-2, DP-3, DB-5, DB-6, DN-5 and DN-6

(Fig. 3.22 to 3.24). In addition, all of them exhibited similar ammonia sensing

responses. (Table 3.1) Since pristine CNTs investigated in this work are the same

primary material for synthesis of B-CNTs and N-CNTs and the content of N and B

is rather low (2.2% for N and 2.2-3% for B from XPS analysis), the dominant

sensing mechanism that produced the decrease in conductivity might arise from

physisorption of NH3 on the CNTs surface and not from interactions with doped

sites.

Nonetheless, it is important to point out that devices DN-5 and DN-6 based on N-

CNTs showed slightly better response to all ammonia concentrations compared to

devices based on pristine and B-CNTs (Table 3.1). For instance, for the lowest

ammonia concentration of 1.5 ppm, DN-5 presented a response of 0.7% compared

to 0.4% and 0.5% from B-CNTs and pristine CNTs, respectively. While for 20 ppm

DN-6 exhibited a sensing response of 1.6% compared to 1.4% from pristine CNTs

and 1% from B-CNTs.

This small improvement is contrary to what is expected from computational

analysis comparing pristine CNTs, B-CNTs and N-CNTs where a higher affinity of

NH3 to B-CNTs was demonstrated74,75. But, at the same time, it is consistent to

previous experimental investigations which demonstrated the advantages of using

nitrogen- doped carbon nanotubes instead of pristine CNTs to obtain better

sensing response to ammonia.76,77 The improvement is based on the interaction

between NH3 and N-pyridinic defects on the SWCNTs that leads to an important

charge transfer from gas molecules to N-CNTs. These strong interactions account

for chemisorption and therefore they are difficult to eliminate after ammonia

exposure. This would explain the incomplete recovery under continuous flow of N2

after each ammonia exposure depicted in Figure 3.24.

52

Nevertheless, the leading interaction is the sorption of NH3 molecules on carbon

lattice of CNT walls without noticeable charge transfer, which causes small lattice

distortions and is detected as the reduction of the current through the CNTs. This

effect is apparent for all pristine, B-doped and N-doped CNTs.

Devices based on pristine CNTs were less sensitive compared to previous studies

where a response of 1.9 % was obtained for 1.5 ppm during 200 seconds56. This

same case applies for devices based on N-CNTs, where a 2% response was

exhibited for 1 ppm during 400 s exposure.76

In order to achieve a deeper understanding regarding the ammonia sensing

mechanisms taking place on carbon-based sensors tested in this work, further

investigations need to be performed. However, it can be concluded from the

presented results that no significant improvement in sensitivity to ammonia was

observed by using N- and B- doped SWCNTs.

53

a)

b)

c)

d)

Figure 3.22: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DP-2 and DP-3 based on Pristine-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). In addition, a slight and incomplete recovery is exhibited by DP-2 and not by DP-3 under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration.

0 2000 4000 6000 80003.76x10

-4

3.80x10-4

3.84x10-4

3.88x10-4

3.92x10-4

3.96x10-4

N2

N2

N2

N2

20

ppm

10 ppm

5 ppm

2.5 ppm

1.5 ppm

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DP-2

N2

0 2000 4000 6000 8000

5.5x10-4

5.6x10-4

5.7x10-4

5.8x10-4

N2

N2

N2

N2

20 ppm

10 ppm

5 ppm

N2

1.5 ppm

2.5 ppm

Sourc

e-d

rain

curr

ent I S

D (

A)

Time (s)

DP-3

0 2000 4000 6000 8000

0

1

2

3

4

5 20 ppm

10

ppm

5

ppm

2.5

ppm

1.5

ppm

R

/Ro

(%

)

Time (s)

DP-2

0 2000 4000 6000 8000

0

2

4

6 20

ppm

10

ppm

5

ppm

2.5

ppm

1.5

ppm

R

/Ro

(%

)

Time (s)

DP-3

54

a)

b)

c)

d)

Figure 3.23. Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DB-5 and DB-6 based on B-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). Moreover, an incomplete recovery is also exhibited by both sensors under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration.

0 2000 4000 6000 80001.448x10

-2

1.449x10-2

1.450x10-2

1.451x10-2

1.452x10-2

1.453x10-2

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DB-5

N2

N2

N2

N2 20 ppm

10 ppm

5 ppm

2.5 ppm

1.5 ppm

N2

0 2000 4000 6000

6.20x10-4

6.25x10-4

6.30x10-4

6.35x10-4

20 ppm

10 ppm

5 ppm

2.5 ppm

N2

N2

N2

N2

N2

1.5 ppm

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DB-6

0 2000 4000 6000 8000

0.00

0.05

0.10

0.15

0.20

0.25

0.30 20

ppm

10

ppm

5

ppm

2.5

ppm

R

/Ro (

%)

Time (s)

DB-5

1.5

ppm

0 2000 4000 6000

0.0

0.5

1.0

1.5

2.0

2.5 20

ppm

10

ppm

5

ppm

2.5

ppm 1.5

ppm

R

/Ro (

%)

Time (s)

DB-6

55

a)

b)

d)

c)

Figure 3.24: Response under different ammonia concentrations (1.5 ppm, 2.5 ppm, 5 ppm, 10 ppm and 20 ppm) of devices DN-5 and DN-6 based on N-CNTs. (a), (b) A decrease in current (ISD) upon exposure to all ammonia concentrations can be observed in both sensors (black arrows). Moreover, an incomplete recovery is also exhibited by both sensors under pure N2 flow. (c), (d) Sensing response, ΔR/Ro in (%). Exposure times are delimited by dotted lines with the corresponding concentration.

0 2000 4000 60008.50x10

-4

8.60x10-4

8.70x10-4

8.80x10-4

8.90x10-4

N2

N2

N2

N2

N2

20 ppm

So

urc

e-d

rain

cu

rre

nt

I SD (

A)

Time (s)

DN-51.5 ppm

2.5 ppm

5 ppm

10 ppm

0 2000 4000 6000

3.36x10-4

3.40x10-4

3.44x10-4

3.48x10-4

N2

N2

N2

N2

N2

20 ppm

10 ppm

5 ppm

2.5 ppm

1.5 ppm

Sourc

e-d

rain

curr

ent I S

D (A

)

Time (s)

DN-6

0 2000 4000 6000

0

1

2

3

4

20

ppm

10

ppm

5

ppm

2.5

ppm

R

/Ro

Time (s)

DN-5

1.5

ppm

0 2000 4000 6000

0

1

2

3

20

ppm

10

ppm

5

ppm

2.5

ppm 1.5

ppm

R

/Ro

Time (s)

DN-6

56

Regarding the devices based on rGO, even though they had similar electrical

characteristics, only DR-1 exhibited a slight decrease in current upon exposure to

20 ppm and 50 ppm of ammonia, as can be seen in figure 3.25. The corresponding

sensing responses were 0.6% for 20 ppm and 0.7% for 50 ppm. However, the noisy

signal made it difficult to reliably determine that the changes in current (ISD) were

due to interactions between NH3 and rGO.

Previous studies have reported the good sensitivity of rGO based sensors to high

concentrations of ammonia, proposing a sensing mechanism by physisorption as

well as chemisorption of NH3 mainly through hydrogen bonding at the defect sites

and with the functional groups (carboxyl, carbony, epoxy and hydroxyl)78. However,

only sensitivity to concentrations in the range of 200 ppm have been achieved.43

According to the results presented in this work, it can be concluded that the

sensitivity of rGO to low concentrations of ammonia is significantly lower that the

sensitivity of all CNTs-based gas sensors.

Figure 3.25: Source-drain current (ISD) response of device DR-1 to different ammonia concentrations. Due to the noisy signal it is difficult to determine that changes in current (ISD) were due to interactions between NH3 and rGO.

0 2000 4000 6000 8000 100000.435

0.440

0.445

0.450

0.455

50

ppm

20

ppm

10

ppm

5

ppm 2.5

ppm

So

urc

e-d

rain

cu

rren

t (

A)

Time (s)

DR-1

1.5

ppm

57

3.3.2 Recovery Properties of Gas Sensors

The purpose of this experiment was to determine the recovery properties of gas

sensors after ammonia exposure. For this, the devices were stored at 22 °C and

30% of humidity for 3 weeks, then a continuous N2 flow was applied for 4 hours

and finally a second ammonia exposure experiment with the same parameters was

performed. The results are summarize in table 3.2. Sensors based on rGO did not

show significant response to second ammonia exposure experiment, they are not

further discussed in this section.

Devices DS-1 and DS-2 based on SC-CNTs did not recover their initial resistance

after 3 weeks of storage. An increase in around 800 Ω for DS-1 and 600 Ω for DS-

2 was observed. This result confirms the strong interactions between NH3

molecules and SC-CNTs discussed in section 3.1 and the need for external

treatments, like high temperature annealing, in order to desorb the remaining

ammonia molecules on the device. In addition, the ammonia response from the

second exposure revealed a reduction in sensing properties. The difference

between the first and second response to 1.5 ppm of ammonia was around 0.5%

for DS-1 and 2.4% for DS-2, whereas to 20 ppm only 0.3% for DS-1 and 0.6% for

DS-2, meaning that even though the sensitivity was decreased the devices were

still able to detect high concentrations of ammonia (Table 3.2)

In contrast, pristine-CNT sensors showed a lower decrease in sensing response.

Only 0.16% for 1.5 ppm and 0.18% for 20 ppm from device DP-2 and 0.1% for 1.5

ppm and 0.2% for device DP1. Regarding sensors based on B-CNTs, device DB-

6 showed a difference of 0.06% to 1.5 ppm and 0.04% to 20 ppm compared to the

first ammonia experiment. On the other hand, the sensing response of device DN-

5 based on N-CNTs differed in 0.47% for 1.5 ppm and 0.25% for 20 ppm while for

device DN-6 a difference of 0.14% and 0.41% was observed for 1.5 ppm and 20

pm, respectively (Table 3.2).

Since the values of sensing response are similar for pristine CNTs, N-CNTs and

B-CNT the difference between the first and second exposure experiments can be

directly compared.

58

Table 3.2: Gas sensing response of first and second ammonia exposure experiments of

gas sensors based on SC-CNTs, Pristine-CNTs, B-CNTs and N-CNTs.

(-): No reliable sensing response. X: Broken device.

ΔR/Ro (%)

Material

Device

Exposure

experiment

R(Ω)

1.5 ppm

(600s)

2.5 Ppm

(300s)

5 Ppm

(300s)

10 Ppm

(300s)

20 Ppm

(300s)

sc-

CNTs

DS-1 1 268 1.52 1.08 1.41 2.07 3.07

2 893 1 0.68 1.07 1.72 2.74

DS-2 1 15000 3.21 2.26 3.02 3.72 4.96

2 15800 0.83 0.95 1.75 3.21 4.41

Pristine CNTs

DP-1 1 12 - - 0.13 0.43 0.74

2 14.5 0.03 0.04 0.09 0.16 0.24

DP-2 1 250 0.48 0.46 0.67 1.07 1.45

2 167 0.32 0.28 0.48 0.8 1.27

DP-3 1 174 0.22 0.22 0.35 0.55 1.03

2 202 0.32 0.23 0.36 0.55 0.8

BCNTs

DB-1 1 8 - 0.05 0.09 0.15 0.24

2 X - - - - -

DB-2

1 8.5 0.05 0.04 0.05 0.06 0.09

2 8.6 0.05 0.04 0.04 0.07 0.10

DB-3

1 7.5 0.37 0.21 0.23 0.28 0.33

2 X - - - - -

DB-5 1 6.8 0.03 0.03 0.05 0.09 0.13

2 7.5 0.02 0.04 0.03 0.05 0.08

DB-6 1 160 0.21 0.26 0.47 0.77 1.06

2 150 0.27 0.21 0.49 0.69 1.1

NCNTs

DN-1 1 7.3 - 0.03 0.04 0.08 0.1

2 7.27 0.03 0.03 0.05 0.09 0.1

DN-2

1 9 - 0.02 0.02 0.02 0.03

2 X - - - - -

DN-5

1 112 0.70 0.51 0.86 1.14 1.5

2 110 0.23 0.24 0.43 0.69 1.25

DN-6 1 186 0.37 0.3 0.59 1.04 1.58

2 200 0.23 0.19 0.39 0.64 1.17

59

The wide variation of sensing response between the first and second exposure

experiments of devices based on N-CNTs compared to pristine and B-CNTs might

suggest that a strong interaction between ammonia and N-CNTs occurred during

the first exposure. Thus, the ammonia molecules still attached to the N-CNTs

network, probably also by chemisorption on the N-pyridinic sites as suggested in

section 3.1, might prevent interactions with new NH3 molecules during the second

exposure experiment, therefore decreasing the sensor’s response76. In addition,

low difference exhibited by pristine and B-CNTs would suggest weak interactions

leading to a detachment of NH3 molecules during the 3 weeks of storage. However,

further investigations should be performed in order to understand desorption

mechanisms after NH3 exposure.

In conclusion, all devices presented a decrement in sensing response after 3

weeks of storage under controlled condition of 22°C and 30% humidity. The lowest

recovery was observed for SC-CNTs followed by N-CNTs, pristine CNTs and finally

B-CNTs. From this, it is particularly evident that there is almost no chemisorption

of NH3 molecules at B-CNTs as was suggested from computational results,74

probably because the model investigated in the report is not the same as in the

experiment. Further study of the different recovery values of each material can

provide insight to the NH3 desorption mechanism.

60

4. Conclusions

In this work the proof-of-concept for carbon-based nanomaterial gas sensors using

the same setup as for SiNW-based gas sensors was realized. Gas sensing devices

based on SC-CNTs, pristine CNTs, B-CNTs, N-CNTs and rGO for ammonia

detection purposes were successfully fabricated using a drop casting approach

and characterized under NH3 exposure.

Firstly, the optimal dispersion dilutions for fabrication of gas sensors was

investigated by means of deposition experiments and SEM analysis. Dilutions of

50 % v/v and 2% v/v in NMP were determined as optimal for fabrication of gas

sensors based on rGO and pristine CNTs, B-CNTs, N-CNTs, respectively.

Based on SEM and electrical characterizations, it was found that the deposition of

nanomaterial is a crucial step in the process of fabricating highly sensitive devices

to low ammonia concentrations. A direct relation between the amount and quality

of distribution of the nanomaterial deposited on the IDE and the electric and

sensing properties of the sensor was established. It was observed that increasing

the amount of nanomaterial on the electrodes leads to an increase in sensor’s

conductivity in one hand and to a decrease in ammonia sensitivity on the other

hand.

All sensors tested in this work, except for rGO based gas sensors, exhibited a

measurable response to 1.5, 2.5, 5, 10 and 20 ppm of ammonia. In addition, the

best response was achieved by sensors based on SC-CNTs. Specifically, 3.2% to

1.5 ppm for an exposure time of 10 minutes and almost 5% to 20 ppm for only 5

minutes exposure (ΔR/Ro). The results are significantly better than previous results

reported for metal oxide based sensors for sensitivity and for SiNW based sensors

for response time. Furthermore, no recovery after each ammonia exposure was

observed and a sensing mechanism model based on small charge transfer upon

ammonia exposure was suggested.

The sensors based on SC-CNTs are expected to reliably detect NH3 in

concentrations of hundreds of ppb after few minutes of exposure. Such sensitivity

is relevant for diagnostics of certain diseases like liver cirrhosis, kidney failure12

61

and Helicobacter pylori infections13, using low-power, compact SC-CNTs based

sensors operating at room temperature.

Devices based on pristine CNTs, B-CNTs and N-CNT exhibited similar sensing

responses to all ammonia concentrations. Thus, it can be concluded that possibly

no significant improvement can be achieved by using doping materials in case of

ammonia detection. Most probably because NH3 molecules interact with carbon

atoms of CNTs rather than with dopant atoms.

Finally, the recovery properties of sensors after 3 weeks of storage in controlled

conditions of temperature and humidity were analyzed. The lowest recovery was

observed for SC-CNTs followed by N-CNTs, pristine CNTs and finally B-CNTs

Outlook and Future Work

The proof of principle of carbon-nanomaterial based gas sensors in the setup,

which was originally developed for SiNW-based gas sensors, was successfully

realized in this work. This investigation opens new interesting research directions

to further investigations in the field of gas sensing technology at the Chair.

Following the investigation started by this work, the use of an even more diluted

mixture of N2/NH3 would allow to reach sub-ppm ammonia concentrations and the

limit of detection of devices could be experimentally determined.

Furthermore, the carbon nanomaterials used for this work can now be also tested

under exposure to different gases to investigate their sensing response and the

mechanism underlying the interaction between gas molecules and nanomaterial.

In addition, for each gas studied the ideal operation temperature, sensor recovery

and maximum number of operation cycles can be determined.

On the other hand, as demonstrated by this work, external treatments are

necessary for complete recovery of sensors after ammonia exposures. Therefore,

different experiments can be aimed to study different approaches to achieve it, for

instance high temperature annealing, vacuum treatment or a combination of both.

In addition, the influence of humidity and the selectivity to different components of

the human breath could lead to more reliable ways for diagnosis in the biomedical

field.

62

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MASTER’S THESIS - TU Dresden · 2020. 9. 3. · Dresden, Germany. 31. August. 2015 . II Acknowledgments I would like to thank Prof. Gianaurelio Cuniberti for giving me the opportunity