Block Copolymers: An Effective Tool for Fundamental and

Applied Chemical Engineering

By

Douglas Russell Greer

A dissertation submitted in partial satisfaction of the

requirements for the degree of

Doctor of Philosophy

in

Chemical Engineering

in the

Graduate Division

of the

University of California, Berkeley

Committee in charge:

Professor Nitash P. Balsara, Chair

Professor Bryan D. McCloskey

Professor David E. Wemmer

Fall 2017

Block Copolymers: An Effective Tool for Fundamental and Applied Chemical Engineering

© Copyright 2017

Douglas Russell Greer

All Rights Reserved

1

Abstract

Block Copolymers: An Effective Tool for Fundamental and Applied Chemical Engineering

By

Douglas Russell Greer

Doctor of Philosophy in Chemical Engineering

University of California, Berkeley

Professor Nitash P. Balsara, Chair

Block copolymer (BCP) self-assembly has garnered significant attention for several decades because it can yield ordered structures in a wide range of morphologies with potential or practical applications in many fields. A diblock copolymer is a polymer consisting of two distinct monomers. The monomers are arranged such that there are distinct chains of each monomer, and the chains are covalently linked together to form a single copolymer chain. In BCPs, the enthalpic contributions to free energy are often significant enough overcome entropy, resulting in the formation of microdomains of each type of monomer. This self-assembly is useful for fundamental studies of molecular conformation and for engineering materials wherein the useful properties of two chemically distinct chains are incorporated into a single molecule. In this work we will demonstrate the extraordinary effectiveness block copolymers to both types of studies.

In this dissertation work, we used peptoid diblock copolymers to identify a motif common to all bulk phase crystalline peptoid polymers. Poly N-substituted glycine materials (peptoids) have the capacity for prolific diversity due to their large library of monomers, synthetic sequence control, and monodispersity. These properties make peptoids an ideal material for the study of the relationship between chemical structure and supramolecular structure. In order to probe this relationship, we synthesized and analyzed a series of crystalline peptoid copolymers, systematically varying peptoid side-chain length (S) and main-chain length (N). In all peptoids, we found that the three unit cell dimensions - a, b, and c - are simple linear functions of S and N. These relationships indicate that the molecules adopt extended, planar conformations. This new structural motif can be used to design broad classes of assemblies which have specific unit cell sizes, functional group densities, or aqueous monolayer thicknesses, based upon a specific backbone conformation and packing preference. Furthermore, these materials ordered well enough in water to achieve the first 2 Å level transmission electron microscopy of a synthetic polymer.

2

In the second study, we used block copolymers to impart two properties in a material effective for the pervaporation – a separation consisting of permeation and evaporation – of aqueous volatile organic compounds (VOCs). We performed this separation using a microphase separated polystyrene-block-polydimethylsiloxane-block-polystyrene (SDS) copolymer membrane. The PDMS domains are rubbery and have good permeation properties for volatile organic compounds (VOCs). The PS domains are glassy and provide the membrane with structural integrity. We find that using SDS block copolymer membranes is effective for the removal of inhibitors from lignocellulosic dilute-acid hydrolysate. Furthermore, the pervaporation-treated hydrolysates are suitable for ethanol fermentation with Saccharomyces cerevisiae . These results indicate that block copolymer-based pervaporation is a viable approach for hydrolysate detoxification in an industrial bioethanol production process.

Taken together, these studies demonstrate that block copolymers are an effective tool with which to implement both fundamental molecular engineering studies and chemical engineering design.

i

Acknowledgements

“If I have seen further it is by standing on ye sholders of Giants.”

Newton, 1675

I’d like to thank my mom for encouraging my education and consistently providing a path for fulfilling learning. I’d like to thank my dad for teaching me the scientific method and skepticism. There are countless other things for which I owe them thanks. Thank you to my brothers, our experience is shared.

I’m grateful to my advisors who provided me the opportunity to do interesting, fulfilling, and impactful graduate research. Nitash Balsara has built an excellent group with a culture that fosters teamwork and balance. Ron Zuckermann is the Facility Director Nanobio facility at the Molecular Foundry, a user facility in which I spent most of my graduate career. The experience was truly unique – learning from and working with so many scientists in different in different stages of their careers under effective management. I respect that both their groups continue to have open communication and safe practices. I’m truly fortunate that my graduate school career was generally so positive under their leadership.

I believe that the best way for skilled learning is through purposeful mentorship. I’m grateful to Nikos Petzetakis for being a super mentor through the EBI project and teaching me the focus, patience, and flexibility required for organic chemistry. I’m grateful to Ryan Spencer for his wisdom, guiding me through the peptoid project when it seemed intractable.

We often learn so much from those we are trying to teach, and I am grateful for my mentees. Michael Stolberg has been an extraordinarily effective research assistant and good friend. We’ve seen the emergence of several patterns and I look forward to seeing them published. Best of luck in graduate school, Michael. Zachary Pieters taught me how to effectively synthesize peptoids. He was a great roommate and I wish him the best at the Chicago College of Osteopathic Medicine. I was thankful to have Harrison Bergman with me to the start of the peptoids project, and I’m happy he went on to work with Kent Kirshenbaum, a key player in the peptoids community.

I’m grateful for my coworkers at each of the four lab spaces at which I worked. In the Balsara lab, I am thankful to Chae-Young Shin for teaching me pervaporation techniques, Alex Wong for his useful discussions on separations and catalysts, Jacob Thelen and Whitney Loo for their lessons on X-ray scattering, Adri Rojas for keeping the lab safety culture, and the rest of the group for useful discussions and their friendship. At the Molecular Foundry, I am thankful to Mark Kline who enabled our high-throughput synthesis, Joyjit Kundu for his work on peptoid models, Jing Sun for starting the work I continued, John Edison for his discussions on peptoid conformations, Rita Garcia for

ii

her support and enthusiasm, Michael Connolly for his equipment management and training, and the rest of the users and staff for their day-to-day support. At the Energy Biosciences Institute, I’d like to thank Chris Somerville for his vision and qualifying exam suggestions and I’ll thank Stefan Bauer and his team for their superb, professional analytical chemistry work. I’m thankful for the beamline scientists that enabled so much of this work, especially Mike Brady and Chenhui Zhu.

I’d like to thank the members of the Soft Matter Electron Microscopy program for their cooperation. It truly was a special and impactful project. Xi Jiang, it’s been wonderful working closely with you, the images are beautiful and I’m proud of what we’ve done. David Prendergast, Ken Downing, Andy Minor, it’s been really fulfilling to work on such a collaborative project.

Thank you, friends, for making the bay area feel like home the past five years - Steve, Nate, Max, Evin, Michael, Steve, Brian, Kona, Brycen, Noah and all the rest. Good luck Cal Club Water Polo, I’m proud of you all, Go Bears!

The Soft Matter Electron Microscopy Program (KC11BN), the Molecular Foundry and Beamline 7.3.3 of the Advanced Light Source at Lawrence Berkeley National Laboratory (LBNL) are supported by the Director of the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work at the Molecular Foundry was supported by a user project and access to computational resources administered by LBNL’s High Performance Computing Services Group. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. This work was partially funded by the Energy Biosciences Institute. Hydrolysate was provided by the National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, a national laboratory of the U.S. Department of Energy managed by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy under Contract Number DE-AC36-08GO28308.

iii

Table of Contents

Abstract ........................................................................................................................... 1

Acknowledgements .......................................................................................................... i

Table of Contents ............................................................................................................ iii

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

Block Copolymers ........................................................................................................ 1

Peptoid Polymers......................................................................................................... 1

Membrane Separation Engineering ............................................................................. 3

Outline of Dissertation ................................................................................................. 3

Chapter 1 – Universal Relationship between Molecular Structure and Crystal Structure in Peptoid Polymers and Prevalence of the Cis Backbone Conformation ....................... 5

1.1 Summary: .............................................................................................................. 5

1.2 Background:........................................................................................................... 6

1.3 Results and Discussion: ........................................................................................ 7

1.4 Conclusion ........................................................................................................... 22

1.5 Acknowledgement: .............................................................................................. 22

Chapter 2 – Fermentation of Hydrolysate Detoxified by Pervaporation through Block Copolymer Membranes ................................................................................................. 23

2.1 Summary: ............................................................................................................ 23

2.2 Background:......................................................................................................... 24

2.3 Methods: .............................................................................................................. 25

2.3.1 Membrane Properties and Testing ................................................................ 25

2.3.2 Dilute Acid Hydrolysis ................................................................................... 26

2.3.3 Pervaporation and Analysis ........................................................................... 26

2.3.4 Thermodynamic Properties Calculation ........................................................ 28

2.3.5 Yeast Culture and Fermentation ................................................................... 29

2.4 Results and Discussion ....................................................................................... 30

2.5 Conclusion ........................................................................................................... 39

2.6 Acknowledgement ............................................................................................... 39

Conclusion .................................................................................................................... 40

References .................................................................................................................... 41

Appendix 1 - Supporting information for Chapter 1 ....................................................... 48

A1.1 Peptoid Synthesis .............................................................................................. 48

A1.2 Nanosheet Formation ....................................................................................... 48

iv

A1.3 X-Ray Scattering ............................................................................................... 49

A1.4 Molecular Dynamics Simulation ........................................................................ 49

A1.5 Peptoid Characterization Data: .......................................................................... 53

Ac-Ndc9-Nte5 .......................................................................................................... 53

Ac-Ndc9-Nte9 .......................................................................................................... 54

Ac-Ndc9-Nte15......................................................................................................... 55

Ac-Nhp9-Nde9 ......................................................................................................... 56

Appendix 2 - Supporting information for Chapter 2 ....................................................... 57

Table A.2.1 ................................................................................................................ 58

1

Introduction

Block Copolymers

Block copolymers (BCPs) have been established as effective systems for achieving ordered structures on the nanoscale due to their self-assembly. BCPs are defined as two or more chemically distinct polymer chains covalently bonded together to form a single copolymer chain. In BCPs, the enthalpic contributions to free energy are often significant enough to result in microphase separation which is manifested in the formation of microdomains. The phenomenon in which these ordered microdomains appear out of a disordered melt (typically at a specific temperature) is called the order-to-disorder transition (ODT). The fundamental quantification of these entropic and enthalpic contributions were determined by Flory and Huggins.1,2

The physics of the ODT in A-B diblock copolymers is well characterized. The mean field theory of Leibler predicts that for symmetric block copolymers wherein the volume fraction of the A-block (fA) is 0.5, the ODT occurs at the temperature at which χN = 10.495, where χ is the temperature-dependent Flory-Huggins interaction parameter between segments A and B, and N is the number of segments per chain.3 The theory of A-B diblock copolymers ordering into specific microdomain geometries - such as spheres, cylinders, and lamella – was laid out by Matsen and Bates.4 Additionally, there is theory for correlating the molecular weight of the polymer and the length-scale of the microdomains; for instance, a 0.643 power law was reported for lamellar microdomains.5

BCP self-assembly has attracted considerable attention for many decades because it can yield ordered structures in a wide range of morphologies, including spheres, cylinders, bicontinuous structures, lamellae, vesicles, and many other complex or hierarchical assemblies.6 These aggregates provide potential or practical applications in many fields, including separation processes, energy storage and production, pattern transferring, and pharmaceutical packaging and delivery.7 Leveraging the effective self-assembly of block copolymers will be a key aspect of the studies in this dissertation.

Peptoid Polymers

Poly N-substituted glycine materials (peptoids) are an excellent platform with which to study the fundamentals of diblock copolymer relationships due to their large library of monomers, synthetic sequence control, and monodispersity.8–11 These properties make peptoids an ideal platform with which to study the relationship between chemical structure and folded or supramolecular structure.

Peptoids polymers have unsurpassable purity, at molecular purities often greater than 90%, and polydispersity indices (PDIs) less than 1.0003. The theories of block copolymers assemblies are usually applied to monodisperse polymers (PDI = 1.0), while typical solution-phase polymers syntheses yield PDIs greater than 1.1. This monodisperse property of peptoids removes one variable from the self-assembly of

2

block copolymers, allowing for a more direct comparison of self-assembly theory and observation.12

Bioinspired polymeric materials are attracting increasing attention due to their finely tuned physical and chemical structures inspired by nature, with the stability and processability afforded by traditional polymers. Many well-defined peptoid molecular structures have been identified, typically from short oligomers, including ribbons,13 loops,14 helices,15,16 and macrocycles.17,18 Despite the wealth of useful supramolecular structures seen from these materials, there is no consensus on their polymeric conformation in solution or in the melt.

We used the self-assembly of BCP peptoid systems to precisely determine the crystal structure of peptoid polymers. In this dissertation, we explore our theory that the well-defined diffraction of a model set of peptoid BCPs can be used to describe the chemistry/assembly relationship in all ordered peptoid polymers. Understanding the fundamental assembly relationships of peptoids is impactful to the field of bioinspired polymers, as the assembly mechanisms of many of their nanostructures are not well understood.

3

Membrane Separation Engineering

Industrially, membrane separations are typically performed with polymeric membranes. These separations are divided into broad classes by the phases of the feed and permeate. Processes utilizing polymeric membranes are used to separate species from liquids based on size, ranging from ionic-sized particles in reverse osmosis to larger particles in micro-filtration.19 Polymeric membranes have been effective in gas-phase separations, especially in removing water from vapor phases, and overcoming azeotropes in distillation processes.20 Pervaporation - the combination of permeation and evaporation - has a liquid feed and vapor permeate, has received more attention in recent years, owing to its mild separation conditions21 and its potential for the in-situ

removal of products.22

Membrane technologies have been shown to play a key role in process intensification and products recovery and purification in biorefining and bioenergy production processes. Namely, these processes include the separation and purification of individual molecules from biomass, the removal of fermentation inhibitors, enzyme recovery from hydrolysis processes, membrane bioreactors for bioenergy and chemical production, bioethanol dehydration, and bio-oil and biodiesel production.23

I used block copolymers to impart two properties in a material effective for the pervaporation separation of aqueous organic compounds. Specifically, we used a polystyrene-block-polydimethylsiloxane-block-polystyrene (SDS) copolymer membrane, which contains co-continuous polystyrene (PS) and polydimethylsiloxane (PDMS) domains. The PDMS domains are rubbery and have good permeation properties for volatile organic compounds (VOCs), as we have previously reported.24 The PS domains are glassy and provide the membrane with structural integrity. Specifically, the membranes we cast had active layers 10 μm thick, which was only feasible with through the strength imparted by polystyrene block. We used these effective membranes to test whether we could design pervaporation processes to make hydrolysates are suitable for ethanol fermentation with Saccharomyces cerevisiae. Our results are important to the design of industrial bioethanol production processes.

Despite two productive years on membrane separation research, further work on this project was discontinued in January 2015 due to funding cuts. The project funding cut was concurrent with the 50% decrease in oil prices over the 6 months prior to Jan 2015. Subsequently, my work on peptoid systems was begun.

Outline of Dissertation

In Chapter 1, we synthesized a series of peptoid block copolymers, changing main chain and sidechain length, and examine the crystalline unit cell sizes using X-ray diffraction. We find a specific molecular conformation, not seriously considered in previous theory, which explains not only the unit cell dimensions of our compounds, but of all peptoid polymer crystals previously reported. Additional information about the methods involved in this study can be found in Appendix 1.

4

In Chapter 2, we demonstrate the pervaporation of VOCs with polystyrene-block-polydimethylsiloxane-block-polystyrene (SDS) to be an effective method for separating inhibitors from dilute acid pretreated hydrolysate. This pervaporation process was the first to produce a hydrolysate effective for subsequent fermentation for ethanol production. Additional information about the thermodynamics involved in this study can be found in Appendix 2.

5

Chapter 1 – Universal Relationship between Molecular Structure and Crystal Structure in Peptoid Polymers and Prevalence of the Cis Backbone Conformation

*This chapter is reproduced with permission from the Journal of the American Chemical Society, in press. Unpublished work copyright 2017 American Chemical Society.

1.1 Summary:

Peptoid polymers are often crystalline in the solid-state as examined by X-ray scattering, but thus far, there has been no attempt to identify a common structural motif among them. In order to probe the relationship between molecular structure and crystal structure, we synthesized and analyzed a series of crystalline peptoid copolymers, systematically varying peptoid side-chain length (S) and main-chain length (N). We also examined X-ray scattering data from 18 previously reported peptoid polymers. In all peptoids, we found that the unit cell dimensions, a, b, and c, are simple functions of S and N: a (Å) = 4.55, b (Å) = [2.98] N + 0.35, and c (Å) = [1.86] S + 5.5. These relationships, which apply to both bulk crystals and self-assembled nanosheets in water, indicate that the molecules adopt extended, planar conformations. Furthermore, we performed molecular dynamics simulations (MD) of peptoid polymer lattices, which indicate that all backbone amides adopt the cis conformation. This is a surprising conclusion, since previous studies on isolated molecules indicated an energetic preference for the trans conformer. This study demonstrates that when packed into supramolecular lattices or crystals, peptoid polymers prefer to adopt a regular, extended, all-cis secondary structure.

6

1.2 Background:

Poly N-substituted glycine materials (peptoids) have the capacity for prolific diversity due to a large library of monomers, synthetic sequence control, and monodispersity.8–11 These properties make peptoids an ideal platform with which to study the relationship between chemical structure and folded or supramolecular structure.12,25 Additionally, peptoids are non-toxic and can self-assemble in water, providing a useful platform for biomimetic, foldamer, and nanoscale research.26,27 While peptoids are generally thought to be flexible in solution,28,29 many well-defined molecular structures have been identified, typically from short oligomers. Ribbons,13 loops,14 helices,15,16 and macrocycles17,18 have been observed in short chains, all resulting from the deliberate introduction of conformational constraints (e.g. sterically hindered monomers). Higher molecular weight peptoid polymers are receiving increased attention,9,10 due to their convenient and efficient synthesis,30 and a growing interest in the impact of sequence-control on polymer properties.31 Peptoid polymer structure in the bulk phase has been probed by many investigators, but so far there has been no consensus on identifying any underlying common motifs to their structure or packing interactions.

The unit cell dimensions of several crystalline peptoid polymers in the bulk state (i.e. without solvent) have been previously reported. Rosales et al.32 and Lee et al.33 have studied the effect of side-chain length on unit cell dimensions in peptoid homopolymers, and Sun et al.34,35 have studied the crystal structure of diblock copolypeptoids. Peptoids with appropriate hydrophobic and hydrophilic domains have also been shown to form crystalline nanosheets in aqueous solution.36 These sheets have lengths and widths of at least hundreds of nanometers and have thicknesses on the order of a few nanometers, and are a useful platform for mimicking cell membranes.37 Jin et al.36 have performed X-ray scattering measurements on nanosheets formed from diblock peptoid copolymers and measured the thickness of the dried nanosheets by AFM. Thus far, there has been no complete attempt to relate the crystal structure observed in these studies32–36,38 to molecular structure. In particular, all of these studies present the peptoid backbones in the trans conformation without clear evidence.

In order to systematically probe the relationship between molecular structure and crystal structure, we synthesized a series of peptoid block copolymers to explore the dependence of unit cell dimensions on side-chain length (S) and backbone main-chain length (N). We use small angle and wide angle X-ray scattering (SAXS and WAXS) to examine the lattice dimensions, and molecular dynamics simulations on a representative peptoid polymer to interpret the data at the molecular level. Unexpectedly, we discovered a unifying relationship between molecular structure and crystal structure using S and N as the sole parameters. This relationship, which applies to all of the known peptoid polymer crystals reported in the literature,32–36,38 indicates the prevalence of the cis-backbone conformation in both peptoid bulk phases and in supramolecular nanosheet assemblies.

7

1.3 Results and Discussion:

Peptoid polymers are thought to pack into extended conformations in the bulk phase,33,34 and their backbone configuration and is most often depicted in a trans conformation (Figure 1). We designed a set of acetylated peptoid polymers based on a diblock family consisting of one crystalline, hydrophobic block based on n-alkane side chains, and a non-crystalline polar block consisting of ethyleneoxy side chains.34,39 In this study, we varied N and S independently as indicated in Table 1 (compounds 1 - 4). The parameters a, b, and c in Table 1 are the dimensions of the unit cells determined by X-ray scattering. We borrow the crystal dimension nomenclature (a, b, c) used previously.33,34,40

8

Figure 1. Extended peptoid diblock copolymers, shown in an all trans (left) and an all cis (right) configuration, with side chains R1 and R2. The main-chain length (N) listed in Table 1 is equal to n + m.

9

Table 1. Compiled peptoids, their chemistry, and dimensions from X-ray scattering data

Polymer

Nomenclature

Side chain 1

(R1)

Side chain 2,

if diblock (R2)

N S a (Å) b (Å) c (Å) Author

1 Ac-Ndc9-Nte9 18 10 4.64 52.3 24.6 This work

2 Ac-Ndc9-Nte5 14 10 4.63 41.8 24.6 This work

3 Ac-Ndc9-Nte15 24 10 4.63 66.4 24.6 This work

4 Ac-Nhp9-Nde9 18 7 4.63 51.8 18.6 This work

5 Nia6 6 4 - 20.4 14.6 Rosales32

6 Nia15 15 4 4.60 45.5 14.6 Rosales32

7 Nbu15 15 4 4.53 - 13.4 Rosales

32

8 Nhx15 15 6 4.56 - - Rosales

32

9 Noc15 15 8 4.56 - 17.8 Rosales

32

10 Npe15 15 6 4.5 48.8 17.0 Rosales32

11 c-Nbu120 120 4 - - 13 Lee

33

12 c-Nhx72 72 6 - - 16 Lee

33

13 c-Noc88 88 8 4.6 - 20 Lee

33

14 c-Ndc133 133 10 4.5 - 24 Lee

33

15 c-Nddc133 133 12 4.5 - 27 Lee

33

16 c-Nttdc30 30 14 4.4 - 33 Lee

33

17 Ndc9-Nte27 36 10 4.5 104 25 Sun34

18 Ndc12-Nte21 33 10 4.5 99 25 Sun34

19 Ndc18-Nte18 36 10 4.5 108 25 Sun34

20 Ndc24-Nte12 36 10 4.5 113 25 Sun34

21 NClPe6-Nce6 12 7 4.5 33 18 Jin

36

22 Nce6-NClPe6 12 7 4.5 35 18 Jin36

“c-” means polymerized homogeneously in solution as a cyclic polymer. “-“ means data not found or not included.

10

Peptoids 1 - 4 were synthesized by the solid-phase submonomer method41 and acetylated at their N termini.42 The peptoids were purified by reverse phase high performance liquid chromatography (HPLC) using a cyano column to >90% molecular purity. The synthesis methods and analytical characterization are described in detail in the SI. Small angle and wide angle X-ray scattering (SAXS and WAXS) measurements were performed at ALS beamline 7.3.343 and at SSRL beamline 1-5. An atomistic model of bulk Ac-Ndc9-Nte9 was constructed using molecular dynamics (MD) simulations with the CHARMM-based44 force field for peptoid backbones, MFTOID.45

The modeling of peptoids is necessary to interpret their X-ray scattering data in the context of a specific molecular conformation. To our knowledge, there is no cis backbone conformation model with which to interpret bulk phase peptoid polymer scattering in the literature. We therefore performed two MD simulations of periodic boxes consisting of 288 molecules of compound 1 (Ac-Ndc9-Nte9), one simulation starting from a cis and one starting from a trans backbone conformation. Of the four peptoids we synthesized, we chose to model peptoid 1 because it had been previously reported to form nanosheets.39 The simulations were run for 120 ns in an isothermal-isobaric (NPT) ensemble at 298 K and 1 atm pressure where all three orthogonal box dimensions were allowed to fluctuate independently (details in Appendix 1).

Typical molecular conformations obtained at the end of the simulation for the cis and trans backbone assemblies are shown in Figure 2. We show three views, one perpendicular to the backbone, one along the backbone, and one at a non-orthogonal angle to the backbone. We additionally show finer backbone structure with characteristic distances; the red arrows display the backbone width, and the black arrows display the length of two peptoid monomers along the backbone direction. It is evident that in both simulations, the relaxed molecules remain extended, and roughly planar. They appear to be confined to a board-like box as depicted in Figure 2. As expected, the trans conformation is more extended along the backbone direction than the cis conformation. The Nte block exhibits more disorder than the Ndc block. This is clearer in the case of the trans-backbone; compare views along the backbone in Figure 2.

11

Figure 2. The relaxed molecular conformations cis and trans backbones are displayed. Each molecule is a representative taken from a 288-molecule MD simulation and is shown from three angles. The backbone in each conformation is displayed in higher detail, and the approximate width (red) and twice monomeric length (black) is given.

(B) cis-backbone peptoid molecule(A) trans-backbone peptoid molecule

b

a

cca

b

c c

6.3 Å

3.5 Å

5.7

Å

4.4 Å

12

It is clear that the simulations starting from the cis and trans conformation initial conditions do not relax to the same equilibrium configuration. We posit that this is due to the infrequency of cis-trans isomerization in our simulation, which has slow kinetics compared to simulation time.45,46 We estimated the absolute Gibbs free energies of the bulk phase of the Ac-Ndc9-Nte9 in cis and trans backbone conformations using the two-phase thermodynamics (2PT) method.47–49 With this method, one can calculate equilibrium thermodynamic properties of condensed phases from relatively short MD trajectories without thermodynamic integration. The entropic contribution to the free energy is estimated by using the power spectrum, i.e., the vibrational density of states.47 Our calculations (details in Appendix 1) show that the Gibbs free energy of the relaxed cis backbone conformation is lower than that of the trans backbone conformation: the difference is approximately 158 kJ/mol peptoid (8.8 kJ/mol monomer), suggesting that the relaxed cis backbone conformation is closer to the global free energy minimum.

We used SAXS and WAXS to determine the unit cell dimensions for peptoids 1 through 4. Figure 3A shows SAXS intensity versus magnitude of the scattering vector, q. For Ac-Ndc9-Nte9, we see two peaks q = 0.120, and q = 0.255 Å-1 corresponding to spatial dimensions (2 π / q) of 52.3 and 24.6 Å, respectively. These dimensions are very close to the b dimension of 51.9 Å and c dimension of 24.6 Å calculated from the cis backbone simulation, and less consistent with the b dimension of 57.4 Å and c dimension of 19.6 Å calculated from the trans backbone simulation. We therefore label these scattering peaks in Figure 3A as b (010) and c (001) in accordance to the cis backbone model. The values for N and S for Ac-Ndc9-Nte9 are 18 and 10. For each polymer in Figure 3A, N and S are labeled, and the c peak shifts to lower q with increasing S, while the b peak shifts to lower q with increasing N. These observations are consistent with the molecular model displayed in Figure 2. It appears from these data that the b dimension is independent of S, and the c dimension is independent of N. Figure 3B shows WAXS intensity versus magnitude of the scattering vector, q. For Ac-Ndc9-Nte9 (peptoid 1), we see a peak at q = 1.35 Å-1, corresponding to spatial dimensions of 4.63 Å. This dimension is very close to the a dimension of 4.6 Å obtained from the cis backbone simulation, as opposed to the a dimension of 4.7 Å obtained from the trans backbone simulation. We therefore label this scattering peak in Figure 3A as a (100). The WAXS profiles from peptoids 2 through 4 Figure 3B also contain a peak at the same q. It is evident that a is independent of both N and S, as suggested from the molecular model displayed in Figure 2B. The a, b, and c spacings for these four polymers are listed in Table 1, and were calculated by fitting Gaussian functions to the peaks.

We further identified the higher order peaks present in the WAXS data in Figure 3B, which inform the relationship between a and c. Figure 3C displays the higher order reflections for Ac-Ndc9-Nte9. The peaks found in this regime are due to reflections from the (100), (101), (102), and (103) planes, assuming an angle of 93.6 degrees between a and c directions. The curve in Figure 3C is a fit through the experimental data with the higher order peak locations at q values specified by using the a and c dimensions given in Table 1 and an angle of 93.6 degrees between these dimensions. In Figure 2, we have neglected the difference between 93.6 and 90 degrees. The same fitting procedure was used to analyze the WAXS data from the other three peptoids in Figure

13

4B. The angles between a and c for the other Ndc-containing polymers (S = 10) were approximately 93.6 as well, and angle between a and c for Ac-Nhp9-Nde9 (S = 7) was 96.1 degrees. We posit that these small deviations from orthogonality relieve intermolecular steric repulsions between side-chain –CH3 groups adjacent in the c direction. Higher order reflections corresponding to the c direction, (003) and (005) are also seen in the WAXS data. Taken together, these data indicate that the chains adopt a board-like planar shape with an all-cis backbone conformation, and are packed in lattices with main chains parallel to one another in the a and c dimensions.

14

Figure 3. X-ray scattering measurements for the first four polymers in Table 1, plotted as intensity in arbitrary units versus scattering vector q. The traces are offset vertically for clarity. (A) In the small angle regime, the peaks corresponding to the b and c dimensions can be seen. (B) In the wide angle regime, the peaks corresponding to the a spacing can be seen, along with some higher order reflections. (C) The high-q data and fit for Ac-Ndc9-Nte9, with the peaks indexed and their q positions labeled. The fit is plotted in blue on top of the data, and the background fit is shown in green.

4

5

6

7

8

910

4

2

3

4

5

6

7

8

910

5

Inte

nsity

(a.u

.)

8 9

12

q (Å-1

)

Ac-N

hp9 -N

de9

a

Ac-N

dc9 -N

te9

Ac-N

dc9 -N

te5

Ac-N

dc9 -N

te15

(B)

(100)

(003)

(102)

(103)

(101)

(005)

(C)

2

4

6

810

2

2

4

6

810

3

2

4

6

810

4

Inte

nsity

(a.u

.)

5 6 7 8 9

0.12 3 4 5

q (A-1

)

Kapton

(A)

Ac-Ndc9-Nte5

Ac-Ndc9-Nte

9

Ac-Ndc9-Nte15

Ac-Nhp9 -Nde

9

S = 10N = 18

N = 14

N = 24

N = 18

S = 10

S = 10

S = 7

b (010)c (001)

40x103

35

30

25

Inensity

(a.u

.)

1.651.601.551.501.451.401.35

q (Å-1

)

Background

Fit Data/

(100) (101)

(102)

(103)

1.355 1.395

1.479

1.600

15

In order to explore the generality of this molecular conformation and chain packing motif, we examined 18 additional crystalline polymers previously reported in the literature. We found four other studies which reported X-ray scattering from crystalline peptoid polymers (peptoids 5 through 22, Table 1). In this set, there are 12 homopolymers with hydrocarbon side chains, and 10 diblock copolymers with a hydrophilic block and a crystalline hydrophobic block. Taken together, these polymers cover a wide range of backbone and side-chain lengths: N varies from 6 to 133, while S varies from 4 to 14. We pooled lattice dimensions taken from X-ray scattering data for all 22 compounds in Figure 4, wherein the relationship between the a, b, and c dimensions and N and S are examined. The relationships between molecular structure and crystal structure found in our study of peptoids 1 - 4 appear to be universal to all known peptoid polymer crystals.

We first explored the a dimension of all peptoid polymers plotted as a function of side chain length, S (Figure 4A). The a dimension is not dependent on S or N. The mean value of a is 4.55± 0.02 Å. The origin of the WAXS peaks in peptoid polymers is the subject of some controversy. Sun et al.34 and Jin et al.36 ascribed the (100) peak to the distance between adjacent backbones, consistent with the interpretation presented in this paper. However, Lee et al.33 have ascribed this peak to side-chain crystallization without specifying a particular plane, while Rosales et al.32 assume a hexagonal crystal and assign the WAXS peak corresponding to the spatial dimension of about 4.6 Å to reflections from the (300) planes. Neither Lee et al.33 nor Rosales et al.32 were able to explain the presence of all the observed WAXS peaks. To our knowledge, the unit cell proposed in this study is the only interpretation that is consistent with all the observed WAXS peaks for all S and N (see Figure 3B and 3C). Therefore, when compiling data for Table 1, the lowest q peak in the WAXS regime that cannot be attributed to higher order reflections of b and c dimensions gives the a dimension. We used our simulations of peptoid 1 (Figure 2) to calculate the expected a dimension for cis and trans backbone conformations. This calculation was performed by measuring distances between nitrogen atoms on molecules adjacent in the a direction. These a dimensions are 4.6 Å for the cis backbone and 4.7 Å for the trans backbone, which are close but readily distinguishable. In an important study, Mannige et al.50 performed MD simulations and calculated an a dimension of 4.7 Å in trans backbone peptoids. The a dimension of the polymers in Table 1 is more consistent with cis backbone models than trans backbone models.

We next examined the b dimension of the peptoids polymers as a function of main chain length, N (Figure 4B). It is evident from the scattering data that b is a linear function of N. The linear fit through these data gives b (Å) = [2.98 ± 0.08] N + [0.35 ± 1.70], r2 = 0.99. The b dimension is proportional to backbone length and independent of side-chain length. One can also calculate the relationship between b and N using a molecular model. In our cis simulation, we found the distance between adjacent monomers on the same side of the backbone to be 5.7 Å (see Figure 2B), which corresponds to b being a linear function of 2.9 Å per monomer. In contrast, Mannige et al.50 reported this value to be 3.5 Å per monomer in an all-trans model of an extended peptoid. The dotted line in Figure 4B shows the expected b versus N relationship if this were true. The measured slope of 2.98 Å per monomer in Figure 4B is consistent with

16

the peptoid cis conformation, and not in the trans conformation. The value of the linear fit at N = 18 is 54.0 Å, which is more consistent with the dimension of 51.9 Å calculated from our cis backbone simulation than with the dimension of 57.4 Å calculated from our trans backbone simulation.

Finally, we examined the c dimension of the peptoid polymers as a function of the side chain length, S (Figure 4C). The side-chain length, S, is defined as the number of non-hydrogen atoms contained in an equivalent linear side chain, excluding hydrogen atoms. In side chains that are branched, we exclude the redundant branches, and in side chains containing a phenyl group, the redundant ortho and meta carbon are not counted. The c spacing increases linearly with side-chain length and the fit gives c (Å) = [1.86 ± 0.10] S + [5.5 ± 0.8], r2 = 0.97. It is evident that c is independent of N. Note that the intercept of the fit is 5.5 Å. This length represents the c dimension of a hypothetical peptoid crystal as the side-chain length approaches zero. One can readily estimate the expected intercept from a molecular model. As shown in Figure 2, these intercepts are 4.4 Å for cis and 3.5 Å for trans backbones. For both cases, these values are calculated by projecting the polymer into the plane normal to a and calculating the distance between an alpha carbon side-chain atom and the line connecting the two alpha carbon side-chain atoms on the opposite side of the backbone (see red arrows in Figure 2). The simulations also enable determination of the c dimension for S = 10 (box size in Figure 2): c = 24.6 Å for the cis backbones and c =19.6 Å for the trans backbones. Assuming a linear relationship between c and S, the simulations predict the slope of this relationship to 2.02 Å for cis and 1.61 Å for trans. The dotted line in figure 4C shows the expected relationship for trans backbones. The measured slopes and intercepts, 1.86 and 5.5 Å are much closer to those obtained from the cis simulations.

17

Figure 4. The collection of a, b, and c dimensions taken from the scattering data listed in Table 1. (A) The a dimension of peptoid polymers is always seen around 4.6 Å regardless of the side chain, arranged here by S. (B) The b spacing increases linearly with N. (C) The c spacing increases linearly with S and has an intercept of 5.5 Å. In all plots, the calculated trend for the trans conformation is displayed as a dashed red line for reference. The linear fits (solid black line) in all plots are consistent with the cis conformation but not the trans conformation.

35

30

25

20

15

10

5

0

c (

Å)

1614121086420S

c = 1.86 Å * S + 5.5 Å

(C)

trans

cis

14121086420S

5

4

3

2

1

0

a (

Å)

a = 4.6 Å

(A)cis

trans

This workJinLeeRosalesSun

120

100

80

60

40

20

0

b (

Å)

403020100

N

b = 2.98 Å * N + 0.4 Å

trans

cis

(B)

18

The peptoid diblock copolymers (1-4 and 17-22) all self-assemble to form crystalline nanosheets in water. We used WAXS to examine the nanosheet form of compound 1 and compared it to its structure in the bulk phase to reveal the structural similarities (Figure 5). WAXS traces were obtained from a 6 mg/mL aqueous solution of Ac-Ndc9-Nte9 as well as from dried nanosheets and from a bulk sample. The peak locations in all three samples are identical. Therefore, our conclusion regarding the prevalence of the extended, all-cis conformation applies to both bulk crystals and aqueous diblock nanosheets. In addition, the dependence of a, b, and c on S and N given above also applies to aqueous nanosheets.

19

Figure 5. The WAXS scattering of Ac-Ndc9-Nte9 in bulk, as aqueous nanosheets with a subtraction for water, and as dried nanosheets. Traces are offset vertically for clarity and peaks are labeled. Inset is of aqueous sheets at high q. Peak locations and indexing is consistent between samples.

2

3

4

5

6

104

2

3

4

5

6

105

2

Inte

sity (

a.u

.)

3.02.52.01.51.00.5

q (Å-1

)

1.81.61.41.21.0

q (Å-1

)

(103)(102)

(101)

(100)

(001)

kapton

(100)

(101)

(102)

(103)

Bulk Phase

Dried Sheets

Aqueous Sheets

Aqueous Sheets

Ac-Ndc9-Nte9

(001)

(100)

(101)

(102)

(103)

(001)

(003)

(003)

(005)

(005)

20

These aqueous nanosheets were further examined by cryo-electron microscopy (cryo-EM). Data collected from the cryo-EM of peptoid 1 is displayed in Figure 6 along with data from a corresponding molecular dynamics simulation of a peptoid 1 nanosheet. In Figure 6, the reconstructed image (containing the average of cryo-EM data from within a single grain) displays an extended electron dense backbone in the cis conformation. It also displays the sidechains extended out from the backbone in an angle equivalent to the angle found in the cis-backbone simulation. The agreement between the reconstructed cryo-EM image and the simulated image using the cis-backbone simulation gives further evidence that the crystal dimensions in the nanosheet and in the bulk phase are equivalent. This cryo-EM present study is the first to demonstrate the possibility of atomic-scale imaging of synthetic polymer crystals. The approach developed is robust and can, in principle, be applied to other synthetic polymer crystals. This particular study and Figure 6 are under review for Science, and not contained in the submission for JACS.

21

Figure 6. Reconstructed experimental image from cryo-EM data contained in a single grain of a nanosheet (top left) is reproduced in a simulated image (top right) obtained by extracting electron density from a molecular dynamics simulation.

22

1.4 Conclusion

We present a universal relationship between the molecular structure and crystal structure of peptoids. Our conclusions are based on X-ray scattering and molecular dynamics simulations. The crystal dimensions are simple linear functions of main-chain length N and side-chain length S:

a (Å) = 4.55± 0.02

b (Å) = [2.98 ± 0.08] N + [0.35 ± 1.70]

c (Å) = [1.86 ± 0.10] S + [5.5 ± 0.8]

These relationships, which apply to all twenty two known crystalline peptoid polymers, indicate the prevalence of the cis backbone conformation, despite numerous previous studies which either implicitly or explicitly assumed a trans peptoid backbone.32–

36,38,40,50,51 These relationships reveal a common, extended backbone conformation that displays the side chains in an apposed geometry, creating a planar, board-like shape. Furthermore, in both the bulk phase and in aqueous nanosheets, these polymers pack together with their backbones in close contact and alignment with one another. This suggests that the all-cis peptoid backbone itself is capable of forming multiple weak inter-chain attractive interactions, likely the result of CH-O hydrogen bonding52,53 or amide dipole interactions.54 This new structural motif can be used to design broad classes of assemblies which have specific unit cell sizes, functional group densities, or nanosheet thicknesses, based upon a specific backbone conformation and packing preference.

1.5 Acknowledgement:

The Soft Matter Electron Microscopy Program (KC11BN), the Molecular Foundry and Beamline 7.3.3 of the Advanced Light Source at Lawrence Berkeley National Laboratory (LBNL) are supported by the Director of the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work at the Molecular Foundry was supported by a user project and access to computational resources administered by LBNL’s High Performance Computing Services Group. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. We gratefully acknowledge Dr. John R. Edison and Dr. Stephen Whitelam for their discussions on molecular model building. This chapter is under review for publication in JACS.

23

Chapter 2 – Fermentation of Hydrolysate Detoxified by Pervaporation through Block Copolymer Membranes

*This chapter was reported in Green Chemistry55

2.1 Summary:

The large-scale use of lignocellulosic hydrolysate as a fermentation broth has been impeded due to its high concentration of organic inhibitors to fermentation. In this study, pervaporation with polystyrene-block-polydimethylsiloxane-block-polystyrene (SDS) block copolymer membranes was shown to be an effective method for separating volatile inhibitors from dilute acid pretreated hydrolysate, thus detoxifying hydrolysate for subsequent fermentation. We report the separation of inhibitors from hydrolysate thermodynamically and quantitatively by detailing their concentrations in the hydrolysate before and after detoxification by pervaporation. Specifically, we report >99% removal of furfural and 26% removal of acetic acid with this method. Additionally, we quantitatively report that the membrane is selective for organic inhibitor compounds over water, despite water’s smaller molecular size. Because its inhibitors were removed but its sugars left intact, pervaporation-detoxified hydrolysate was suitable for fermentation. In our fermentation experiments, Saccharomyces cerevisiae strain SA-1 consumed the glucose in pervaporation-detoxified hydrolysate, producing ethanol. In contrast, under the same conditions, a control hydrolysate was unsuitable for fermentation; no ethanol was produced and no glucose was consumed. This work demonstrates progress toward economical lignocellulosic hydrolysate fermentation.

24

2.2 Background:

Lignocellulosic feedstocks are a potential large-scale source of renewable energy, transportation fuel, and organic chemicals.56 Because lignocellulosic biomass is the most abundantly available source of biomass, its feedstocks are a prospect to replace fossil fuels as a source for chemicals and energy.57 Many plant species can be used as lignocellulosic feedstocks, but for the purpose of this study, we choose Miscanthus × giganteus, a plant species in focus of research at our institute. Miscanthus × giganteus is a tall perennial grass hybrid bred for its extraordinary capacity to fix carbon. When compared to corn, Miscanthus produces more biomass per acre, requires less fertilization and water input, lives through the year, and does not compete as a food crop.58,59

Lignocellulosic feedstocks are typically processed in two steps: First, cellulose and hemicellulose are depolymerized to soluble sugars. Second, these sugars are converted by fermentation into high value products such as alcohols.60 In this study, common methods for pretreatment and fermentation are used: partial depolymerization of Miscanthus is achieved with heat and dilute acid pretreatment, and yeast is used to produce ethanol by fermentation.61,62 The product of the depolymerization step is called hydrolysate. Unfortunately, the depolymerization step also produces toxic inhibitors such as acids, furans, and phenols.63,64 These inhibitors, through a number of biological mechanisms both known and unknown,65,66 reduce overall ethanol yield, retard ethanol production, and even prevent fermentation.67 In fact, the inhibitors exhibit such acute toxicity that additional detoxification steps are required.64 Techniques for detoxification of lignocellulosic hyrdrolysates include but are not limited to alkali68 or other chemical addition,69 enzymatic treatment,70 liquid-liquid extraction,71 sorption,72 and ion exchange.73 However, commonly studied detoxification methods typically require additional inputs or separation processes, for example, using inorganic lime for detoxification requires the additional separation of a highly alkaline solid phase.74,75

In this study, we explored the feasibility of pervaporation as a means to remove inhibitors. Pervaporation is the combination of two words, permeation and evaporation. These two combined phenomena can be accomplished using a polymer membrane. With dense membranes, i.e. non-porous membranes, the pervaporated species sorb onto the membrane surface, diffuse across the membrane, and evaporate into an evacuated vessel maintained at low pressure. Pervaporation is driven by gradients in chemical potentials. Volatile species are thermodynamically favored to evaporate into the evacuated vessel, and these species are pervaporated at different rates due to differences in sorption equilibriums and diffusion kinetics.76 The starting material is called the feed, the pervaporated material is called the permeate, and the remaining non-pervaporated material is called the retentate. Membrane pervaporation has the potential to reduce the energy required for biofuel purification and to increase the efficiency of biofuel production.20

Previously, pervaporation has been studied as a method for the separation of water and furfural for green chemistry applications.77 Pervaporation has also been used as part of a joint hydrolysate detoxification process in which pervaporation removed furfural, and

25

the enzyme laccase removed phenolic compounds, a process which ultimately improved fermentation.78 However, this is the first work wherein pervaporation been used as the sole hydrolysate detoxification method and in-depth quantification of inhibitor removal, thermodynamic driving force analysis, and subsequent fermentation analysis are detailed.

We performed pervaporation on a Miscanthus hydrolysate using a microphase separated polystyrene-block-polydimethylsiloxane-block-polystyrene (SDS) copolymer membrane. Microphase separation forms co-continuous polystyrene (PS) and polydimethylsiloxane (PDMS) domains. Our objective was to remove the inhibitors from hydrolysate by pervaporation while leaving fermentable sugars intact. The PDMS domains have good transport properties for organic species (refer to patent WO 2013/071174 A2, fig. 32-34),79 as PDMS is a good transporter of ethanol and other organic compounds.80 The PS domains are rigid and provide the membrane with structural integrity. SDS copolymer membranes are more selective for organic compounds relative to commercially available cross-linked PDMS membranes.81 The efficacy of the pervaporation-based process was demonstrated by fermentation, using the pervaporation-detoxified hydrolysate and Saccharomyces cerevisiae strain SA-1, a robust strain isolated in 1993 from a Brazilian Copersucar industrial plant which produced bioethanol from sugar cane substrates.82

2.3 Methods:

2.3.1 Membrane Properties and Testing

Polymer Source (Montreal) provided the SDS copolymer, which was used as received. The product was labeled with the following properties: number averaged molecular weight of the middle PDMS block was 104 kg/mol, number averaged molecular weight of each end PS block was 22 kg/mol, polydispersity index of the copolymer was 1.3, and 86 weight% of the sample was the triblock copolymer (the remainder was mainly diblock copolymer).

In 20 mL cyclohexane (Sigma Aldrich, used as received), 1 g SDS was stirred and dissolved. This solution was spin casted on a Biomax 50 PBQK 20 cm x 20 cm nanofiltration support membrane, and cut to fit our pervaporation cells. The pervaporation membrane was formed by casting three separate aliquots of 7 mL solution at 1300 rpm onto the support with one minute’s time for drying between aliquot applications. The membrane was then placed in a vacuum oven at room temperature to remove excess solvent. The average thickness for SDS membranes formed by this method was 10 μm.

The pervaporation flux of deionized water through the membranes thus obtained was measured at 70 °C and compared with that obtained using an unsupported SDS membrane of 120 μm micrometer-measured thickness. The thicknesses of the two supported membranes used in this study were determined to be 8 and 12 μm from these measurements by assuming that water permeability is independent of membrane

26

thickness (see Equation 1). The fluxes were 4.7 and 3.1 g/hr for the 8 and 12 μm thick membranes.

2.3.2 Dilute Acid Hydrolysis

Hydrolysate was obtained from the National Renewable Energy Lab (NREL) with the following conditions: Miscanthus × giganteus plant material (around 1 inch size) was pretreated with 1.5 %(w/w) sulfuric acid at a 25 %(w/w) biomass loading at 190 °C for approximately 1 minute, then the pressure was rapidly released. The liquid phase after filtration is referred to as hydrolysate. In some industrial processes, hydrolysate is subjected to enzymatic hydrolysis. In this study, the hydrolysate was used as received. Upon receipt, the hydrolysate thus obtained was stored at -20 °C. Before pervaporation, the hydrolysate was thawed, centrifuged at 2000 g for 10 min, filtered through a Whatman 2V folded filter paper, and its pH was increased to 3.0 by adding KOH. The resulting hydrolysate contains a complex mixture of inhibitory organic compounds (see Appendix 2).

2.3.3 Pervaporation and Analysis

Pervaporation of hydrolysate was performed in a laboratory bench test unit built by Sulzer Chemtech, Germany and described previously.83 The two membranes were used in parallel, contributing a permeation area of 37 cm2 apiece. The SDS membranes were held inside a circular cell restrained with an O-ring. The temperature

of the feed was controlled in the range of 70 ± 1 oC. The experiment began with 530 mL

of hydrolysate in the feed tank. A vacuum of < 3 mbar was applied using a vacuum pump on the permeate side of the membranes (Welch, model 2014). Permeates were condensed in a cold trap (ChemGlass CG-4516-02) cooled with liquid nitrogen. Permeate samples were weighed to determine the mass permeated through the membrane during the experiment. The hydrolysate retentate retained in the feed tank after 24 h of pervaporation was used in the fermentation experiments described below and was called pervaporation-detoxified hydrolysate. The SDS membranes were then replaced by impermeable non-porous Teflon membranes. A control hydrolysate sample was prepared starting with a fresh feed of 530 mL of pH 3 hydrolysate in the feed tank and pumping it through the pervaporation apparatus for 24 h at 70 °C. No pervaporation occurred in this control experiment.

Figure 1 depicts the schematic of the pervaporation apparatus. In pervaporation and control experiments, feed samples were collected approximately every 90 minutes. In the pervaporation experiment, samples were also collected from the permeate cold trap.

27

Figure 1. Schematic diagram of the pervaporation apparatus. Hydrolysate is pumped across a membrane module and the retentate is returned the feed tank and mixed. The permeated species are collected in a cold trap under vacuum.

28

Glucose, xylose, acetate, ethanol, 5-hydroxyfurfural, and furfural were analyzed using a high pressure liquid chromatography (HPLC) method84 on a system (Agilent Technologies, Santa Clara, CA, USA) equipped with a refractive index detector (RID). Samples were injected onto a 300 mm x 7.8 mm Aminex HPX-87H column (BioRad, Richmond, CA, USA) with a guard column of the same material. Elution was performed at 50 °C with 5 mM sulfuric acid at a flow rate of 0.6 mL/min. We chose glucose as the normalization standard because it has a high concentration and does not degrade under experimental conditions, as observed in our repeated preliminary studies. In Miscanthus hydrolysate, galactose and mannose are also present, but in lower concentration. These sugars co-elute with xylose on the HPLC column used and were therefore the three sugars were quantified together as “xylose”.

Gas chromatography and mass spectroscopy (GCMS) analysis of the compounds was performed as described previously.85 Briefly, 1 mL of hydrolysate was spiked with the internal standard 4-isopropylphenol and the mixture extracted four times, each with 0.5 mL of ethyl acetate. The ethyl acetate phases were combined and mixed dried over sodium sulfate. An aliquot was derivatized with N,O-bis(trimethylsilyl)-trifluoroacetamide (BSTFA) containing 1 % trimethylchlorosilane (TMCS). 1 µL was injected in splitless mode onto a VF5-ms capillary column (30 m x 0.25 mm x 0.25 µm, Agilent, Santa Clara). An Agilent 7890A gas chromatograph coupled to an Agilent 5975C single quadrupole mass spectrometer was used for analysis.

Organic acids were quantified using liquid chromatography and mass spectroscopy (LCMS) (QTOF, Agilent Technologies, Santa Clara, CA, USA) in negative ion mode.

2.3.4 Thermodynamic Properties Calculation

The molar flux of pervaporated species i, iJ , is given by Wijmans and Baker,76

sm

molpypx

t

PJ

2pi

sat

iiii

i ])[( , (1)

where iP is the permeability of the membrane, t is the membrane thickness (SDS

copolymer only), ix is the feed mole fraction, i is the activity coefficient, sat

ip is the

saturated vapor pressure, iy is the permeate mole fraction and pp is the total permeate

pressure. In our experiments we approximated the product pi py to zero because of the

low permeate pressure (<3 mbar). In our experiments, the water permeability was calculated to be 5.1*10-12 mol m / m2 Pa s.

The separation factor for pervaporation, pervap , is a measure of the enrichment of

species i compared to another species, in our case, water.

whih

wpip

pervapcc

cc

/

/ , (2)

29

where c denotes concentration (g/L), subscript w denotes water, subscript p denotes

permeate, and subscript h denotes hydrolysate feed. E.g. ipc denotes the concentration

of species i found in the permeate, detected by HPLC, LCMS, or GCMS.

The separation factor for evaporation, evap , is a measure of the enrichment of species i

compared to water due to evaporation alone.

sat

w

i

wi

wi

evapp

up

xx

pp sat

ii

/

/ , (3)

where ip is the pressure of component i in the gas phase and

wp is the pressure of

water in the gas phase. Aqueous binary activity coefficients, i , were obtained from

Gmehling et al.,86 and the fraction of undissociated acid molecules, iu , were obtained

from Green and Perry,87 i.e., AHHA ; AHAHAui / . Ideally,

multicomponent thermodynamic parameters should be used to model a multicomponent vapor-liquid equilibrium. However, there is little multicomponent thermodynamic data in the literature. Because the hydrolysate solution contains more than 90% water, we expect water-water and water-solute interactions to dominate. We thus expect the binary thermodynamics to provide a reasonable starting point for modeling the vapor-liquid equilibrium of hydrolysate.

The membrane selectivity, mem , is a measure of the enrichment of species i compared

to water due to permeation through the membrane alone.

,/

/

sat

ii whih

wpip

i

sat

w

evap

pervap

w

imem

cc

cc

up

p

P

P

(4)

where wP is the permeability of water. Implicit in this analysis is the assumption that the

binary aqueous parameters i and iu are applicable in our multicomponent hydrolysate

system.

2.3.5 Yeast Culture and Fermentation

The yeast strain Saccharomyces cerevisiae SA-1 was provided by the Yeast Biochemistry and Technology Laboratory, Biological Science Department, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Brazil. Stock cultures were grown at 30 °C for 3 days in YPD medium (10 g/L yeast extract, 20 g/L peptone, 20 g/L glucose) supplemented with 20 g/L agar for solid culture. Two biologically duplicate colonies were grown at 30 °C at 200 rpm in an Innova 2000 platform shaker overnight using 10 mL of synthetic complete media (SC-80). SC-80 contains 80 g/L glucose, 2 g/L dropout mix (US Biological), 6.7 g/L yeast nitrogen base (Becton, Dickinson and Company), 19.5 g/L MES buffer, and a small amount of KOH to adjust the pH to 5.5. After overnight growth, cells were harvested by centrifugation.

30

Fermentation was performed in 25 mL Hungate bottles under anaerobic conditions. The pervaporation-detoxified hydrolysate fermentation broth contained pervaporation-detoxified hydrolysate, water to match the amount removed by pervaporation, and harvested yeast cells, and had an initial OD600 of 0.3. The control hydrolysate fermentation broth contained control hydrolysate and harvested yeast cells, and had an initial OD600 of 0.3. The OD600 of values of 0.3 included background subtraction to account for hydrolysate absorption. The fermentation was performed at 34 °C and 200 rpm using a Certomat BS-1 Sartorious shaker for time = 0-50 h and room temperature with no shaking for time = 50-312 h. Additionally, control and pervaporation-detoxified hydrolysate fermentations were performed under these same conditions at 34°C and 200 rpm for time = 0-119.5 h, with the addition of the components of SC-80 to match SC-80 levels.

During fermentation, OD600, ethanol concentration, and glucose concentration were measured under aseptic conditions. 50 μL of fermentation broth were used to measure the OD600 using a Genesys 20 Visible Spectrophotometer. Simultaneously, 250 μL were centrifuged, filtered, and analyzed for glucose and ethanol concentrations using the HPLC system described above.

2.4 Results and Discussion

Hydrolysate is a complex mixture comprising a multitude of organic compounds. Our HPLC, GCMS, and LCMS methods detect around 100 compounds thereof. The concentrations of these compounds in the hydrolysate before and after pervaporation are given in Appendix 2. The end alcohol content of the subsequent fermentation depends on its starting sugar concentration. The three sugars quantified in our hydrolysate are glucose, xylose, and arabinose. The short list of our focus inhibitors in hydrolysate is acetic acid, formic acid, furfural, and guaiacol. This short list contains at least one member of each of the main inhibitor classes: acids, furans, and phenolics. Our objective is to use pervaporation to remove the inhibitors without affecting the sugar and then determine the biological consequences of detoxification by fermentation using the Saccharomyces cerevisiae strain SA-1.

The effect of our 70 °C 24 h pervaporation treatment on hydrolysate is summarized in Figure 2 and Table I. The abscissa in Figure 2 gives the initial concentrations of the compounds of interest. The blue bars display the mass percent of these compounds removed by pervaporation. Also shown in Figure 2 are changes in the concentration of the compounds of interest due to heat treatment alone, as determined in our control experiment, wherein the hydrolysate is heated to 70 °C and pumped through the system for 24 h. These results are represented by the red bars in Figure 2. The change in glucose concentration is identically zero because we use this component as an internal standard for our concentration determinations. Slight decreases in the normalized masses of xylose (2%) and arabinose (8%) are seen during pervaporation (Figure 2). Similar decreases are seen in the control experiment. We attribute the observed consumption of sugars to reactions that occur spontaneously in hydrolysate at 70 °C. To a good approximation, the pervaporation process leaves the sugars intact.

31

Figure 2. Measured removal of inhibitors and sugars by treatment versus their initial concentration. Results of pervaporation treatment are shown with blue bars and results of control treatment are shown with red bars. Glucose is used as an internal standard and its removal is zero by definition. A pervaporation detoxification significantly removes inhibitors without the removal of sugars.

32

Table I

Starting concentrations of select compounds in hydrolysate and

their mass % retained in the hydrolysate after 24 hours of

pervaporation or control treatment.

Species cihi

(g/L)

Mass%

Pervap

Mass%

Control

Xylose 45 98 97

Glucose 19 100 100

Acetic Acid 8.7 73 106

Arabinose 5.1 92 96

Formic Acid 2.5 47 46

Furfural 0.80 0 88

Guaiacol 0.0018 0 87

cihi is the species i concentration initially in the hydrolysate feed

Glucose is used as internal standard

33

It is evident in Figure 2 that guaiacol and furfural, both uncharged inhibitors, are efficiently removed by pervaporation. After pervaporation-detoxification, the concentrations of these species in the hydrolysate are not detected. About 10% of guaiacol and furfural are consumed in the control experiment, indicating that some of the removal of these species during experimentation is attributable to chemical reactions. The amounts of formic acid removed in the control and pervaporation experiments are similar. This suggests that pervaporation has a limited effect on formic acid. The concentration of acetic acid increases slightly in the control experiment. The observed 27% removal of acetic acid by pervaporation treatment (Figure 2) is attributed to permeation and evaporation across the membrane.

Table 2 shows the concentrations of the inhibitors in the hydrolysate and the permeate at the beginning of pervaporation (time = 0-2 h) and end of pervaporation (time = 22-24 h). The water concentration in the hydrolysate (cih) decreases as pervaporation proceeds, starting at 945 g/L and ending at 906 g/L. The water concentration in the permate (cip) increases as pervaporation proceeds, starting at 985 g/L and ending at 991 g/L. The reason for this will be made clear shortly. The main inhibitor removed by pervaporation is furfural. Pervaporation of hydrolysate containing 0.69 g/L of furfural results in an aqueous permeate with 6.3 g/L of furfural. From these measurements, the calculated pervaporation separation factor, βpervap, of furfural in our SDS membrane is 8.6. This is consistent with published literature on furfural pervaporation.79 Guaiacol is also effectively removed by pervaporation, βpervap

= 11. Acetic acid is found in the hydrolysate and permeate in the beginning and the end of pervaporation. The pervaporation separation factors obtained in the beginning and at the end are similar (0.77 and 0.73). Our βpervap data for guaiacol, furfural, and acetic acid are similar to those found in previous literature.77,88 Formic acid is found in permeate at the end of pervaporation with a relatively low pervaporation separation factor, βpervap =0.11. By the end of the pervaporation (time = 22-24 h), furfural and guaiacol have completely permeated are neither found in the permeate nor the hydrolysate samples taken at this time point. The concentration of water in the final permeate sample (time t=22-24 h) is higher because it contains no furfural (the hydrolysate also contains no furfural).

The vapor-liquid equilibrium data of the compounds of interest listed in Table II enable determination of the driving forces and membrane properties of the separations described above. The separation factors due to evaporation alone, βevap, are listed in

Table II (we were unable to obtain i for guaiacol). It is perhaps worth noting that

evaporation of furfural is driven by its large activity coefficient ( i =85); the saturation

pressure of furfural relative to that of water is only 0.096. The membrane selectivity factors for acetic acid and furfural are in the vicinity of 1.1. These membrane selectivity values are remarkably similar to values reported for ethanol transport through SDS membranes.89 Upon first impression, membrane selectivity values of approximately 1.1 may seem unremarkable. However, because water has a much smaller molecular size than furfural or ethanol, pervaporation membranes selective for organic species must overcome the quicker diffusion associated with smaller molecules. In addition to PDMS, important examples of the chemistry of organic-selective polymer membranes include poly[1-(trimethylsilyl)-1-propyne] (PTMSP), and polymers of intrinsic microporosity (PIMs) such as PIM-1 and PIM-7.90

34

35

Fermentation experiments were performed on pervaporation-detoxified and control hydrolysates. The results are shown in Figure 3. The pHs of the hydrolysates were adjusted to 5.5 using KOH. Because water is permeated during pervaporation, water was added to the pervaporation sample until the glucose levels in the pervaporation sample and control sample were matched. Saccharomyces cerevisiae SA-1 cells were added to the treated hydrolysate samples at time = 0. The concentrations of glucose and ethanol were monitored for 312 h and the results are shown in Figures 3a and 3b. (The small difference in initial glucose concentration is due to the water added with the aqueous cell cultures.) In the case of pervaporation-detoxified hydrolysate, both Figures 3a and 3b show evidence for slow conversion of glucose into ethanol. In contrast, the change in glucose (Figure 3a) and ethanol (Figure 3b) concentrations in the control hydrolysate over time = 0-312 h are negligible. The time dependence of OD600 is shown in Figure 3c. In the pervaporation-detoxified hydrolysate we see an increase in OD600 (Figure 3c) accompanying ethanol production (Figure 3b). In the control hydrolysate, OD600 approximately doubled in the first 50 h, which we attribute to one cellular division. However, the inhibitors in the control hydrolysate interfere with yeast growth processes. There was no sustained growth (Figure 3c) in the control hydrolysate and thus no consumption of glucose (Figure 3a) or production of ethanol (Figure 3b). The data show that pervaporation-detoxified hydrolysate is suitable for fermentation and ethanol production. All the glucose is consumed, converted primarily into ethanol. To our knowledge, this is the first experiment demonstrating that the detoxification of pretreated hydrolysate by pervaporation alone is sufficient for ethanol production by fermentation with yeast.

36

Figure 3. Fermentation data of the pervaporation-detoxified hydrolysate and control hydrolysate versus time are shown. (a) Glucose and (b) ethanol concentrations were measured by HPLC and (c) optical density was measured at 600 nm (OD600). (a-c) The data from three fermentation experiments are shown with curves to guide the eye.

37

An additional set of fermentation experiments were performed on pervaporation-detoxified and control hydrolysates, as shown in Figure 4. In this fermentation, SC-80 nutrient components were added before fermentation. This was done because Saccharomyces cerevisiae SA-1 cells flourish in SC-80 nutrient. Again, the pHs of the hydrolysates were adjusted to 5.5 using KOH, water was added to the pervaporation sample until the glucose levels in the pervaporation sample and control sample were matched, and Saccharomyces cerevisiae SA-1 cells were added to the treated hydrolysate samples at time = 0. The concentrations of glucose and ethanol were monitored for 119.5 h and the results are shown in Figures 4a and 4b. Pervaporation-treated hydrolysate fermentation shows glucose (Figure 4a) conversion into ethanol (Figure 4b). In contrast, the change in glucose (Figure 4a) and ethanol (Figure 4b) concentrations in the control hydrolysate over time = 0-119.5 h is negligible. In the pervaporation-detoxified hydrolysate, a steady increase in OD600 (Figure 4c) accompanies ethanol production (Figure 4b). The data demonstrate that pervaporation-detoxified hydrolysate is suitable for ethanol production by fermentation. With the addition of SC-80 components to the pervaporation-detoxified hydrolysate, fermentation proceeds quicker and produces more ethanol. Despite the wealth of nutrients provided by the SC-80 components, the inhibitors in the control hydrolysate prevent its fermentation.

38

Figure 4. SC-80 nutrients are added to Pervaporation-detoxified hydrolysate and control hydrolysate and these mixtures are fermented. In the fermentation broths, (a) glucose concentration, (b) ethanol concentration, and (c) optical density at 600 nm (OD600) were measured and are plotted versus time. (a-c) The data from two fermentation experiments are shown with curves to guide the eye.

39

2.5 Conclusion

In this study, pervaporation with a polystyrene-block-polydimethylsiloxane-block-polystyrene membrane has demonstrated the ability to remove inhibitors from Miscanthus × giganteus dilute acid pretreated lignocellulosic hydrolysate, while leaving sugars intact. The thermodynamics for separation are elucidated, showing the membrane’s selectivity for furfural and acetic acid over water. Our in-depth thermodynamic analysis allows for future calculation and comparison of hydrolysate separation techniques. Furthermore, the pervaporation-treated hydrolysates are suitable for ethanol fermentation with Saccharomyces cerevisiae strain SA-1 with and without further nutrient addition. These results indicate that pervaporation is a viable approach for hydrolysate detoxification in an industrial bioethanol production process.

2.6 Acknowledgement

This work was funded by the Energy Biosciences Institute. Hydrolysate was provided by the National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, a national laboratory of the U.S. Department of Energy managed by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy under Contract Number DE-AC36-08GO28308. This chapter was reported in Green Chemistry.55

40

Conclusion

The thrust of this work was to leverage the self-assembly of block copolymers for fundamental studies on polymer conformations and for practical applications which require materials with two distinct properties. We used peptoid diblock copolymers to identify a motif common to all bulk phase crystalline peptoid polymers. We used block copolymers to impart two properties in a material effective for the separation of aqueous organic compounds. These studies were both facilitated by the formation of microdomains within a block copolymer material.

I found that all peptoid polymers have an extended cis backbone conformation with crystal dimensions predictable by simple linear functions of main-chain and side-chain length. This new structural motif can be used to design broad classes of assemblies - including microdomains, nanosheets, and nanotubes - which have specific unit cell sizes, functional group densities, or nanosheet thicknesses, based upon a specific backbone conformation and packing preference. We suspect that this conformation is stabilized due to weak hydrogen bonding to of hydrogens on sidechain alpha carbons to backbone carbonyl oxygen atoms, and research on this mechanism could yield new classes of biomimetic polymers.

It is interesting to note that in most traditional lamellar diblock copolymer systems, the microdomain spacing has approximately a 0.64 power law with degree of polymerization, and this has been measured precisely with ideal amorphous polymers.91 This power law coefficient is in agreement with the theory of microdomain structure, wherein a 0.643 power law was reported.5 In peptoid systems, a linear trend (power law of 1.00) is observed, speaking to the high degree of order. The microdomain structure theory does not apply well to peptoid systems because of their high degree of crystallinity, low molecular weight, and intermolecular interactions. However, researchers pursuing precise domain spacings or interesting physicals of microphase separation may take interest in this system.

I used block copolymers to impart two properties – mechanical strength and solution/diffusion of VOCs – in a material effective for biofuel-relevant separation. Pervaporation with a SDS membrane demonstrated the ability to remove inhibitors from Miscanthus × giganteus hydrolysate, while leaving sugars intact. The resulting pervaporation-treated hydrolysates were suitable for ethanol fermentation with Saccharomyces cerevisiae, indicate that pervaporation is a viable approach for bioethanol production processes. Our follow-up work (published elsewhere) also demonstrates that block copolymer membranes are effective for the pervaporation of other VOCs from their binary mixtures in water,24 and further research may reveal that these separations are also viable in a true industrial setting with multicomponent feeds.

Taken in total, our studies demonstrate that block copolymers are an effective tool for both fundamental molecular engineering studies and chemical engineering design.

41

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Appendix 1 - Supporting information for Chapter 1

A1.1 Peptoid Synthesis

The mPEG amine submonomers for Nde (mPEG2-NH2) were purchased from PurePEG (98% purity), and for Nte (mPEG3-NH2) were purchased from Peptide Solutions, Inc. (98% purity). Linear alkyl amines were purchased from TCI (>98% purity). All submonomers were used without further purification. Automated solid-phase submonomer synthesis of the polypeptoids was performed on a Symphony X peptide synthesizer at a scale of 200 mg Rink amide resin (0.64 mmol/g) following published procedures.41 Displacement reactions were performed at amine concentrations of 1 M in N,N’-dimethylformamide (DMF) for 30 min at room temperature. Bromoacetylation reactions were performed with bromoacetic acid and N,N'-diisopropylcarbodiimide (both at 0.8 M in DMF) for 20 min at room temperature.

Peptoids were cleaved from resin by treating with an acid cleavage cocktail of dichloromethane/trifluoroacetic acid/water (50/45/5, v/v) for 10 min at room temperature. After resin filtration and washing, the cocktail was evaporated, and the peptoids were lyophilized from acetonitrile/water (1:1, v/v).

Acetylation was performed on the crude, cleaved peptoid (~200 mg). The peptoid was dissolved in 2 mL DMF/tetrahydrofuran (THF) (1:5, v/v), followed by the addition of 100 µL acetic anhydride and 100 µL pyridine, and allowed to stir at room temperature for 20 minutes. The volatiles were then removed by evaporation, and the peptoid lyophilized from acetonitrile/water (1:1, v/v).

Purification was performed on a reverse phase HPLC Waters prep system using a XSelect HSS cyano column (5 µm, 18 x 150 mm). A linear, binary elution gradient was used (where solvent A was 10% isopropyl alcohol (IPA) in water, and solvent B was 10% IPA in ACN), from 50 – 95% B in 20 minutes at a flow rate of 20 mL/min). Note that TFA was not included in the buffer system, since the peptoid N-acetyl group exhibits some acid lability.42 50 mg of peptoid dissolved in 3 mL of ACN/water (1:1, v/v), was used per injection. The fractions were collected and analyzed by MALDI using a superDHB matrix. The collected fractions containing pure product were combined, evaporated, and lyophilized from ACN/water (1:1, v/v) to afford a fluffy white powder. The product was then analyzed by reverse phase HPLC equipped with an analytical cyano column and MicroTOF electrospray mass spectrometer. 50 mg of final product was obtained in most cases.

A1.2 Nanosheet Formation

To form nanosheets, each peptoid we synthesized was dissolved in 50/50 v/v tetrahydrofuran (THF)/water at a concentration of 2 mg/mL. The THF was evaporated under a vacuum of 500 torr at room temperature, over a period of 24 hours, leaving behind the aqueous peptoid solution at a concentration of about 6 mg/mL. The

49

solutions turned turbid, consistent with the formation of nanosheets, which was verified by atomic force microscopy. WAXS measurements were performed as described above on solutions encapsulated in Kapton windows. The nanosheet samples were not annealed. Dry nanosheets were obtained by putting drops of aqueous nanosheet solution on Kapton and drying the sample in a fume hood.

A1.3 X-Ray Scattering

Wide angle X-ray scattering (WAXS) measurements were performed at ALS beamline 7.3.3.43 Small angle X-ray scattering (SAXS) measurements were performed at ALS beamline 7.3.3 and SSRL beamline 1-5. Prior to measurement, lyophilized peptoids were placed between Kapton windows separated by a rubber gasket spacer, annealed at 125 °C for half an hour, and cooled slowly to remove thermal history. All four diblock peptoids synthesized for this study are known to be isotropic above 100 °C.35

A1.4 Molecular Dynamics Simulation

Based on the experimentally observed X-ray scattering, we prepared a number of low energy, ordered bulk phase peptoid configurations as initial structures for our molecular dynamics simulations. The simulations were performed with the simulation package NAMD.92 To explore the underlying conformations of the peptoids within the bulk-phase, we used the polymer Ac-Ndc9-Nte9 as our model. We considered a bulk phase consisting of all-cis and all-trans conformations of this polymer, expecting one of these conformations to be dominant due to free energy differences. We also considered that the polymer molecules have a polar N terminal block and a hydrophobic C terminal block, meaning each molecule has a backbone directionality. With this directionality in mind, we considered that each molecule’s neighbor in the a and c direction could either be assembled parallel or antiparallel to it. The structure is mainly stabilized by the strong interaction in the close packing direction (a direction). Thus, we quickly found that bulk phases in which adjacent molecules are arranged antiparallel in the a direction are disfavored due to higher internal energy configurations. We completed simulations of bulk phases with parallel-parallel assemblies (designated PP), and with parallel assembly along the a direction but antiparallel along the c direction (designated PA). We note that the physical Ac-Ndc9-Nte9 material will likely contain contributions from both PP and PA assemblies.

For each simulation, a periodic box consisting of 288 polymers (16 along a, 3 along b, and 6 along c-axis) was considered. Each system is equilibrated for at least 100 ns of molecular dynamics simulations in the isothermal-isobaric (NPT) ensemble at 298 K and 1 atm pressure where all the three orthogonal box dimensions were allowed to fluctuate independently. The constant pressure is maintained using the Nosé-Hoover Langevin piston method93,94 with an oscillation period of 100 fs and decay time of 50 fs. All the bonds involving hydrogen atoms were kept rigid (using SHAKE95). Particle-mesh Ewald summations96 were used to evaluate long-range electrostatic interactions with a

50

real-space cut-off of 12 Å. Van der Waals interactions were calculated up to a distance of 12 Å , with a smoothing function applied from 10 Å to 12 Å to ensure vanishing energies and forces at the cutoff. All the systems were simulated using a leap-frog integration algorithm with a 2-fs timestep. The relaxation for each model is checked by ensuring that the final configuration is independent of the initial preparations. The data was recorded in the steady state (energy and box dimensions fluctuate around the respective mean values). The equilibrated configuration (in the NPT ensemble) was again evolved in the NVT (constant number of particles, volume, and temperature) ensemble to estimate the internal energy. The absolute value of Gibbs free energies were determined using the Two Phase Thermodynamics (2PT) method as discussed in Chapter 1. Here we estimate the absolute entropy and quantum (zero point energy and heat capacity) corrections to the enthalpy from the vibrational density of states function (vDOS), determined from a Fourier transform for the atomic velocity autocorrelation function, i.e., the power spectrum. When determining the vDOS, we ran an additional 4 ps canonical (constant particle, constant temperature or NVT) simulation, saving snapshots of the system (atomic velocities and coordinates) every 1 fs. In the 2PT formalism, each discrete frequency in the vDOS is modeled as a quantum harmonic oscillator. The value of internal energy over time is shown in Figure 1. The cis backbone conformations have much lower energy than the trans-backbone conformation. The two models (PP and PA) of the cis conformation are approximately energetically degenerate.

Using the equilibrated configurations obtained from MD simulations, we calculated the histograms of a, b, and c spacings for Ac-Ndc9-Nte9. The spacings calculated from MD simulations of cis-PP and cis-PA are in good agreement with spacings found with X-ray scattering measurements, as seen in Figure S1. The a distance was calculated by measuring the distance between each nitrogen atom and the nearest nitrogen atom on an adjacent molecule in the a direction. The b dimension was calculated by measuring the distance between each carbon atom at the N terminus and the carbon atom on an adjacent molecule in the b direction. The c dimension was calculated by measuring the distance between each carbon atom on the backbone and the nearest carbon atom on an adjacent molecule in the c direction. The c dimension distribution for the trans conformation model has a broader distribution indicative of disorder and slight bending of the backbones within the structure.

51

Figure A1.1 Simulation data from a molecular model of compound 1. (Left Panel) The probability densities of a, b and c dimensions averaged over 100 well separated time points after relaxation. Probability maxima are labeled. (Right Panel) (Top) The internal energy (without the Zero Point energy) is plotted versus time for after equilibration. (Bottom) Probability distribution of the distance between intramolecular backbone N atoms on the same side backbone (see Figure 2, Chapter 1)

52

Figure A1.2 - WAXS detail for compound 4. The high-q WAXS data and fit for Ac-Ndc9-Nte9, with the peaks indexed and their q positions labeled. The fit is plotted in blue on top of the data, and the background fit (log poly5) is shown in green. The angle between a and c is 96.1 degrees.

30x103

25

20

15

10

Inte

nsity (

a.u

.)

1.81.71.61.51.41.3

q (Å-1

)

Background

Fit Data/

(100)

(101)

1.356

1.432

(102)1.578

(103)

1.777

53

A1.5 Peptoid Characterization Data:

Ac-Ndc9-Nte5

M/Z = 2850.22 Observed Mass in MicroTOF: 1426.1 (+2 H+); 951.1 (+3 H+); The peak at 2352.6 is a contaminant in the MicroTOF, and is present in all runs for all users. The Observed Mass in MALDI is 2871.6, which is within instrument sensitivity (1 part per thousand m/z) of + 23Na

54

Ac-Ndc9-Nte9

M/Z = 3663.69 Observed Mass: 1832.9 (+2 H+); 1222.2 (+3 H+)

55

Ac-Ndc9-Nte15

M/Z = 4882.38 Observed Mass: 1221.6 (+4 H+); 1628.8 (+3 H+); 2352.6 (+2 H+)

56

Ac-Nhp9-Nde9

M/Z = 2888.03 Observed Mass: 1445.0 (+2 H+); 963.7 (+3 H+);

57

Appendix 2 - Supporting information for Chapter 2

58

Table A.2.1

Table AI –

The change in mass of inhibitors with pervaporation detoxification or control treatment. The inhibitors’

concentration before and after pervaporation and their concentration in the permeate is also given.

Mea

sure

dB

yH

PL

C

Pervapor-

ation

retentate

mass% of

initial

Mass%

of initial

in control

Starting

Concen-

tration

(μg/mL)

End

retentate

Concen-

tration

(μg/mL)

Mean Permeate

Concentration

Glucose 100 100 19100 28200 -

Xylose 98 97 45300 65200 -

Arabinose 92 96 5140 7050 -

HMF 90 93 288 390 -

Furfural 0 88 802 - 700

Glycerol 0 230 -

Formic 46 47 2470 1740 328

Acetic 73 106 8650 10150 7787

Mea

sure

db

y L

CM

S

Oxalic Acid 99 133 92 136 -

cis-Aconitic Acid 128 192 9 20 -

Maleic Acid 236 575 5 19 0.71

Glucuronic Acid 65 65 236 227 -

Citric Acid 72 78 103 107 -

Galacturonic Acid 86 87 568 628 <

Gluconic Acid 215 216 154 447 <

Pyruvic Acid 122 140 104 190 -

Tricarballylic Acid 0 20 33 - -

Glyoxylic Acid 298 > -

Malic Acid 150 158 311 541 1.5

Malonic Acid 24 41 18 6 -

trans-Aconitic Acid 347 128 3 13 -

Methylmalonic Acid 84 247 4 5 2.0

Succinic Acid 257 235 15 50 0.4

Glycolic Acid 397 368 43 239 <

Lactic Acid 105 106 31 53 <

Itaconic Acid 81 104 5 7 1.7

Glutaric Acid - - -

Fumaric Acid 81 101 4 4 -

2-Hydroxy-2-methylbutyric Acid - - -

Adipic Acid - - -

Levulinic Acid 114 105 996 1557 <

2-Furoic Acid 92 130 11 15 <

Mea

sure

dby

GC

MS 2,3-Dihydroxybenzoic Acid 1 1 -

2,5-Dihydroxybenzoic Acid 2 3 +

2,6-Dimethoxyphenol 1 + +

2-hydroxybenzoic acid + + -

2-Hydroxybenzyl Alcohol + + +

3,4-Dihydroxybenzaldehyde 255 243 2 6 +

3,4-Dihydroxybenzoic Acid 170 143 4 9 +

3-Hydroxybenzoic Acid + + -

3-Methylcatechol + + -

4-Hydroxybenzaldehyde 133 128 21 42 -

4-Hydroxybenzoic Acid 118 109 7 12 +

4-Hydroxycoumarin 60 94 2 2 -

4-Hydroxymandelic Acid 80 82 4 6 +

4-Methylcatechol + + -

4-OH-3-OCH3-Mandelic Acid 126 108 2 4 -

Acetosyringone 103 101 1 2 -

Acetovanillone 107 121 3 5 +

Benzoic Acid + + +

Benzyl Alcohol + + -

Caffeic Acid 166 152 4 9 -

59

[Table continued from previous page]

Table AI –

The change in mass of inhibitors with pervaporation detoxification or control treatment. The inhibitors’

concentration before and after pervaporation and their concentration in the permeate is also given.

Mea

sure

dB

yH

PL

C

Pervapor-

ation

retentate

mass% of

initial

Mass%

of initial

in control

Starting

Concen-

tration

(μg/mL)

End

retentate

Concen-

tration

(μg/mL)

Mean Permeate

Concentration

Glucose 100 100 19100 28200 -

Xylose 98 97 45300 65200 -

Arabinose 92 96 5140 7050 -

HMF 90 93 288 390 -

Furfural 0 88 802 - 700

Glycerol 0 230 -

Formic 46 47 2470 1740 328

Acetic 73 106 8650 10150 7787

Mea

sure

db

y L

CM

S

Oxalic Acid 99 133 92 136 -

cis-Aconitic Acid 128 192 9 20 -

Maleic Acid 236 575 5 19 0.71

Glucuronic Acid 65 65 236 227 -

Citric Acid 72 78 103 107 -

Galacturonic Acid 86 87 568 628 <

Gluconic Acid 215 216 154 447 <

Pyruvic Acid 122 140 104 190 -

Tricarballylic Acid 0 20 33 - -

Glyoxylic Acid 298 > -

Malic Acid 150 158 311 541 1.5

Malonic Acid 24 41 18 6 -

trans-Aconitic Acid 347 128 3 13 -

Methylmalonic Acid 84 247 4 5 2.0

Succinic Acid 257 235 15 50 0.4

Glycolic Acid 397 368 43 239 <

Lactic Acid 105 106 31 53 <

Itaconic Acid 81 104 5 7 1.7

Glutaric Acid - - -

Fumaric Acid 81 101 4 4 -

2-Hydroxy-2-methylbutyric Acid - - -

Adipic Acid - - -

Levulinic Acid 114 105 996 1557 <

2-Furoic Acid 92 130 11 15 <

Mea

sure

dby

GC

MS 2,3-Dihydroxybenzoic Acid 1 1 -

2,5-Dihydroxybenzoic Acid 2 3 +

2,6-Dimethoxyphenol 1 + +

2-hydroxybenzoic acid + + -

2-Hydroxybenzyl Alcohol + + +

3,4-Dihydroxybenzaldehyde 255 243 2 6 +

3,4-Dihydroxybenzoic Acid 170 143 4 9 +

3-Hydroxybenzoic Acid + + -

3-Methylcatechol + + -

4-Hydroxybenzaldehyde 133 128 21 42 -

4-Hydroxybenzoic Acid 118 109 7 12 +

4-Hydroxycoumarin 60 94 2 2 -

4-Hydroxymandelic Acid 80 82 4 6 +

4-Methylcatechol + + -

4-OH-3-OCH3-Mandelic Acid 126 108 2 4 -

Acetosyringone 103 101 1 2 -

Acetovanillone 107 121 3 5 +

Benzoic Acid + + +

Benzyl Alcohol + + -

Caffeic Acid 166 152 4 9 -

Mea

sure

db

yG

CM

S Coniferyl Alcohol 48 4 10 7 -

Eugenol + + +

Ferulic Acid 100 67 94 140 2.0

Gallic Acid + + -

Guaiacol 2 + +

Homovanillic 155 133 3 7 -

Homovanillyl Alcohol + + -

Hydroquinone 99 67 9 13 -

iso-Eugenol + + -

iso-Ferulic Acid + + -

p-Coumaric 108 77 61 143 1.3

Resorcinol - - -

Salicylaldehyde + + +

Sinapaldehyde 60 54 5 4 -

Sinapic Acid 1 1 -

Syringaldehyde 126 131 22 41 +

Syringic Acid 125 117 14 25 -

Vanillic Acid 130 123 25 47 +

Vanillin 144 164 33 70 7.0

Vanillyl Alcohol + 3 -

Cis p-Coumaric 89 84 3 4 -

Cis Ferulic Acid 74 76 3 4 -

1-Guaiacylethanol + 4 2.8

2-Guaiacylacetaldehyde 16 18 22 5 -

2-OH-1-Guaiacylpropanone 90 96 11 14 -

2-Syringylacetaldehyde 20 14 8 2 -

3-Guaiacylacetol 46 35 151 108 -

3-Guaiacylpropanol 100 98 9 13 -

3-Syringylacetol 31 19 121 53 -

4-(2-OH-Ethyl)phenol + 4 +

Guaiacylacetone 67 72 10 10 +

1-(4-OH-Phenyl)-acetol 91 86 8 11 -

1- Syringylacetol 99 88 23 33 4.0

1-Guaiacylacetol 97 86 69 96 2.3

1-Guaiacylpropan-1,2-dione 77 100 26 29 2.0

1-Syringylpropan-1,2-dione 96 101 12 16 -

2-OH-1-(4-OH-Phenyl)-propan-1-one 89 90 3 4 2.0

2-OH-1-Guaiacylethanone 99 93 32 45 -

2-OH-1-Syringylethanone 86 82 16 20 -

2-OH-1-Syringylpropanone 94 91 4 5 -

3-(4-OH-Phenyl)-acetol 63 52 3 15 -

3-Guaiacylpropanoic Acid 87 91 3 4 -

3-OH-1-Guaiacylpropanone 83 93 2 3 -

3-OH-1-Syringylpropanone 86 89 3 3 -

- not detected

+ present but concentration < 1 µg/mL

> present but concentration >500 µg/mL

< present but concentration < .1 µg/mL