Development of Electrochemical Methods for Detection of Pesticides and Biofuel

Production

Asli Sahin

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY 2012

Asli Sahin

Development of Electrochemical Methods for Detection of Pesticides and Biofuel

Production

Asli Sahin

Electrochemical methods coupled with biological elements are used in industrial, medical and environmental applications. In this thesis we will discuss two such applications: an electrochemical biosensor for detection of pesticides and biofuel generation using electrochemical methods coupled with microorganisms.

Electrochemical biosensors are commonly used as a result of their selectivity, sensitivity, rapid response and portability. A common application for electrochemical biosensors is detection of pesticides and toxins in water samples. In this thesis, we will focus on detection of organosphosphates (OPs), a group of compounds that are commonly used as pesticides and nerve agents. Rapid and sensitive detection of these compounds has been an area of active research due to their high toxicity. Amperometric and potentiometric electrochemical biosensors that use organophosphorus hydrolase

(OPH), an enzyme that can hydrolyze a broad class of OPs, have been reported for field detection of OPs. Amperometry is used for detection of electroactive leaving groups and potentiometry is used for detection of pH changes that take place during the hydrolysis reaction. Both these methods have limitations: using amperometric biosensors, very low limits of detection are achieved but this method is limited to the few OPs with electroactive leaving groups, on the other hand potentiometric biosensor can be used for detection of all OPs but they don’t have low enough limits of detection. We have

developed a novel dual enzyme biosensor biosensor with OPH and horseradish peroxidase (HRP) for detection of OPs with phenolic leaving groups which are much more common than electroactive leaving groups. This biosensor was used for detection of dichlofenthion, an OP which does not have an electroactive leaving group.

Electrochemical biosensors with enzymatic layers such as the one described above are commonly used, however there are no clear guidelines on how to design these sensors. Based on the application, the biosensor might be designed for a target limit of detection, sensitivity and/or linear range. Simulation tools can be used to provide general guidelines that can be applied to a wide range of biosensors and reduce extensive development time. In chapter 3, reaction-diffusion equations for an enzymatic biosensor with saturation kinetics are solved. Dimensionless numbers that can guide the researchers on designing and optimizing biosensors are presented. Additionally results from a formaldehyde biosensor are presented to demonstrate the use of some of the guidelines introduced.

Recently biofuel production using electricity has become an attractive area of research in the interface between electrochemistry and biology. Biofuel production from renewable resources is the challenge of our century. Bioproduction using natural photosynthesis routes suffers from low efficiencies. An alternative to photosynthesis is the use of chemolithoautotrophic organisms that are genetically engineered to produce biofuels. These organisms use inorganic redox mediators as their energy source instead of photosynthesis. A sustainable biofuel production process is feasible, if the chemolithoautotrophic organisms are coupled with an electrochemical reactor to recycle the redox mediator. We will describe the development of an electrochemical reactor that

reduces nitrite to ammonia which is the natural mediator couple for the chemolithoautotrophic organism Nitrosomonas europaea. Using nickel and glassy carbon electrodes nearly 100% current efficiencies were obtained in batch electrolysis experiments in media optimal for N.europaea growth. Additionally effect of production of the potential biofuel isobutanol on cathode performance was studied.

A flow-by electrochemical reactor with a porous electrode was designed to supply sufficient ammonia to a 5 L chemostat containing wild type N.europaea. Biomass production using electrochemically regenerated mediator was demonstrated by coupling this electrochemical reactor with N.europaea chemostat. Ammonia removal and biomass yield for the bacteria were unaffected by the coupled operation which suggests the electrochemically reduced media did not alter aerobic metabolism of the bacteria.

Reduction of nitrite to ammonia was further investigated using linear sweep voltammetry and electrochemical impedance spectroscopy. A limiting current plateau was identified in linear sweep experiments. In the pH range between 6.0 and 8.0, the flux of protons to the electrode were determined to cause mass transfer limitations. The same electrochemical methods were also used to assess the changes in performance of cathodes with time. Formation of a film was observed on glassy carbon and nickel electrodes. The film did not seem to cause any changes to the nickel electrode. On the other hand overpotential for the glassy carbon electrode was significantly reduced which would decrease power requirements for the electrochemical reactor and the overall process.

Table of Contents Chapter 1 Introduction ...... 1 References ...... 15 Figures ...... 22 Appendix A ...... 24 Chapter 2 A Dual Enzyme Electrochemical Assay for the Detection of Organophosphorus Compounds using Organophosphorus Hydrolase and Horseradish Peroxidase ...... 31 Abstract ...... 31 Introduction ...... 32 Experimental ...... 37 Results and Discussion ...... 40 Conclusions ...... 44 References ...... 46 Tables and Figures ...... 50 Chapter 3 Design Rules for the Development of Electrochemical Biosensors using Immobilized Enzymes ...... 58 Abstract ...... 58 Introduction ...... 58 Model Description ...... 62 Experimental ...... 66 Results and Discussion ...... 68 Conclusions ...... 72 References ...... 76 Tables and Figures ...... 79 Chapter 4 Electrochemical Reduction of Nitrite to Ammonia for Use in Biofuel Production ...... 90 Abstract ...... 90 Introduction ...... 91 Experimental ...... 97 Results and Discussion ...... 99 Conclusions ...... 108 References ...... 110 Tables and Figures ...... 113

Chapter 5 Biomass Production from Electricity Using Ammonia as an Electron Carrier in a Reverse Microbial Fuel Cell ...... 125 Abstract ...... 125 Introduction ...... 126 Experimental ...... 129 Results ...... 133 Discussion ...... 136 References ...... 141 Tables and Figures ...... 144 Appendix A ...... 149 Chapter 6: Performance of Glassy Carbon and Nickel Cathodes used for Reduction of Nitrite to Ammonia for use in Biofuel Production ...... 158 Abstract ...... 158 Introduction ...... 158 Experimental ...... 163 Results and Discussion ...... 165 Conclusions ...... 171 References ...... 175 Tables and Figures ...... 178 Appendix A ...... 185 Chapter 7 Conclusions ...... 190

LIST OF FIGURES AND TABLES

Chapter 1

Figure 1 Schematic of the proposed detection scheme where the OP with a phenolic leaving group is hydrolyzed by OPH followed by detection of the phenolic compound by HRP. A change in current is observed as the output.

Figure 2 Schematic of the reverse microbial fuel cell.

Table A1 Summary of thermodynamic and estimated parameters used in efficiency calculations.

Figure A1 Flowchart for the N.europaea bioreactor.

Figure A2 Flowchart for the N.europaea bioreactor and electrochemical reactor for reduction of nitrite to ammonia.

Chapter 2

Table I A sample list of organophosphates that can have electroactive group and/or phenolic leaving group.

Table II Summary of kinetic parameters for the hydrolysis of several OP substrates by OPH

Table III Limit of detection of OP biosensors for various OPs.

iii

two, and one stream is incubated with OPH. The correlation is developed based on differences in current, effectively reducing background noise and other interferences. Schematic and photograph of the microfluidic system.

Figure 2 Initial production rate of 2, 4-dichlorophenol as a function of dichlofenthion concentration. Each sample was prepared in 50mM Tris (pH 9.0) and measurements were made at 25ºC. The resulting KM and kcat values can be found in Table II. All data were collected in triplicate and the error bars represent standard deviations.

Figure 3 Calibration curve showing change in current as a function of 2, 4- dichlorophenol concentration in 100 mM phosphate buffer at a pH 8.0.

Figure 4 Chronoamperometric plots showing the change in reduction current obtained by switching from a background stream (c.f. Figure 1C) to a second stream. The switch occurred at approximately 60 seconds, and the background solution was re-introduced at 90 seconds. In each experiment, the background solution contained 100µM dichlofenthion. In the first case (- - - -), the sample contained only 0.19 mg/mL OPH indicating that OPH itself does not increase current. The heavy solid line shows the increase in reduction current from a sample of 100µM dichlofenthion incubated with 0.19 mg/mL OPH. The lighter solid line shows a positive control that a sample containing 20 µM 2, 4-dichlorophenol does indeed cause a significant change in current.

Figure 5 Characterization and calibration of the dual enzyme biosensor. Figure 5A shows the amperometric response of the HRP electrode with alternating injections of dichlofenthion only solutions and solutions of 50µM dichlofenthion with OPH incubated at different times. Figure 5B illustrates the change in peak height as a function of incubation time. Figure 5C shows the calibration curve for dichlofenthion obtained using the plateau values of the hyperbolic fits(left axis), and gives the concentration of 2,4- dichlorophenol obtained from the correlation in Figure 3 as a function of the dichlofenthion concentration (right axis).

Chapter 3

Table I Summary of guidelines to design an electrochemical biosensor with an enzymatic layer with target design criteria.

Table II Kinetic parameter and diffusion coefficients used in simulations

Figure 1 Schematic showing the experimental parameters (bold outline) that affect the biosensor response and goals (dashed outline) of biosensor design.

Figure 2 Schematic of a biosensor with an enzymatic gel and direct electron transfer at the electrode.

Figure 3 Dimensionless product flux at the surface as a function of dimensionless concentration of substrate for different values of at α=0.01. Inset graph showing the time it takes to reach steady state for each .

Figure 4 Concentration profile for the product within the enzyme layer when =10.0 for different values of α. Negligible charge transfer resistance; α =10, large charge transfer resistance; α =0 and intermediate values of charge transfer; α =0.1 and α =0.01.

Figure 5 Dimensionless product flux at the surface as a function of dimensionless concentration of substrate for α=0.1, α=1, α=10, α=100.0 and α=1000.0 at =10.0 .

Figure 6 Change in dimensionless sensitivity, s, as a function of change in α for =10.

Figure 7 Representation of ordered bi-bi equation with substrate inhibition.

Figure 8 Experimental (points) and predicted steady state current response at different enzyme gel thicknesses for a formaldehyde biosensor.

Figure 9 Simulated calibration curve for formaldehyde.

Chapter 4

Table I Current efficiencies after 1hr electrolysis experiments in an undivided cell with nickel, glassy carbon and copper cathodes in different solutions at current densities 1.0- 20 mA/cm2.

Figure 1 Schematic showing the electrochemical cell where nitrite is reduced to ammonia and the bioreactor where chemolithoautotrophic cells oxidize ammonia back to nitrite. The cells could be genetically modified to produce chemicals or biofuels.

Figure 2 Chart outlining the requirements (circles with bold outline) for an electrochemical reactor supplying mediator for microbial biofuel production and parameters (circles with dashed outline) affecting the performance.

Figure 3 Schematic of the divided flow-by electrolysis cell consisting of a porous cathode and a planar anode separated by a cation exchange membrane.

Figure 4 Panel A and B show polarization curves obtained on glassy carbon and nickel rotating disk electrodes in 1 M sodium nitrite and 100 mM phosphate buffer at a pH of 7.0, respectively, at different rotation speeds and at a scan rate of 5.0 mV/s. The inset shows the limiting current as a function of square root of rotation speed for nickel rotating disk electrode.

Figure 5 Polarization curve of a nickel rotating disk electrode in 100 mM phosphate buffer at a pH of 7.0 with 0, 5.0, 20, 100, 1000 mM sodium nitrite and required amount of sodium chloride to achieve 1.1 M sodium ion concentration. Inset shows the current density as a function of nitrite concentration at -0.8 V vs. Ag/AgCl.

Figure 6 Polarization curves taken on a nickel rotating disk electrode at 400 rpm in 100 mM phosphate buffer (pH 7.0 and 1.0 M NaCl), 100 mM phosphate buffer at a pH of 7.0 and 1.0 M NaNO3, and 100 mM phosphate buffer at a pH of 7.0 and 1.0 M NaNO2. vi

Figure 7 Current as a function of potential for different rotation speeds for nitrite reduction on nickel (sweep rate was 5.0 mV s-1) in 100 mM phosphate buffer at a pH of 7.0 and 1 M sodium nitrite and 100 mM phosphate buffer pH 7 .0 and 1 M sodium nitrite and 50 mg/L isobutanol at different rotation speeds .

Figure 8 Current efficiencies for different cathode materials at varying current densities: copper (rectangles), nickel (triangles) and glassy carbon (circles) in 1.0 M sodium nitrite solution in 100 mM phosphate buffer at a pH of 7.0 after 1 hr of electrolysis in an undivided cell at various current densities.

Figure 9 Constant current experiments in 100 mM phosphate buffer at a pH of 7.0 and 2 2 2 1.0 M sodium nitrite on a nickel RDE at 1 mA/cm , 5 mA/cm and 10 mA/cm .

Figure 10 Current efficiencies in the flow-by reactor (divided cell) for different starting concentrations of nitrite (20, 50, 100 mM sodium nitrite in 100 mM phosphate buffer at a pH of 7) with 80 (pores per inch) ppi glassy carbon cathode at 24.6 mA/cm2 (1 mA/cm2 based on estimate of real area) and (100 mM sodium nitrite) with 90 ppi nickel electrode at 10.6 mA/cm2 (1 mA/cm2 based on estimate of real area).

Figure 11 Change in the catholyte pH for different anolytes in the flow-by (divided cell): 50 mM phosphate buffer at a pH of 7.0 and 50 mM sodium nitrite (filled circles), 0.5 M sulfuric acid (empty circles), 2.0 M sulfuric acid (triangles) and 3.0 M sulfuric acid (rectangles) operating at 49 mA/cm2 (2 mA/cm2 based on estimate of real area).

Chapter 5

Table I Summary of biokinetic and stoichiometric parameters describing growth of wild- type Nitrosomonas europaea on electrochemically regenerated media.

Figure 1.Overview of a reverse microbial fuel cell which uses the ammonia/nitrite redox couple as a mediator and ammonia-oxidizing N. europaea cells as biocatalysts.

vii

Figure 2 Linear sweeps of nickel, copper and glassy carbon rotating disk electrodes in 50 mM Phosphate Buffer + 50 mM Sodium Nitrite at pH 7.0 at 400rpm. Inset: constant current (2mA/cm2) experiments in 50 mM Phosphate Buffer + 50 mM Sodium Nitrite at pH 7.0 with nickel, copper and glassy carbon rotating disk electrodes at 400 rpm.

Figure 3 Current efficiency of nitrite reduction to ammonia as a function of percentage of nitrite converted to ammonia in spent media from the bioreactor. Inset shows the current efficiencies for 20mM sodium nitrite in 100mM phosphate buffer as a function of applied potential in batch experiments in an undivided three electrode cell with a glassy carbon cathode.

Figure 4 Ammonia removal efficiency and observed biomass yield for continuous flow cultivated Nitrosomonas europaea. Period 1 was defined by growth on synthetic media prepared from ACS grade chemicals. Period 2 was defined by cultivation on spent media from Period 1 that was electrochemically regenerated. Period 3 was defined by cultivation on spent media from Period 2 that was electrochemically regenerated. COD represents the chemical oxygen demand associated with complete oxidation to CO2. Replicate results are presented in Figure A1.

Table A1 Half cell reactions and thermodynamic cell potential for inorganic electron mediators.

Table A2 Summary of biokinetic and stoichiometric parameters describing growth of wild-type Nitrosomonas europaea on electrochemically regenerated media.

Table A3 Thermodynamic and biokinetic properties of common inorganic electron mediator couples.

Figure A2 Current efficiency of nitrite reduction to ammonia in spent media from wild- type and genetically N. europaea.

Table A4 Physical, thermodynamic and biokinetic properties of ammonia, nitrite, iron (II), hydrogen sulfide and hydrogen.

viii

Table A5 Current Efficiency in the flow reactor in spent media from wild-type and genetically N. europaea.

Figure A1 Ammonia removal efficiency and observed biomass yield for continuous flow cultivated Nitrosomonas europaea (Replicate 1). Period 1 was defined by growth on synthetic media prepared from ACS grade chemicals. Period 2 was defined by cultivation on spent media from Period 1 that was electrochemically regenerated.

Figure A2 Current efficiency of nitrite reduction to ammonia in spent media from wild- type and genetically N. europaea.

Chapter 6

Figure 1 Polarization curve of a nickel rotating disk electrode in 100 mM phosphate buffer at a pH of 7.0 with 50 mM sodium nitrite and 0, 5, 15, 100 mM ammonium sulfate. At a rotation speed of 400 rpm and a scan rate of 5 mV/s.

Figure 2 Experimental polarization curves (black lines) and fits to data (grey lines) for a nickel rotating disk electrode at 400 rpm in 100 mM phosphate buffer and 50 mM sodium nitrite at pH 6.0, 6.5, 7.0, 7.5 and 8.0.

Figure 3 Experimental polarization curves and fits to data for a nickel rotating disk electrode at 400 rpm in 50 mM sodium nitrite in 50, 100, 300 mM phosphate buffer at pH 7.0.

Figure 4 Schematic showing nitrite reduction at the electrode and acid-base equilibrium for phosphate buffer in solution for a rotating disk electrode with a diffusion layer thickness, δ.

Figure 5 Panel A-B and C-D show Nyquist plots for glassy carbon and nickel rotating disk electrodes, respectively, in 50 mM sodium nitrite at pH 6.0-8.0 (panels A and C) in

100 mM phosphate buffer and at pH 7.0 at phosphate buffer concentrations of 50,100 and 300 mM (panels B and D). Experimental data for impedance is shown with symbols and the fits are shown using continuous lines.

Figure 6 Panel A-B and C-D show Nyquist plots and polarization curves obtained on glassy carbon and nickel rotating disk electrodes in fresh 50 mM sodium nitrite and 50 mM phosphate buffer at a pH of 7.0 as a function of time during the marathon study, respectively. Both impedance and polarization curves were recorded for a rotation speed of 400rpm. The scan rate for the linear sweeps was 5.0 mV/s. Experimental data for impedance is shown in symbols and the fits are shown using continuous lines.

Figure 7 Rate constant, k, as a function of time for glassy carbon and nickel electrodes.

Figure A1 Survey for glassy carbon electrode (EV/step 0.8 eV/s, Time/Step 100 msec, number of sweeps 3, pass energy 187.85 eV)

Figure A2 Atomic concentrations of carbon, oxygen, sodium and nitrogen determined in multiplex experiments for glassy carbon electrode. (EV/step 0.1 eV/s, Time/Step 300 msec, number of sweeps 30, pass energy 11.75 eV)

Figure A3 Survey for nickel electrode (EV/step 0.8 eV/s, Time/Step 100msec, number of sweeps 3, pass energy 187.85 eV)

Figure A4 Atomic concentrations of carbon, oxygen, sodium, nickel and nitrogen determined in multiplex experiments for nickel electrode. (EV/step 0.1 eV/s, Time/Step 300 msec, number of sweeps 30, pass energy 11.75 eV)

ACKNOWLEDGEMENTS

I would like to thank my advisors Professor Alan C West and Professor Scott

Banta for all their help, mentoring and patience. I would also like to thank all Banta and

West lab members, especially Kristen Shattuck-McKenzie, Ugur Emekli, Joshua

Gallaway and Robert Von Gutfeld for their time, advices, and help. My special thanks to

Damla Eroglu for scientific discussions and ideas over lunch, Igor Volov for coming up with more abstract ideas then I thought was humanly possible, Feng Qiao, and Kevin

Dooley for their support, Elliot C Campbell and Oren Shur for valuable discussions over coffee.

Most of all, I would like to thank my family; especially my parents and sister for their encouragement and support, Ates for making me smile at least once a day every day and Jason for helping me with everything I need help for and always giving sound advice.

Chapter 1 Introduction

Biomolecules and microorganisms are promising engineering tools since they have been refined by evolution to efficiently perform specific tasks. They are commonly used in pharmaceutical, environmental and industrial applications. As well as being used alone they are coupled with other fields such as electrochemistry, spectroscopy and chromatography to improve specificity and efficiency of different applications.

An emerging field at the interface between electrochemistry and biology aims to harness biomolecules for applications in biofuel production, electricity generation and detection of target molecules. This thesis describes two such applications: pesticide detection using electrochemical biosensors, and biofuel generation using electricity. Though the requirements for biosensors and biofuel production from electricity are very different, the goals are accomplished by combining advantages of electrochemical methods and biological elements.

Electrochemical Biosensors

Biosensors are commonly used in various applications ranging from medical to industrial, for detection of analytes with high selectivity and specificity. A biosensor consist of a biological element (bioreceptor) coupled with a physiochemical transducer to produce a signal in the presence of a specific analyte (1). Various types of biosensors can be designed, differing in the bioreceptor and transducer used. Examples of bioreceptors used in biosensors are enzymes, antibodies, tissues, organelles, cells and microorganisms (1). Transducers include change in pH, heat, conductivity, light, mass change and electroactive substances (2). Depending on the transducer the change in response can be measured optically, electrochemically or calorimetrically. When the transducer is an electroactive substance the signal can be measured

2 potentiometrically or amperometrically using electrochemical methods (3). Potentiometric sensors generally involve the use of an ion-selective electrode such as pH, ammonia or fluoride selective electrodes. The difference in potential across the ion selective membrane is measured

(1). Amperometric biosensors constitute the majority of biosensors available commercially. They are operated at a fixed potential to measure changes in current as a result of electroactive substance produced in the enzymatic reaction (1).

Developing a successful electrochemical biosensor requires combination of knowledge from many disciplines such as biology, electrochemistry, solid and surface physics and bioengineering. The biosensor response is required to be accurate, reproducible, rapid, and linear over the range of interest. The final product also needs to be easy to manufacture, inexpensive portable and easy to use (3).

The first electrochemical biosensor was reported by Clark and Lyons in 1962 and it was developed to measure glucose levels in blood (4). The glucose sensor takes advantage of an enzyme called glucose oxidase that can specifically and efficiently oxidize glucose. When this enzyme is immobilized on an electrode it can produce current proportional to the amount of glucose present (5). After 50 years of development today glucose sensors are used worldwide by diabetes patients every day.

Based on the type of bioreceptor used electrochemical biosensors can be divided into two groups: affinity and biocatalyst based biosensors (3). Antibodies and nucleic acids are commonly used in affinity based sensors where the analyte binds to the bioreceptor irreversibly and causes a change in the environment that can be detected electrochemically. Biocatalytic biosensors employ enzymes, cells and tissues that can catalyze reactions of interest. The response is then

3 measured by monitoring the rate of product formation; disappearance of substrate or inhibition of reaction.

Detection of Organophosphates

In this thesis we are going to describe an enzymatic electrochemical biosensor for detection of pesticides. Rapid detection of pesticides in water samples is an important environmental application. According to the EPA, 105 million pounds of pesticides were produced in the USA in 2001. Organophosphates (OPs) made up 70 % of this production (6).

OPs are neurotoxic compounds that are commonly used as nerve agents and pesticides. Nerve agents belonging to this group such as sarin, soman and tabun were developed in Germany during World War II. In 1995 sarin was used in the terrorist attack on the Tokyo subway system and caused hospitalization of 5500 victims and 12 fatalities (7). Tabun and sarin were also used in the Iran-Iraq War. OPs irreversibly inhibit acetylcholinesterase (AChE), a serine protease found in central and peripheral nervous systems. The task of AChE is to terminate synaptic transmission by hydrolytic destruction of the neurotransmitter, acetylcholine (8). The inhibition of AChE by OPs leads to acute neurotoxic response (9). Due to their high toxicity, rapid detection of OPs has been a topic of interest.

Analytical Methods for OP Detection

Since the introduction of OPs to the market in the 1970s, many analytical methods have been developed to detect these compounds (10). Analytical techniques such as gas and liquid chromatography, capillary electrophoresis, enzyme-linked immunoabsorbant assay (ELISA) and various types of spectroscopy are commonly used for OP detection (10, 11). One of the most widely used techniques is high resolution gas chromatography with element-specific detection.

Though this analytical technique has very low limits of detection, it has several disadvantages such as laborious sample preparation and decomposition of OPs at high temperatures needed for injection (10). Immunoassay based methods are also commonly used for OP detection. The advantage of immunobased methods such as ELISA, radio immunoassay and immunosensors is their high specificity (12). However the need to grow antibodies for each pesticide specifically, long analysis times and extensive sample handling are disadvantageous (13, 14). Alternatively, spectrophotometric methods are used for OP detection but they are limited by the matrix interference and long detection times (10). Though these methods are very sensitive and specific, they are expensive, have long sample preparation times and require trained personnel.

Field Detection of OPs

Field detection of OPs is essential in order to determine water quality and health risks.

However analytical techniques described above are not suitable for field detection. To address the challenges associated with the use of analytical methods, electrochemical biosensors capable of field detection have been developed. Two main classes of electrochemical biosensors are based on indirect detection by cholinesterase inhibition or direct detection using organophosphate hydrolase (OPH).

Electrochemical Acetylcholinesterase Based Biosensors

Cholinesterase based sensors rely on inhibition of acetylcholinesterase (AChE) or butrylcholinesterase (BuChE) (10). AChE and BuChE are the two major types of cholinesterase, mainly differing in substrate preference (15, 16). AChE is primarily found in the blood and neuronal synapses whereas BuChE is primarily found in the liver. Cholinesterase based sensors

5 using different electrochemical techniques have been reported: (i) amperometric (17-24), (ii) potentiometric (25-28) and (iii) conductometric (29, 30).

Though cholinesterase based sensors are very sensitive they have limitations, including lack of selectivity due to inhibition of cholinesterase by other toxic compounds (e.g. carbamates and organochlorines), long incubation times, multiple preparation steps and problems associated with reactivation of AChE (14).

Electrochemical Organophosphorus Hydrolase Based Biosensors

Organophoshorus hydrolase (OPH) biosensors are based on hydrolysis of OPs. OPH is a metalloenzyme that was first found in the bacteria Pseudomonas diminuta and Flavobacterium sp in pesticide contaminated soils (31, 32). OPH has broad substrate specificity and was reported to hydrolyze a number of pesticides such as paraoxon, parathion, coumaphos, diazinon, dursban, methyl parathion and nerve agents such as sarin, soman, tabun and VX (31, 33, 34).

The hydrolysis reaction is:

Rxn 1

where R1 and R2 are aryl or alkyl groups and X is phenoxy, thiol moiety, cyanide or fluorine group.

Reaction 1 is coupled with amperometric or potentiometric methods to develop an electrochemical biosensor. Potentiometric transducers have been used to detect the H+ produced during hydrolysis reaction (35, 36). Potentiometric biosensors can be used to detect a broad range of OPs. However high limits of detection limit their use to off-line analysis (35, 37).

Alternatively amperometric transducers are used, if one of the hydrolysis products is electroactive (14, 35, 38-41). These sensors have very low limits of detection but they can only be used for detection of pesticides with electroactive leaving groups.

A new method that can detect a wide class of OPs with low limits of detection is needed.

In chapter 2, we present such an approach for OP detection using a dual enzyme biosensor consisting of OPH and horseradish peroxidase (HRP). Since many OPs have aromatic hydrolysis products such as dichlofenthion, prothiofos and s-seven, a dual enzyme electrode consisting of

OPH and HRP can be used to detect these OPs: first, OPH hydrolyzes the OP, and second, the phenolic hydrolysis products acting as mediators for electron transfer (see below) are detected at the HRP electrode as shown in Figure 1.

An important consideration when designing an enzymatic electrochemical biosensor is stability of the enzyme in operation and long term storage. The immobilization method, electrode material and design all affect the performance and stability of the biosensor. Various immobilization methods such as physical adsorption of enzyme on the electrode, entrapment in a polymer film, covalent bonding to the electrode surface and crosslinking are commonly employed (2, 3). In this study a peroxide sensor is simply constructed by absorbing HRP onto an

7 electrode surface. By operating this electrode at the appropriate potential a reduction current is produced, proportional to concentration of the peroxide.

The choice of electrode material is also very critical to biosensor performance. HRP sensors on different electrode materials such as gold (42-44), carbon and graphite (45-47) and platinum (48-50) are reported. Based on the electrode material, electrons for the reduction of hydrogen peroxide can directly come from the electrode (45). This is called direct electron transfer. When direct electron transfer is employed the electrical contact between the electrode and the redox active site of the enzyme is critical. To obtain good electrical contact the distance between the electrode and enzyme needs to be minimized. Alternatively, mediators such as phenols (51-54), aromatic amines (51, 54, 55), ferrocyanide (56) and ferrocenes (57) are used to transfer electrons. This is called a mediated electron transfer. The reduction current at the HRP electrode is proportional to the concentration of the electron mediator, when a constant amount of hydrogen peroxide is present in the system. This principle is used for detection of phenolic compounds and it can be carried out at potentials as low as -50 mV vs. Ag/AgCl (58). Thus the described dual enzyme sensor has the advantage of detecting a large class of OPs at low operating potential where the background and interferences are negligible.

Design of Electrochemical Biosensors with Immobilized Enzyme Layer

Glucose oxidase biosensors for detection of glucose, HRP based biosensors for detection of hydrogen peroxide and phenols and OPH biosensor for detection of pesticides are only a few examples of electrochemical biosensors with an immobilized enzyme layer. Even though these electrochemical biosensors with enzymatic layers are commonly used in a variety of applications, there is a lack of guidelines on how to design these biosensors to achieve a target

8 limit of detection, linear range and/or sensitivity. Several parameters have been identified to affect the performance of these biosensors such as transport of the substrate and the product through the immobilized enzyme layer, oxidation/reduction kinetics at the electrode, enzyme activity and loading and operating conditions such as pH and temperature. Of these parameters, increasing enzyme activity and improving enzyme loading have been studied by many groups to increase biosensor performance. Advances in designing new surfaces to improve enzyme immobilization and in engineering the enzymes for increased activity and loading have been reported. During the last couple decades carbon nanotubes have been widely used to design new surfaces. The use of nanoscale featured surfaces result in high surface area, improved electrical conductivity and stability (38, 59-61). Self-assembling proteinacious hydrogels are an example of enzyme engineering to achieve high enzyme loadings while maintaining enzyme activity.

These hydrogels are designed by appending alpha helical leucine zippers to the termini of proteins which then self-assemble into hydrogels by forming crosslinks (62-64). These advances have the potential to improve biosensor performance tremendously. However at the same time the operational space that needs to explored is broadened. This causes an increase in biosensor development time.

In order to reduce the developmental time and to explore this operational space, researchers have used numerical solutions to the reaction diffusion equations (65-69).

Additionally analytical solutions have been presented for limiting cases. Most of the initial research in this area focused on development of enzymatic glucose sensors where glucose oxidase is entrapped in a redox hydrogel (70, 71). Studies to extend the linear range of sensors

(70, 72), understanding diffusion through a semi-permeable outer membrane (73, 74) and determination of kinetic constants (75) have also been reported (76). Though many researchers

9 reported using simulations to shorten biosensor development time and predict performance of sensors prior to development of a specific biosensor there is a lack of general guidelines on how to design a biosensor. With the improvements in immobilization surfaces and enzyme engineering, the need for such guidelines is ever increasing. In chapter 3, we will present guidelines for biosensor development.

Biofuel Production

Developing renewable energy sources has been one the great engineering challenges of

21st century (77-80). In year 2005, 500 exajoules of energy was used worldwide and fossil fuels made up 80-90% of this energy (81). With the increasing demand for energy, biofuel production from renewable energy is becoming even more vital. Current biofuel production methods such as production of ethanol from corn (82), biodiesel from microalgae (83) and ethanol from switchgrass (84) have much promise. However these technologies suffer from low efficiencies of solar energy conversion associated with photosynthesis. The theoretical maximum for biomass production from photosynthesis for C3 and C4 plants is 4.6 % and 6.0 % , respectively (85) and in application an average efficiency of less than 1% have been reported (86). The inefficiency is due to losses during chlorophyll-based light absorption, carbohydrate synthesis and respiration

(85, 87). An additional practical challenge is converting biomass into a biofuel that is compatible with the existing infrastructure. For a technology to produce viable alternative biofuel it is required be have high efficiencies, low land use and have minimum effect on food and water sources.

Reverse Microbial Fuel Cells

An alternative to existing technologies is proposed in figure 2 where electrical energy from renewable sources is used directly to produce biofuel. The system consists of a bioreactor containing chemolithoautotrophic bacteria that are amenable to genetic engineering to produce biofuel and an electrochemical reactor that can provide reducing equivalents to the bacteria. In a microbial fuel cell biological organisms oxidize fuels and produce electricity. In rMFC the system runs in the opposite direction using electricity to produce fuels, thus is referred to as a reverse microbial fuel cell (rMFC). With the recent advances in sustainable electrical power production such as wind turbines, geothermal energy, solar energy and etc., rMFCs have a lot of potential. Additionally rMFCs do not require use of food sources, require low land and water use and can easily be scaled up.

Direct Electron Transfer

One of the most important challenges of this process is the transfer of electrons from the electrode to the biocatalyst. Direct electron transfer has been reported in organisms such as

Geobacter sulfurreducens (88, 89) and Shewanella putrefaciens (90) but the capability to accept electrons from an electrode is not commonly found in many microorganisms. Cells need to be immobilized on the electrode for direct electron transfer. This is advantageous in terms of retaining the biocatalyst but can present challenges related to internal and external mass transport; thus it is difficult to scale-up these processes (91).

Mediated Electron Transfer

On the other hand, mediated electron transfer is easily scaled-up since immobilization of the biocatalyst on the electrode is not required. In mediated electron transfer soluble redox mediators are used for transfer of electrons from the electrode to the biocatalyst. This allows for spatial and temporal decoupling of the bioreactor from the electrochemical reactor which allows for simultaneous optimization of each component. Choice of mediator is critical for the success of an rMFC. The mediator must have fast oxidation/reduction kinetics at the electrode and in the biological organism, be chemically stable, soluble, abundant and inexpensive (92). Fe+2/Fe+3,

- - -2 + - NO2 /NO3 , H2S/SO4 , H2/H and NH3/NO2 can act as mediators for chemolithoautotrophic organisms (93-95). When developing an rMFC, an organism with a natural redox mediator can be exploited or alternatively an organism can be engineered to accept a convenient mediator.

In this work we chose ammonia/nitrite as the mediator since it is a low cost, abundant, safe and soluble redox mediator couple. The chemolithoautotrophic organism Nitrosomonas europaea, which utilizes ammonia naturally as its sole energy source for growth was used as the biocatalyst. An electrochemical reactor that efficiently reduces nitrite to ammonia in electrolytes required for growth was developed.

Energy Efficiency

From a thermodynamic point of view all mediators are required to supply the same energy to the rMFCs. Therefore metabolic efficiency of the biological element determines the overall efficiency of the process. Energy efficiency, , is defined as the energy that will be obtained from the fuel divided by the energy input:

ne gy f om uel (1) lect ical ne gy n ut

Energy from the fuel is estimated from the heat of combustion value tabulated in literature. Heat of combustion for isobutanol is = -2670kJ/mol. For our rMFC

- (2)

where is the flow rate of isobutanol, I is current and is the cell potential. Molar flow rate of isobutanol can be calculated using a aday’s law,

(3)

where is the biological requirement for electrons to produce one isobutanol molecule and

F= 96500 C/mol a aday’s constant. Substituting equation 3 into equation 2 we find:

. (4)

For the rMFC in this work nbio =105 (minimum, see Appendix A for details). If we use the thermodynamic cell potential for Vcell = -0.91 V, we get a maximum efficiency of 29 % (see

Appendix A for details). Depending on the source of electricity a more accurate comparison can be made to current technologies. For example if this rMFC is coupled with solar panels with 20

% energy capture efficiency, the total efficiency of biofuel production would be 5.8 %. It should be noted that this theoretical total efficiency is on the same order of magnitude for the current technologies without any optimizations.

Electrochemical Reduction of Nitrite to Ammonia

The electrochemical reactor designed for biofuel production needs to have minimal power requirements, tolerance for the biofuel produced in the bioreactor, compatibility with the bioreactor, and high long-term performance. In this thesis we focus on the electrochemical reactor for reduction of nitrite to ammonia for use in biofuel production.

Nitrate/nitrite reduction has been investigated by many groups mainly for applications of low level radioactive waste water treatments in alkaline solutions (96, 97). The main goal of waste water treatment applications is to reduce nitrates/nitrites to nitrogen gas for release to the environment. The most important challenge is the selectivity of the process: nitrogen has oxidation numbers ranging from +5 to -3, such that it is very difficult to control the final product of reduction. Therefore when developing a reactor for nitrite reduction one must consider the electrolyte composition (pH, mediator concentration, supporting electrolytes, etc.), cathode and anode materials, and operating conditions.

Overview of this thesis

The goal of this work is to present two cases in which the advantages of electrochemical methods are combined with specificity of biological elements. To do so we used electrochemical methods for detection of organophosphates and biofuel production in a reverse microbial fuel cell. In chapter 2, an electrochemical biosensor for field detection of neurotoxic organophosphorus compounds is described. The development of an amperometric dual enzyme biosensor that has the potential to detect a large class of organophosphates without interference from other species is discussed. The model compound dichlofenthion, a seldom investigated

OPH substrate, is detected using a dual enzyme biosensor consisting of HRP and OPH enzymes.

In chapter 3, guidelines for the design of electrochemical biosensors with immobilized enzyme layers, obtained by using simulation tools are described. The guidelines are general to aid researchers in designing sensors with a target limit of detection, sensitivity and linear range.

These guidelines can be used to assess sensor feasibility prior to extensive development. A model for reaction-diffusion in the system is presented and solved numerically.

In chapter 4, an electrochemical reactor for reduction of nitrite to ammonia, which is the natural redox couple for the chemolithoautotrophic bacteria N. europaea is described. Batch constant current electrolysis experiments and linear sweep voltammetry are used for selecting cathode material. As a proof of principle a divided flow-by porous reactor is designed to recycle the redox mediator to a chemostat containing wild type N.europaea cells.

In chapter 5, the flow-by reactor described in chapter 4 is used to regenerate ammonia from nitrite and coupled with the bioreactor containing N.europaea. Biomass generation via energy obtained from a soluble redox mediator is demonstrated.

In chapter 6, the reduction mechanism of nitrite to ammonia in neutral buffered electrolytes is investigated using linear sweep voltammetry and electrochemical impedance spectroscopy. A mechanism involving acid-base equilibrium for the buffer in solution and nitrite reduction at the electrode is proposed. Additionally, the long term performance of nickel and glassy carbon electrodes is studied.

References

1. V. C. a. N. Yang, That T, Biosensors and Their Applications, Kluwer Academic/ Plenum Publishers, New York (2000).

2. J. D. Newman and S. J. Setford, Mol Biotechnol, 32, 249 (2006).

3. D. Grieshaber, R. MacKenzie, J. Vörös and E. Reimhult, Sensors, 8, 1400 (2008).

4. L. C. Clark and C. Lyons, Annals of the New York Academy of Sciences, 102, 29 (1962).

5. J. Wang, Sensors Update, 10, 107 (2002).

6. EPA, 2000-2001 Pesticide Market Estimates: Usage, in Pesticide Inductry Sales and Usage (2001).

7. T. Okumura, K. Suzuki, A. Fukuda, A. Kohama, N. Takasu, S. Ishimatsu and S. Hinohara, Academic Emergency Medicine, 5, 618 (1998).

8. D. M. Quinn, Chemical Reviews, 87, 955 (1987).

9. R. T. D. Giulio and D. E. Hinton, The toxicology of fishes, Taylor & Francis (2008).

10. K. Rekha, M. S. Thakur and N. G. Karanth, Critical Reviews in Biotechnology, 20, 213 (2010).

11. S. Yao, A. Meyer and G. Henze, Fresenius' Journal of Analytical Chemistry, 339, 207 (1991).

12. E. I. Rainina, E. N. Efremenco, S. D. Varfolomeyev, A. L. Simonian and J. R. Wild, Biosensors and Bioelectronics, 11, 991 (1996).

13. N. Jaffrezic-Renault, Sensors, 1, 60 (2001).

14. P. Mulchandani, W. Chen, A. Mulchandani, J. Wang and L. Chen, Biosensors and Bioelectronics, 16, 433 (2001).

15. M. Parveen and S. Kumar, Recent trends in the acetylcholinesterase system, IOS Press (2005).

16. E. Giacobini, Butyrylcholinesterase, Informa Health Care (2003).

17. G. Liu and Y. Lin, Analytical Chemistry, 78, 835 (2006).

18. I. Palchetti, A. Cagnini, M. Del Carlo, C. Coppi, M. Mascini and A. P. F. Turner, Analytica Chimica Acta, 337, 315 (1997).

19. D. Martorell, F. Céspedes, E. Martínez-Fàbregas and S. Alegret, Analytica Chimica Acta, 290, 343 (1994).

20. J. Diehl-Faxon, A. L. Ghindilis, P. Atanasov and E. Wilkins, Sensors and Actuators B: Chemical, 36, 448 (1996).

21. C. La Rosa, F. Pariente, L. Hernández and E. Lorenzo, Analytica Chimica Acta, 295, 273 (1994).

22. J. L. Marty, K. Sode and I. Karube, Electroanalysis, 4, 249 (1992).

23. G. Palleschi, M. Bernabei, C. Cremisini and M. Mascini, Sensors and Actuators B: Chemical, 7, 513 (1992).

24. P. Skladal and M. Mascini, Biosensors and Bioelectronics, 7, 335 (1992).

25. T. Imato and N. Ishibashi, Biosensors and Bioelectronics, 10, 435 (1995).

26. S. Kumaran and M. Morita, Talanta, 42, 649 (1995).

27. C. Tran-Minh, P. C. Pandey and S. Kumaran, Biosensors and Bioelectronics, 5, 461 (1990).

28. S. Kumaran and C. Tran-Minh, Analytical Biochemistry, 200, 187 (1992).

29. V. N. Arkhypova, S. V. Dzyadevych, A. P. Soldatkin, A. V. El'skaya, N. Jaffrezic- Renault, H. Jaffrezic and C. Martelet, Talanta, 55, 919 (2001).

30. A. M. Nyamsi Hendji, N. Jaffrezic-Renault, C. Martelet, A. A. Shul'ga, S. V. Dzydevich, A. P. Soldatkin and A. V. El'skaya, Sensors and Actuators B: Chemical, 21, 123 (1994).

31. D. P. Dumas, S. R. Caldwell, J. R. Wild and F. M. Raushel, J. Biol. Chem., 264, 19659 (1989).

32. W. W. Mulbry and J. S. Karns, Appl. Environ. Microbiol., 55, 289 (1989).

33. K. I. Dave, C. E. Miller and J. R. Wild, Chemico-Biological Interactions, 87, 55 (1993).

34. W. J. Donarski, D. P. Dumas, D. P. Heitmeyer, V. E. Lewis and F. M. Raushel, Biochemistry, 28, 4650 (1989).

35. M. J. Schöning, R. Krause, K. Block, M. Musahmeh, A. Mulchandani and J. Wang, Sensors and Actuators B: Chemical, 95, 291 (2003).

36. A. Mulchandani, P. Mulchandani, W. Chen, J. Wang and L. Chen, Analytical Chemistry, 71, 2246 (1999).

37. C. M.-H. Cho, A. Mulchandani and W. Chen, Appl. Environ. Microbiol., 70, 4681 (2004).

38. R. P. Deo, J. Wang, I. Block, A. Mulchandani, K. A. Joshi, M. Trojanowicz, F. Scholz, W. Chen and Y. Lin, Analytica Chimica Acta, 530, 185 (2005).

39. J. Wang, R. Krause, K. Block, M. Musameh, A. Mulchandani and M. J. Schöning, Biosensors and Bioelectronics, 18, 255 (2003).

40. S. H. Chough, A. Mulchandani, P. Mulchandani, W. Chen, J. Wang and K. R. Rogers, Electroanalysis, 14, 273 (2002).

41. V. Sacks, I. Eshkenazi, T. Neufeld, C. Dosoretz and J. Rishpon, Analytical Chemistry, 72, 2055 (2000).

42. J. Hong, A. A. Moosavi-Movahedi, H. Ghourchian, A. M. Rad and S. Rezaei-Zarchi, Electrochimica Acta, 52, 6261 (2007).

43. A. I. Yaropolov, M. R. Tarasevich and S. D. Varfolomeev, Bioelectrochemistry and Bioenergetics, 5, 18 (1978).

44. J. Zhao, R. W. Henkens, J. Stonehuerner, J. P. O'Daly and A. L. Crumbliss, Journal of Electroanalytical Chemistry, 327, 109 (1992).

45. T. Ruzgas, L. Gorton, J. Emnéus and G. Marko-Varga, Journal of Electroanalytical Chemistry, 391, 41 (1995).

46. V. J. Razumas, J. J. Jasaitis and J. J. Kulys, Bioelectrochemistry and Bioenergetics, 12, 297 (1984).

47. E. Csöregi, G. Jönsson-Pettersson and L. Gorton, Journal of Biotechnology, 30, 315 (1993).

48. M. Comtat and H. Durliat, Biosensors and Bioelectronics, 9, 663 (1994).

49. H. Durliat, A. Courteix and M. Comtat, Bioelectrochemistry and Bioenergetics, 22, 197 (1989).

50. O. Adeyoju, E. I. Iwuoha and M. R. Smyth, Talanta, 41, 1603 (1994).

51. H. B. Dunford and A. J. Adeniran, Archives of Biochemistry and Biophysics, 251, 536 (1986).

52. V. Vojinovic, R. H. Carvalho, F. Lemos, J. M. S. Cabral, L. P. Fonseca and B. S. Ferreira, Biochemical Engineering Journal, 35, 126 (2007).

53. R. Solná, S. Sapelnikova, P. Skládal, M. Winther-Nielsen, C. Carlsson, J. Emnéus and T. Ruzgas, Talanta, 65, 349 (2005).

54. F.-D. Munteanu, A. Lindgren, J. Emneus, L. Gorton, T. Ruzgas, E. Csoregi, A. Ciucu, R. B. van Huystee, I. G. Gazaryan and L. M. Lagrimini, Analytical Chemistry, 70, 2596 (1998).

55. J. Kulys, U. Bilitewski and R. D. Schmid, Bioelectrochemistry and Bioenergetics, 26, 277 (1991).

56. N. Peña, G. Ruiz, A. J. Reviejo and J. M. Pingarrón, Analytical Chemistry, 73, 1190 (2001).

57. M. H. Smit and A. E. G. Cass, Analytical Chemistry, 62, 2429 (1990).

58. T. Ruzgas, J. Emnéus, L. Gorton and G. Marko-Varga, Analytica Chimica Acta, 311, 245 (1995).

59. S. Huang, Y. Qu, R. Li, J. Shen and L. Zhu, Microchimica Acta, 162, 261 (2008).

60. S. C. Barton, Y. Sun, B. Chandra, S. White and J. Hone, Electrochemical and Solid-State Letters, 10, B96 (2007).

61. J. Wang, Electroanalysis, 17, 7 (2005).

62. M. Moniruzzaman, A. Sahin and K. I. Winey, Carbon, 47, 645 (2009).

63. I. R. Wheeldon, E. Campbell and S. Banta, J Mol Biol.

64. I. R. Wheeldon, J. W. Gallaway, S. C. Barton and S. Banta, Proceedings of the National Academy of Sciences, 105, 15275 (2008).

65. P. N. Bartlett and K. F. E. Pratt, Journal of Electroanalytical Chemistry, 397, 61 (1995).

66. V. Flexer, K. F. E. Pratt, F. Garay, P. N. Bartlett and E. J. Calvo, Journal of Electroanalytical Chemistry, 616, 87 (2008).

67. M. Lyons, Sensors, 6, 1765 (2006).

68. A. Kartono, Sulistan, E.,Mamat, M., Applied Mathematical Sciences, 4, 129901308 (2010).

69. R. Baronas, F. Ivanauskas and J. Kulys, Mathematical Modeling of Biosensors: An Introduction for Chemists and Mathematicians, Springer (2009).

70. A. Cambiaso, L. Delfino, M. Grattarola, G. Verreschi, D. Ashworth, A. Maines and P. Vadgama, Sensors and Actuators B: Chemical, 33, 203 (1996).

71. L. D. Mell and J. T. Maloy, Analytical Chemistry, 47, 299 (1975).

72. L. D. Mell and J. T. Maloy, Analytical Chemistry, 48, 1597 (1976).

73. M. Puida, . vanauskas and V. Lau inavičius, Journal of Mathematical Chemistry, 47, 191 (2010).

74. T. Schulmeister and D. Pfeiffer, Biosensors and Bioelectronics, 8, 75 (1993).

75. T. Rinken, Analytical Letters, 36, 1535 (2003).

76. D. Šimelevičius and R. Ba onas, Journal of Mathematical Chemistry, 47, 430 (2010).

77. M. R. Connor and J. C. Liao, Curr Opin Biotechnol, 20, 307 (2009).

78. H. Alper and G. Stephanopoulos, Nat Rev Microbiol, 7, 715 (2009).

79. F. Zhang, S. Rodriguez and J. D. Keasling, Curr Opin Biotechnol, 22, 775 (2011).

80. G. Stephanopoulos, Science, 315, 801 (2007).

81. International Energy Agency, in, www.worldenergyoutlook.org (2012).

82. G. Franceschin, A. Zamboni, F. Bezzo and A. Bertucco, Chemical Engineering Research and Design, 86, 488 (2008).

83. P. Schenk, S. Thomas-Hall, E. Stephens, U. Marx, J. Mussgnug, C. Posten, O. Kruse and B. Hankamer, BioEnergy Research, 1, 20 (2008).

84. D. Pimentel and T. W. Patzek, Natural Resources Research, 14, 65 (2005).

85. Z.-G. Zhu, Long, S.P., Ort, D.R., Current Opinion in Biotechnology, 19, 152 (2008).

86. D. A. Walker, Journal of Applied Phycology, 21, 509 (2009).

87. A. W. D. Larkum, Current Opinion in Biotechnology, 21, 271 (2010).

88. D. R. Bond and D. R. Lovley, Appl. Environ. Microbiol., 69, 1548 (2003).

89. F. Caccavo, Jr, D. J. Lonergan, D. R. Lovley, M. Davis, J. F. Stolz and M. J. McInerney, Appl. Environ. Microbiol., 60, 3752 (1994).

90. H. J. Kim, H. S. Park, M. S. Hyun, I. S. Chang, M. Kim and B. H. Kim, Enzyme and Microbial Technology, 30, 145 (2002).

91. K. Rabaey, P. Girguis and L. K. Nielsen, Current Opinion in Biotechnology, 22, 371 (2011).

92. M. H. Osman, A. A. Shah and F. C. Walsh, Biosensors and Bioelectronics, 26, 953 (2010).

93. H. D. Peck, Jr., Annu Rev Microbiol, 22, 489 (1968).

94. D. P. Kelly, Archives of Microbiology, 171, 219 (1999).

95. I. Suzuki, Annu Rev Microbiol, 28, 85 (1974).

96. K. Jha and J. W. Weidner, Journal of Applied Electrochemistry, 29, 1305 (1999).

97. D. Reyter, D. Bélanger and L. Roué, Electrochimica Acta, 53, 5977 (2008).

Figures

Figure 1 Schematic of the proposed detection scheme where the OP with a phenolic leaving group is hydrolyzed by OPH followed by detection of the phenolic compound by

HRP. A change in current is observed as the output.

Figure 2 Schematic of the reverse microbial fuel cell.

Appendix A

Energy Efficiency Calculations

The overall reaction of isobutanol production in N.europaea is shown below:

(A1)

Based on ATP generation coupled to O2 reduction using F1F0ATPase for every mole of

ATP 3 moles of electrons which is equivalent to 1.5 mol of NH3 is required. NADH generation via reverse electron transport powered by electrons from respiratory chain and proton motive force requires 2 moles of electrons which is equivalent to 1 mol of NH3 for every mole of NADH. Thus the overall NH3 and electron balance are shown below:

(A2)

We can calculate the thermodynamic minimum for number of electrons required to produce an isobutanol molecule, nthermo, by doing a material and energy balance around

- the bioreactor. The inlet stream to the bioreactor includes NH3, OH , CO2 and O2 and the

- outlet stream includes NO2 , C4H10O, H2O and O2.

- NO2 NH3 OH- C4H10O CO2 O2 H2O

O2 Bioreactor

Figure A1 Flowchart for the N.europaea bioreactor.

The unbalanced overall reaction around the bioreactor is shown below

(A3)

At equilibrium of (A3) needs to be zero using equation (A4)

(A4)

the Gibbs free energy for this reaction can be written as below:

(A5)

Gibbs free energy of formation for CO2, H2O, O2 and, C4H10O can be determined using tabulated values and are listed below for the standard state at 298K:

(A6a)

(A6b)

(A6c)

(A6d)

- Oxidation of NH3 to NO2 can be written as below:

(A7)

(A8)

The overall cell potential and the reaction are shown below

(A9)

The relationship between Gibbs free energy and cell potential is defined in equation

(A10)

Using this equation we can calculate Gibbs free energy for reaction (A9) as shown below

(A11)

- - Therefore Gibbs free energies for formation of NH3, NO2 and OH can be calculated as shown below

(A12)

We can rearrange this equation to get

(A13)

Substituting equation (A13) into equation (A5) and using values in (A6a)-(A6d) we get the following equation

(A14)

When we solve for nthermo we get:

(A15)

The balanced equation for the N.eurapaea bioreactor is

(A16)

Table A1 Summary of thermodynamic and estimated parameters used in efficiency calculations.

Ammonia nthermo 29 nbio 105

Vthermo (V) -0.91

Maximum Efficiency 29%

Proton Balance for the Overall System

Another important advantage for the rMFC is the proton balance of the overall system. Since the reduction reaction in the electrochemical reactor is exactly the reverse of the oxidation reaction in the bioreactor, there is no need to add protons to the system.

This helps to produce an economically feasible process. Figure A2 below shows the flowchart for the overall process. The inlets to the process are CO2, H2O and O2. The overall reactions for the bioreactor and the electrochemical reactor are shown in (A17) and (A18), respectively. Based on metabolic flux calculations 17.5 moles of protons are produced in the bioreactor and the same number of moles are consumed in the electrochemical reactor, therefore there is no need for proton addition to the system.

Figure A2 Flowchart for the N.europaea bioreactor and electrochemical reactor for reduction of nitrite to ammonia.

(A17)

(A18)

Chapter 2 A Dual Enzyme Electrochemical Assay for the Detection of

Organophosphorus Compounds using Organophosphorus Hydrolase and

Horseradish Peroxidase

Abstract

Neurotoxic organophosphorus (OP) compounds are commonly used as chemical warfare agents and pesticides. Due to their high toxicity, rapid and sensitive field detection of these compounds has been an ongoing topic of interest. Biosensors made with organophosphate hydrolase enzyme (OPH) are generally designed to either amperometrically detect an electroactive leaving group produced following enzymatic cleavage, or to potentiometrically detect the pH change that occurs during cleavage.

Since OPs are more likely to have phenolic leaving groups as compared to electroactive leaving groups, we have developed a new amperometric dual enzyme electrochemical assay that enables the detection of a broad class of OP compounds by using the OPH enzyme combined with horseradish peroxidase (HRP). The assay has been applied to the detection of dichlofenthion, which does not have an electroactive leaving group and is not a commonly investigated OPH substrate. Using reverse phase HPLC, we have determined the Michaelis-Menten kinetic parameters of an engineered OPH enzyme to be

-1 KM = 0.11 ± 0.02 mM and kcat = 0.046 ± 0.003 s with dichlofenthion as the substrate.

Detection of the phenolic leaving group from the OPH enzyme reaction using the HRP electrode is carried out at -50 mV vs. Ag/AgCl where the noise and background are low and interferences are negligible. After optimization of the solution pH, the dual enzyme biosensor was found to have a limit of detection (LOD) of 24 µM (7.56ppm), and a sensitivity of 0.095 ± 0.024 nA/ µM for dichlofenthion. By detecting the phenolic leaving

32 groups from the OP targets using the HRP electrode, this new electrochemical assay platform has the potential to detect a broad range of important OP compounds.

Introduction

Organophosphorus compounds (OPs) constitute a large fraction of the pesticides that are commercially available. They work by inhibiting acetylcholinesterase (AChE), resulting in an acute neurotoxic response in targeted pests (1); however, these pesticides can be highly toxic to non-targeted species as well. Nerve agents such as sarin, soman and VX also belong to this class of compounds. Sarin was used in terrorist attacks to the subway system in Tokyo in 1999 and resulted in hospitalization of 5500 victims, with twelve fatalities (2). Tabun and sarin were also used by Iraq in the first Persian Gulf War and against Kurdish rebels (3). In 1997, a chemical weapons convention asked all its members to destroy all chemical weapons within a 10 year period. This resolution was signed by 170 countries and ratified by 139 (3). However, the production of all OPs could not be banned due to their common use as pesticide. Due to their adverse environmental effects and high toxicity, sensitive, selective, portable and fast detection of

OPs has been a topic of interest.

Gas and liquid chromatography, enzyme-linked immunoabsorbant assays

(ELISAs) and various types of spectroscopy are some of the analytical techniques that have been utilized for detection of OPs (4-8). These methods can have very low limits of detection (LOD) and they are highly selective and sensitive, however, they require trained personnel, expensive instruments, and elaborate sample preparation; thus they are not readily employable for field detection.

Numerous biosensors have been developed in an attempt to satisfy these needs; and electrochemical biosensors are especially desirable for field applications since they are easy to manufacture and deploy, and potentially can be engineered to be highly selective and sensitive. Reported electrochemical OP sensors are based on either indirect detection by cholinesterase enzyme inhibition or by direct detection by the organophosphorus hydrolase enzyme (OPH) (9).

Potentiometric and amperometric cholinesterase-based sensors have been reported. Cholinesterase sensors based on potentiometry rely on the pH change caused by the production of acetic acid during hydrolysis (10, 11). Amperometric AChE sensors are based on the change in concentration of the electroactive product thiocholine, produced as a result of hydrolysis of acetylcholine (12-14). When OPs are present, AChE is inhibited and therefore less thiocholine is produced. AChE sensors coupled with choline oxidase (ChO) are also commonly employed. The product of this coupled enzyme reaction is hydrogen peroxide which is a very common analyte and can be detected using peroxidases such as horseradish peroxidase (HRP) (15). Cholinesterase-based biosensors have LODs comparable to the laboratory techniques listed previously, but they can suffer from time consuming sample preparation, slow response, and poor selectivity (5, 16).

Direct detection using OPH can be utilized for a broad class of OPs since OPH has been reported to hydrolyze a wide range of pesticides such as paraoxon, parathion and nerve agents such as sarin, soman, tabun and VX (17, 18). Amperometric and potentiometric electrochemical OPH biosensors have been reported in the literature.

Amperometric OPH biosensors rely on the detection of the electroactive product of the hydrolysis reaction (19-24). These sensors can achieve very low LODs but their use is

34 limited to the select few OPs with electroactive leaving groups. Most of the amperometric

OPH biosensors reported in literature are based on anodic detection of the hydrolysis product p-nitrophenol at high overpotentials. Due to high operating potentials, interferences constitute a problem as well as surface fouling caused by the phenolic groups (25). Potentiometric OPH biosensors rely on detecting the change in local pH as result of the hydrolysis reaction (19, 26-28). These sensors can be used for all OPs that can be hydrolyzed by OPH, but they do not have low LODs. Therefore, an electrochemical biosensor for field detection of OPs with low LOD and broad substrate selectivity to various OPs is needed.

The dual enzyme electrochemical assay presented here is a new approach to the electrochemical detection of OPs. The main goal of this chapter is to introduce this new method and demonstrate its application to an under investigated pesticide that does not have an electroactive leaving group. Since many OPs, such as dichlofenthion, prothiofos and EPBP (s-seven) have aromatic hydrolysis products, a dual enzyme electrode consisting of OPH and HRP can be used to detect these OPs (29). In this concept, the

OPH hydrolyzes the OP, and the phenolic hydrolysis products serve as mediators for electron transfer (see below), and are thus detected at the HRP electrode as shown in

Figure 1. This concept can also be applied to many other enzyme electrodes that have mediated electron transfer such as laccase, tyrosinase and other peroxidase-modified electrode structures (30-32).

HRP-based biosensors have been widely studied for detection of hydrogen peroxide since it is an important analyte produced in many coupled enzyme reactions.

The peroxidase cycle for HRP is shown below:

In reaction 1a, HRP is oxidized to Compound I in the presence of hydrogen peroxide. In the following reactions, Compound I is first reduced to Compound II and then back to native HRP. The electrons necessary for the reduction are either provided directly by the electrode surface or indirectly by means of electron mediators, shown as AH2 in reactions

1b-c. These mediators shuttle electrons between the electrode surface and HRP. Phenols

(33-36), aromatic amines (33, 36, 37), ferrocyanide (38), ferrocenes (39) and iodine can act as mediators for the HRP electrode. The change in the concentration of the mediators can be detected in the presence of a constant amount of hydrogen peroxide. This principle is widely used for detection of phenolic compounds and it can be carried out at operating potentials as low as -50 mV vs. Ag/AgCl. The large number of phenols listed in the literature as mediators for HRP (29) suggest that this approach should work for a large class of OPs. Table I shows a sample list of organophosphates that have electroactive leaving groups and phenolic leaving groups. There are only three OPs with electroactive leaving groups as opposed to 11 with phenolic leaving groups of which 6 have been reported to be detected by HRP biosensors. Therefore, the dual enzyme electrochemical assay has the potential to detect a large class of OPs. The dual enzyme assay also has low operating potentials where the background and interferences can be rendered negligible.

A significant challenge in the development of new OPH-based sensors is the OPH enzyme itself. This enzyme is generally not obtainable commercially, and it is a difficult protein to express in E. coli. This has resulted in a wide range of reported kinetic parameters for the enzyme as it can be difficult to obtain protein in large amounts with high activity. We have recently created OPH fusion proteins where we have added helical appendages to the enzyme to enable it to self-assemble into enzymatically active biomaterials (40). A benefit of this effort is that the mutant OPH enzymes express at significantly higher levels under standard culturing conditions, with high activity.

Therefore, this new OPH fusion protein was used to create the dual enzyme biosensors.

To demonstrate the possible broad detection capabilities of the dual enzyme electrochemical assay we chose a rarely studied model OP compound, dichlofenthion, a pesticide and nematicide, which can be hydrolyzed by OPH to produce the mediator, 2,

4-dichlorophenol, as shown in reaction 2. The mediator can then be detected using HRP electrodes (29, 41).

Dichlofenthion 2,4-dichlorophenol

Dichlofenthion hydrolysis by OPH has not been reported in the literature to the best of our knowledge. Thus, to confirm enzymatic activity of OPH with dichlofenthion we have also conducted kinetic studies using reverse phase high performance liquid

37 chromatography (HPLC). The results of the kinetics studies and optimization of the dual enzyme electrochemical assay for detection of dichlofenthion are presented in this chapter.

Experimental

Chemicals and Reagents

Dichlofenthion, 2,4-dichlorophenol (Fluka, 99% purity), horseradish peroxidase

(HRP) (Type VI-A), sodium phosphate monobasic and sodium phosphate dibasic were purchased from Sigma-Aldrich (99% purity). Hydrogen peroxide (30% w/v solution) was purchased from Fisher Scientific. The OPH fusion protein was expressed and purified in-house as described in Lu et al. (40). Lyophilized OPH was hydrated to an enzyme concentration of 75 mg/mL before use in electrochemical experiments and the activity was determined to be 1.6 ± 0.3 moles of substrate hydrolyzed per mol enzyme per second using the assay described in Lu et al. (40). All HPLC grade reagents including water, methanol, acetonitrile, and acetic acid were from Sigma. Glassy carbon electrode rods (5 mm diameter) were purchased from SPI Supplies (West Chester, PA).

Kinetic Studies

HPLC Kinetics

All samples were analyzed using a Waters 1525 series binary pump HPLC system equipped with a Waters 2998 photodiode array detector and temperature controlled column housing. Analyte separation was performed using a reverse-phase SUPELCO

LC-8 column (15 cm x 4.6 mm, 5 µm particle size, Sigma-Aldrich). The column was maintained at a constant tem e atu e of 30 ˚C. A g adient mobile hase was used consisting of methanol: acetic acid (A, 99:1 v/v) and water: acetic acid (B, 99:1 v/v).

Both mobile phases were degassed and filtered using 0.22 µm filtering systems. The flow was initially set to 35:65 A:B and changed linearly to 100:0 A:B over 20 min upon injection. Flow was restored to 35:65 A:B and held for 5 min before a new injection was made. The flow rate was held constant at 1.5 mL/min for all runs. The column effluent was monitored by UV detection at 280 nm. All data were recorded and analyzed using

Empower Pro software (Waters Corporation).

Sample Preparation

100µL of dichlofenthion working solution was transferred to a 1.5 mL centrifuge tube and was treated with 10 µL of 21 mg/mL OPH. The reaction was maintained at 25

˚C in a tem e atu e cont olled wate bath ( ishe ). The eaction was quenched at the desired time by the addition of 200 µl of methanol. The reaction products were spun down in 10kDa cellulose triacetate ultrafiltration tubes (VWR) at 5,000g for 5 min to remove the enzyme prior to injection. 25 µl injections of the product were analyzed by

HPLC for production of 2, 4-dichlorophenol.

Calibration Curve & Quality Control

Stock solutions of dichlofenthion were prepared in ethanol at concentrations of

0.25µl/mL. Stock solutions of 2, 4-dichlorophenol were prepared in reagent grade water at concentrations of 1.5 mg/mL. Working solutions at the desired concentrations of both were prepared in 50 mM Tris-HCl pH 9.0 just prior to use and kept on ice. Fresh

39 solutions were made daily. A calibration curve was prepared, accounting for methanol dilution, for the hydrolysis product, 2, 4-dichlorophenol, at concentrations ranging from

5-200 µM. Periodic injections of dichlofenthion were made to ensure minimal amounts of autohydrolysis.

Electrochemical Experiments

Electrode fabrication

The glassy carbon electrodes were polished with alumina slurry to a mirror-like finish and rinsed with deionized water. 15 µl of 10mg/mL HRP was cast on the electrode and left overnight at 4 oC. After overnight immobilization, the electrodes were rinsed thoroughly with buffer (42).

Experimental Procedure

A homemade microfluidic device connected to two pumps was used for detection experiments. The design of the microfluidic device was similar to the design described in

Willey et al. (43). An Ag/AgCl reference electrode was included in the channel and the glassy carbon working electrode was embedded in the plexiglass as the working electrode as shown in Figure 1D. The size of the channel was 5mm x 30mm. A gasket with 0.3 mm thickness and tubing with a 0.5mm outer diameter was used to construct the microfluidic device. The flow rate of the stream was set to 0.8 mL/min. The sample stream (100mM phosphate buffer pH 8.0, 40 µM hydrogen peroxide, and dichlofenthion) was split into two streams, one of which was used as background stream and the other as the analyte.

The analyte stream (5mL) was incubated with OPH enzyme (12.5 µL) for varying times

40 prior to injection. By switching between the streams and amperometrically measuring the reduction current at -50 mV vs. Ag/AgCl (3M NaCl), the difference between incubated analyte stream and background stream was determined. In all electrochemical experiments, a µAutolab Potentiostat (Ecochemie, Netherlands) was used to maintain a constant potential. A platinum wire was used for the counter electrode and an Ag/AgCl electrode was used as the reference electrode.

Results and Discussion

Kinetics

The hydrolysis of dichlofenthion to 2, 4-dichlorophenol by OPH follows simple

Michaelis-Menten kinetics. The kinetic data for various concentrations of dichlofenthion shown in Figure 2 were fit to the Michaelis-Menten equation using non-linear regression software (Sigma Plot, San Jose, CA). Since the pH optima for OPH and the dual enzyme sensor are 10.5 and 8.0 respectively (40), all kinetic data were collected at an intermediate pH of 9.0. Similar experiments were conducted using parathion instead of dichlofenthion to insure the accuracy of the proposed kinetic assay and the kinetic parameters for parathion were found to be very similar to those in the literature using the mutant OPH constructs (40) (data not shown). The kinetic parameters obtained using dichlofenthion are shown in Table II along with kinetic data for other OPs from the literature. Overall, the kinetic parameters indicate a rather poor activity of OPH for dichlofenthion with a turnover rate three to six orders of magnitude slower than what is reported in the literature for other OPH substrates (44, 45). Since the hydrolysis kinetics of dichlofenthion with wild type OPH has not been reported in the literature before, it is

41 not possible to assess the advantages and disadvantages of mutant OPH in this aspect.

However in our previous work (40 ) the hydrolysis kinetics of parathion, methyl parathion and paroxon with the mutant OPH and wild type data presented in the literature were compared and the advantages of the mutant OPH were discussed.

Electrochemical Assay Optimization

The proposed dual enzyme electrochemical assay relies on detection of the phenolic leaving groups of OPs that result from hydrolysis by OPH. Often, enzyme immobilization is one of the most important aspects of biosensor fabrication, but optimal design of an immobilized enzyme biosensor can be anticipated to be a function of the kinetic parameters. Since OPH has high turnover numbers for some pesticides and low turnover numbers for others (Table II), we decided to adopt an approach for this first generation device where we incubate the pesticide with OPH prior to injection (Figure

1c), which would allow for the construction of a biosensor that can be used for a wide variety of OPs regardless of their relative kinetics with the OPH enzyme.

To optimize and determine the analytical properties of this biosensor, the HRP modified glassy carbon electrode was initially optimized for detection of the phenolic mediator. In order to optimize this electrode, the effect of hydrogen peroxide concentration, flow rate, and pH were studied individually. As has been reported in the literature, we found a saturating hydrogen peroxide concentration (~40 µM), such that additional hydrogen peroxide did not lead to a further change in the reduction current

(41). The flow rate was also optimized for mediator detection (data not shown) and was found to be optimal at 0.8 mL/min. It has previously been reported that the optimum pH

42 for HRP activity is 7.0. This was confirmed in our experiments. The optimum pH for

OPH activity is 11.0 (40). Due to different pH optima of the two enzymes, pH optimization of the dual enzyme system was necessary. In order to optimize for pH, 100

µM dichlofenthion samples prepared in phosphate buffer, with pH values varying from

7.0 to 9.0, were incubated with OPH for 10 min and the change in reduction current upon injection of these samples was determined. The optimum pH was determined to be pH

8.0 (data not shown) and all the following experiments were performed at this pH.

Figure 3 shows the response of the HRP electrode to different mediator concentrations at pH 8.0. The LOD, defined as 3 times the standard deviation for the smallest concentration of sample detected divided by the sensitivity, is 1.36 µM, the sensitivity is 2.31 ± 0.09 nA/ µM and the linear range is 5-100 µM. At higher concentrations than 100 µM, a current plateau is reached (46), as evidenced by the signal measured for 500 µM 2, 4-dichlorophenol.

Figure 4 shows the response of the HRP electrode to injections of 2, 4- dichlorophenol (the mediator), OPH and dichlofenthion incubated with OPH for 10 min as compared to a background solution of dichlofenthion. Upon injection of OPH, the reduction current decreases slightly due to autohydrolysis of dichlofenthion in the background solution. When the electron mediator 2, 4-dichlorophenol is injected, an increase in reduction current is observed. The same behavior is observed upon injection of dichlofenthion incubated with OPH. We have previously found that splitting the sample stream and comparing treated and untreated stream reduced noise and other artifacts (47). Using the sample stream as the background solution for the OP sensor may

43 also reduce interference from autohydrolysis of the pesticide and from other phenolic compounds that might be present.

In order to construct a calibration plot for dichlofenthion, the effect of incubation time of dichlofenthion with OPH was determined. Samples from 100 to 700 second incubation times were injected to the HRP flow cell. Figure 5A shows the typical response from such an experiment. The peak heights were determined and plotted against time to observe the hydrolysis kinetics of OPH with dichlofenthion as shown Figure 5B.

The calibration curve for 2, 4-dichlorophenol was used to convert the measured currents upon injection of dichlofenthion incubated with OPH to mediator concentrations as shown in Figure 5C. Dichlofenthion concentrations used for the calibration plot were within the linear range for 2, 4-dichlorophenol detection determined above. Using the plateau values obtained by hyperbolic fits to the data shown in Figure 5B, a calibration curve was constructed, c.f., Figure 5C. The LOD, defined as the average value of the blank signal and three times its standard deviation, of the dual enzyme assay was determined to be 24 µM(7.56 ppm) and the sensitivity was 0.095 ± 0.024 nA/ µM (46).

Even though the LOD for dichlofenthion is higher than the values reported in the literature for other OPs such as parathion using amperometric and potentiometric methods, this is likely due to the poor activity of OPH with dichlofenthion rather than the detection method. LODs for various substrates with different electrochemical detection methods are listed in Table III. As observed in this table it is possible to achieve LODs at lower micromolar range, for example 0.1µM for methyl parathion, using amperometric detection of the electroactive leaving group p-nitrophenol(20). However as listed in column 3 this biosensor operates at 0.90V vs. Ag/AgCl as opposed to the assay presented

44 in this chapter which operates at 0.050V vs. Ag/AgCl. Potentiometric OPH biosensor reported in this table has a limit of detection of 5µM for Paraoxon and Chlorpyrifos (27).

On the other hand ACHE based biosensor has a LOD of 0.04nM but there are other challenges related to use of these biosensors such as interferences from other pesticides and long incubation and response time(48) .The main goal of this chapter is to present a novel sensing principle that has the potential to detect a large class of OP compounds at low operating potentials where interferences are negligible. The next steps needed to develop this approach will be the improvement of the LOD and sensitivity using protein engineering approaches as well as taking advantage of the hydrogel forming capabilities of the engineered OPH constructs (40). This would require further analysis of the effect of diffusion through the hydrogel material in order to obtain an optimal biosensor design.

However in this first generation dual enzyme biosensor we decided to choose a simple design that can be easily used to detect a wide range of pesticides including those with slow hydrolysis kinetics.

Conclusions

A new electrochemical assay based on the detection of phenolic products of hydrolysis reaction of OPs with OPH is demonstrated. Specifically, we exploit the fact that multiple phenolic compounds are electron mediators for the HRP electrode. This dual- enzyme assay can be extended to other compounds when the OPH hydrolysis product is an electron mediator. Hydrolysis kinetics of a model OP, dichlofenthion, with

OPH was studied using reverse phase HPLC. The Michaelis-Menten parameters KM and

-1 kcat were determined to be 0.11 ± 0.02 mM and 0.046 ± 0.003 s respectively. Due to slow hydrolysis kinetics of the model compound with OPH, a measurement scheme that

45 incubates the pesticide with OPH prior to injection was adopted. The next step in the development of this assay will be to test real life samples and possible interferences.

However the ability of the biosensor to operate at low potentials combined with a design that uses a comparison between split sample streams will likely eliminate false positive signals while enabling the biosensor to detect a broad range of OP compounds. The dual enzyme electrode (optimized for hydrogen peroxide concentration, flow rate and pH) achieved an LOD of 24 (7.56 ppm) µM, and a sensitivity of 0.095 ± 0.024nA/ µM. This new assay can be useful for stand-alone detection of OP compounds, or it may also be integrated into biomimetic toxicology sensors that aim to monitor the general safety of water samples (47, 49, 50).

Acknowledgments

This research was supported by U.S. Army Corps of Engineers Applied Research program with funding from the U.S. Army Engineering Research and Development

Center, Construction Engineering Research Laboratory (ERDC-CERL). The authors would also like to thank Mr. Hoang D. Lu and Dr Zengmin Li for their valuable contributions.

References

1. J. E. Chambers and S. F. Oppenheimer, Toxicological Sciences, 77, 185 (2004).

2. T. Okumura, K. Suzuki, A. Fukuda, A. Kohama, N. Takasu, S. Ishimatsu and S. Hinohara, Academic Emergency Medicine, 5, 618 (1998).

3. S. Chauhan, R. D'Cruz, S. Faruqi, K. K. Singh, S. Varma, M. Singh and V. Karthik, Environmental Toxicology and Pharmacology, 26, 113 (2008).

4. J. Sherma, Analytical Chemistry, 65, 40R (1993).

5. K. Rekha, M. S. Thakur and N. G. Karanth, Critical Reviews in Biotechnology, 20, 213 (2010).

6. X. Cao, S. V. Mello, R. M. Leblanc, V. K. Rastogi, T.-C. Cheng and J. J. DeFrank, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 250, 349 (2004).

7. X. Ji, J. Zheng, J. Xu, V. K. Rastogi, T.-C. Cheng, J. J. DeFrank and R. M. Leblanc, The Journal of Physical Chemistry B, 109, 3793 (2005).

8. J. Orbulescu, C. A. Constantine, V. K. Rastogi, S. S. Shah, J. J. DeFrank and R. M. Leblanc, Analytical Chemistry, 78, 7016 (2006).

9. M. Trojanowicz and M. L. Hitchman, TrAC Trends in Analytical Chemistry, 15, 38 (1996).

10. T. Imato and N. Ishibashi, Biosensors and Bioelectronics, 10, 435 (1995).

11. S. Kumaran and C. Tran-Minh, Analytical Biochemistry, 200, 187 (1992).

12. G. Liu and Y. Lin, Analytical Chemistry, 78, 835 (2006).

13. D. Martorell, F. Céspedes, E. Martínez-Fàbregas and S. Alegret, Analytica Chimica Acta, 290, 343 (1994).

14. K. A. Joshi, J. Tang, R. Haddon, J. Wang, W. Chen and A. Mulchandani, Electroanalysis, 17, 54 (2005).

15. J. Diehl-Faxon, A. L. Ghindilis, P. Atanasov and E. Wilkins, Sensors and Actuators B: Chemical, 36, 448 (1996).

16. N. Jaffrezic-Renault, Sensors, 1, 60 (2001).

17. D. P. Dumas, S. R. Caldwell, J. R. Wild and F. M. Raushel, J. Biol. Chem., 264, 19659 (1989).

18. W. J. Donarski, D. P. Dumas, D. P. Heitmeyer, V. E. Lewis and F. M. Raushel, Biochemistry, 28, 4650 (1989).

19. M. J. Schöning, R. Krause, K. Block, M. Musahmeh, A. Mulchandani and J. Wang, Sensors and Actuators B: Chemical, 95, 291 (2003).

20. J. Wang, R. Krause, K. Block, M. Musameh, A. Mulchandani and M. J. Schöning, Biosensors and Bioelectronics, 18, 255 (2003).

21. R. P. Deo, J. Wang, I. Block, A. Mulchandani, K. A. Joshi, M. Trojanowicz, F. Scholz, W. Chen and Y. Lin, Analytica Chimica Acta, 530, 185 (2005).

22. A. Mulchandani, P. Mulchandani, W. Chen, J. Wang and L. Chen, Analytical Chemistry, 71, 2246 (1999).

23. A. M. Sung Hyo Chough, Electroanalysis, 14, 273 (2002).

24. Y. Lei, P. Mulchandani, J. Wang, W. Chen and A. Mulchandani, Environmental Science & Technology, 39, 8853 (2005).

25. D. Fino, C. Jara, G. Saracco, V. Specchia and P. Spinelli, Journal of Applied Electrochemistry, 35, 405 (2005).

26. P. Mulchandani, A. Mulchandani, I. Kaneva and W. Chen, Biosensors and Bioelectronics, 14, 77 (1999).

27. S. Gaberlein, M. Knoll, C. Zaborosch and F. Spener, The Analyst, 125, 2274 (2000).

28. A. Mulchandani, P. Mulchandani, I. Kaneva and W. Chen, Analytical Chemistry, 70, 4140 (1998).

29. T. Ruzgas, J. Emnéus, L. Gorton and G. Marko-Varga, Analytica Chimica Acta, 311, 245 (1995).

30. E. Burestedt, A. Narvaez, T. Ruzgas, L. Gorton, J. Emnéus, E. Domínguez and G. Marko-Varga, Analytical Chemistry, 68, 1605 (1996).

31. K. R. Rogers, J. Y. Becker and J. Cembrano, Electrochimica Acta, 45, 4373 (2000).

32. A. I. Yaropolov, A. N. Kharybin, J. Emnéus, G. Marko-Varga and L. Gorton, Analytica Chimica Acta, 308, 137 (1995).

33. J. O. B. Dominique and H. B. DUNFORD, European Journal of Biochemistry, 66, 607 (1976).

34. V. Vojinovic, R. H. Carvalho, F. Lemos, J. M. S. Cabral, L. P. Fonseca and B. S. Ferreira, Biochemical Engineering Journal, 35, 126 (2007).

35. R. Solná, S. Sapelnikova, P. Skládal, M. Winther-Nielsen, C. Carlsson, J. Emnéus and T. Ruzgas, Talanta, 65, 349 (2005).

36. F.-D. Munteanu, A. Lindgren, J. Emneus, L. Gorton, T. Ruzgas, E. Csoregi, A. Ciucu, R. B. van Huystee, I. G. Gazaryan and L. M. Lagrimini, Analytical Chemistry, 70, 2596 (1998).

37. J. Kulys, U. Bilitewski and R. D. Schmid, Bioelectrochemistry and Bioenergetics, 26, 277 (1991).

38. N. Peña, G. Ruiz, A. J. Reviejo and J. M. Pingarrón, Analytical Chemistry, 73, 1190 (2001).

39. M. H. Smit and A. E. G. Cass, Analytical Chemistry, 62, 2429 (1990).

40. H. D. Lu, I. R. Wheeldon and S. Banta, Protein Engineering Design and Selection (2010).

41. S. Huang, Y. Qu, R. Li, J. Shen and L. Zhu, Microchimica Acta, 162, 261 (2008).

42. T. Ruzgas, L. Gorton, J. Emnéus and G. Marko-Varga, Journal of Electroanalytical Chemistry, 391, 41 (1995).

43. M. J. Willey, U. Emekli and A. C. West, Journal of The Electrochemical Society, 155, D302 (2008).

44. C. M.-H. Cho, A. Mulchandani and W. Chen, Appl. Environ. Microbiol., 70, 4681 (2004).

45. E. N. Efremenko and V. S. Sergeeva, Russian Chemical Bulletin, 50, 1826 (2001).

46. A. Gustavo González and M. Ángeles Herrador, TrAC Trends in Analytical Chemistry, 26, 227 (2007).

47. X. J. Chen, A. C. West, D. M. Cropek and S. Banta, Analytical Chemistry, 80, 9622 (2008).

48. J. J. Rippeth, T. D. Gibson, J. P. Hart, I. C. Hartley and G. Nelson, Analyst, 122, 1425 (1997).

49. J. Wu, D. M. Cropek, A. C. West and S. Banta, Anal Chem, 82, 8235 (2010).

50. I. E. MacAllister, M. G. t. Jakoby, B. Geryk, R. L. Schneider and D. M. Cropek, Analyst, 134, 320 (2009).

Tables and Figures

Table I A sample list of organophosphates that can have electroactive group and/or phenolic leaving group.

Electroactive Leaving Phenolic Leaving Organophosphate Group Group

Parathion X

Paroxon X

Methyl Parathion X

Dichlofenthion X

Prothiofos X

DEPP X

Aminoparathion X

SV5 X

EBPB S-seven X

Coumaphos X

Malathion

Dicapthon X

Fenitrothion X

Ronnel X

Azinphosmethyl X

Table II Summary of kinetic parameters for the hydrolysis of several OP substrates by

OPH

-1 OP Substrate OPH pH kcat (s ) KM (mM)

-1 -1 Enzyme kcat/KM (M ·s )

Dichlofenthion a Mutant b 9.0 0.046 ± 0.11 ± 0.02 4.2 ± 0.9 x 102

0.003

Parathion (37) Mutant 10.5 1.1 ± 0.1 0.050 ± 2.3 ± 0.2 x 104

0.005

Sarin (42) WT 8.0 56 0.70 8.0 x 104

Chlorpyrifos WT 9.0 73.8 0.26 2.8 x 105

(41)

Paraoxon (42) WT 8.0 3170 0.058 5.5 x 107

a Current work b Mutant constructs are fusion proteins containing leucine zipper domains as previously discussed

Table III Limit of detection of OP biosensors for various OPs.

OP Substrate Enzyme Electrochemical LOD References

Method

Dichlofenthion Mutant Amperometric 24 µM This work.

OPH a 0.050V vs. Ag/AgCl

Methyl OPH Amperometric 0.8µM (1)

Parathion 0.90V vs. Ag/AgCl

Parathion OPH Amperometric 3.43nM (2)

0.9 V vs. Ag/AgCl

Paraoxon OPH Amperometric 0.1 µM (3)

0.75 V vs. Ag/AgCl

Methyl OPH Amperometric 1 µM (4)

Parathion 0.9V vs. Ag/AgCl

Paraoxon OPH Potentiometric 5 µM (5)

Chlorpyrifos OPH Potentiometric 5 µM (5)

Paraoxon ACHE Amperometric 0.04nM (6)

0V vs. Ag/AgCl a Mutant constructs are fusion proteins containing leucine zipper domains as previously discussed.

Figure 1 Schematic of the dual enzyme biosensor. OPH hydrolyzes dichlofenthion to produce 2, 4-dichlorophenol, which is an electron mediator for the HRP electrode. By operating in the presence of H2O2, the change in current is correlated to dichlofenthion concentration. Panel C shows a schematic of implementation: the sample is split into two, and one stream is incubated with OPH. The correlation is developed based on differences in current, effectively reducing background noise and other interferences.

Schematic and photograph of the microfluidic system.

Figure 3 Calibration curve showing change in current as a function of 2, 4- dichlorophenol concentration in 100 mM phosphate buffer at a pH 8.0.

90 seconds. In each experiment, the background solution contained 100µM dichlofenthion. In the first case (- - - -), the sample contained only 0.19 mg/mL OPH indicating that OPH itself does not increase current. The heavy solid line shows the increase in reduction current from a sample of 100µM dichlofenthion incubated with 0.19 mg/mL OPH. The lighter solid line shows a positive control that a sample containing 20

µM 2, 4-dichlorophenol does indeed cause a significant change in current.

Chapter 3 Design Rules for the Development of Electrochemical Biosensors using

Immobilized Enzymes

Abstract

Electrochemical biosensors are commonly used for industrial, environmental and medical applications. However there are no clear guidelines for the design of electrochemical biosensors employing immobilized enzymes that will produce a targeted linear range, limit of detection and sensitivity. Such guidelines can be provided using simulation tools that assess sensor feasibility prior to extensive development. In this chapter a model for reaction-diffusion through an immobilized enzyme film is demonstrated. The model is resolved numerically using a finite difference method to solve the reaction-diffusion equations. Dimensionless numbers that can be used to study the effects of enzyme loading, enzymatic gel thickness, and oxidation/reduction kinetics at the electrode on biosensor performance were identified using Michaelis-Menten enzyme kinetics. Using the dimensionless numbers identified in this chapter, and the plots representing the effects of these dimensionless numbers on design goals, researchers can assess the feasibility of a proposed immobilized enzyme biosensor. Results from an electrochemical formaldehyde biosensor with more complex enzyme kinetics (ordered bi-bi with substrate inhibition) utilizing a self-assembling proteinaceous hydrogel were included to demonstrate the application of some of the guidelines presented.

Introduction

Biosensors face increasing demand for selective and sensitive detection of different molecules for industrial, environmental and clinical applications(1-4). They are an affordable

59 alternative to laboratory techniques that require trained personnel, expensive equipment and possibly delayed response time. Electrochemical biosensors are especially desirable for use in field applications because of their compact design, ease of manufacture, real time response, sensitivity and selectivity (3-6). They are used in many applications ranging from glucose detection to detection of neurotoxic agents (1, 6, 7). There are many different sensing modalities that can be used for biosensor development. Here we focus on biosensors that employ immobilized enzymes and the electrochemical detection of the enzymatic reaction.

Depending on the specific application of an enzymatic biosensor, the goal might be to design for a target limit of detection, linear range and/or sensitivity. In Figure 1, important parameters that affect these goals are listed and include transport of the substrate and the product through the immobilized enzyme layer, oxidation/ reduction kinetics at the electrode, enzyme activity and loading and operating conditions such as pH and temperature. Of these parameters optimizing the enzyme loading and activity has been a major challenge and it depends primarily on the enzyme immobilization method. Different methods such as chemical modification of the electrode surface, entrapment in a membrane and physical absorption are commonly used to create enzyme layers on electrodes (8). Two approaches that researchers focus on to improve enzyme loading and activity are 1) designing new surfaces to improve enzyme immobilization and 2) engineering enzymes for increased activity, increased loading, and/or improved communication with the electrode. One way to improve surface properties is using nanoscale- featured surfaces. In the last decade, carbon nanotubes became popular for use in enzyme immobilization in biofuel cells and biosensors as a result of their high surface area, electrical conductivity and stability (9-12). Increases of ~100-fold in surface area have been reported in the literature (11). Such increases lead to large surface areas available for enzyme immobilization

60 and thus can result in low limits of detection and high sensitivities. An example of engineering enzymes to have increased loading is self-assembling proteinacious hydrogels. These enzymatic hydrogels are engineered by appending alpha helical leucine zippers to the termini of the proteins to add the self-assembly functionality(13-15). The self-assembling hydrogels allow for high enzyme loadings without the decrease in enzymatic activity that commonly occurs upon the addition of chemical crosslinking agents such as glutaraldehyde.

Such advances in designing novel electrode surfaces and engineering enzymes to maximize enzyme loading and activity are essential to improving biosensor performance but at the same time they broaden the operational space and thus extend biosensor development time.

The guidelines provided in this chapter can be used to optimize parameters that affect biosensor performance and can reduce experimental development time significantly. Additionally these guidelines can be utilized to predict sensitivity, linear range and limit of detection (LOD)s, prior to extensive sensor development.

Numerical and analytical solutions to the reaction-diffusion equations have been presented for different cases by many authors(16-20). Analytical solutions are available for limiting cases, whereas numerical solutions were used to determine and optimize a wide range of experimental parameters(21). Many of the earlier studies have focused on optimizing glucose biosensors where the enzyme was entrapped in a redox hydrogel (22, 23). Simple Michaelis-

Menten kinetics was used to model the enzyme kinetics, and first order kinetics between the mediator and the electrode were assumed(16, 23). The effect of experimental parameters on the response at steady state and during a transient were studied (19). Especially the behavior of the glucose sensor in the diffusion limited regime was analyzed since this leads to an extended linear range(22, 24) . Substrate and product inhibition in an enzyme with first order reaction kinetics,

(25) diffusion through a semi-permeable outer membrane(26, 27) and data analysis to determine kinetic constants and enzyme activity (28) were also studied by different groups.

Even though solutions to reaction-diffusion equations have been presented for many cases there is a lack of general guidelines on how to design enzymatic biosensors with a targeted linear range, LOD or sensitivity. The use of novel immobilization surfaces and proteinaceous hydrogels in biosensor development underlines the necessity for such general guidelines that can be applied to biosensors with significantly different characteristics such as high enzyme loadings and enhanced enzyme activity.

In this chapter we use a finite difference method to outline general design criteria for electrochemical biosensors with an immobilized enzyme layer. Dimensionless numbers that affect the linear range, sensitivity and LOD are identified. Changes in these design targets as a function of dimensionless numbers are presented in plots that provide a powerful tool as a starting point for biosensor design for researchers. The general criteria described in this chapter were derived using Michaelis-Menten rate equation. However using the model development described in this chapter, researchers can identify dimensionless numbers for systems with more complex kinetics with saturating enzyme kinetics. This is demonstrated in the results and discussion section for a formaldehyde sensor. A nicotinamide adenine dinucleotide (NAD+) dependent aldehyde dehydrogenase (ALDH) with ordered bi-bi enzyme kinetics was engineered to self-assemble into a proteinaceous hydrogel. The effect of gel thickness on the response of this biosensor was investigated. Additionally the guidelines outlined in this chapter were used to pinpoint parameters that can be changed to improve sensitivity, LOD and linear range of this sensor.

Model Description

The schematic of the system modeled in this study is shown in Figure 2. An aqueous drop containing substrate (S) is placed on the electrode with an immobilized enzyme layer. As the substrate diffuses through the enzyme layer it reacts with the enzyme to form product (P).

The product then diffuses through the gel, and if it is electroactive, is oxidized or reduced at the electrode. When modeling this system we used Michaelis-Menten equation to describe the kinetics within the enzyme layer and cou led it with ick’s law to desc ibe the diffusion of the product and the substrate as shown in equations (1-2):

(1)

(2)

where cp, cs , Dp and Ds represent the concentrations and diffusion coefficients of the product and the substrate, respectively. kcat is the catalytic rate constant in the Michaelis-Menten mechanism,

[E] is enzyme loading, and is Michaelis constant for the substrate. Equations (1-2) were then made dimensionless using:

, , , (3)

* * * * where L is the thickness of the enzyme layer, t , cs , cp and z are dimensionless time, substrate and product concentration and distance from the electrode. The following dimensionless equations were obtained:

(4)

(5)

where and are defined below:

(6)

and physically represent the ratio of the enzymatic reaction rate to diffusion rate through the gel and are usually approximately equal since the magnitude of substrate and product diffusion coefficients are similar ( in this section we will assume and are the same and we will use to refer to both ). is an important design parameter that can be used to determine optimal experimental parameters such as enzyme layer thickness and enzyme loading. The

dimensionless flux of product ( defined below) at the electrode varies largely depending on

. In order to illustrate this effect, the product flux at the electrode as a function of dimensionless bulk concentration at steady state for a range of values of is shown in Figure 3.

This is essentially a dimensionless calibration curve since the flux of electroactive product to the surface is proportional to signal and dimensionless bulk concentration is proportional to the bulk analyte concentration. The initial slope is independent of , however, the plateau is reached at

different concentrations in each case. As increases, the linear range of the biosensor increases. Therefore operating at high is favorable to have extended linear ranges. However, as increases, the response time increases as well. The inset of figure 3 shows the time it takes reach steady state concentrations at different values. Therefore the response time and the desired linear range need to be optimized simultaneously. In order to account for the response time in biosensor design without using transient simulations one can use as order of magnitude estimation for enzyme layer thickness as shown below:

(7)

where is a response time chosen for the biosensor. Equation 7 provides a starting point for design. Optimized enzyme loading or enzyme layer thickness can be determined based on the targeted response time and the mass transfer and reaction kinetics of the biosensor.

Another important design parameter is the dimensionless number, α, which represents the ratio of charge transfer resistance at the electrode to the mass transfer in the enzymatic gel:

(8)

where n is the number of electrons, F is a aday’s constant and di/dcp is the change in current density at the electrode surface with respect to a change in the product concentration. This term represents the sensitivity of the calibration curve for the product at a bare electrode and can be

determined experimentally from calibration curves. is derived from the boundary condition for the oduct at the elect ode defined using a aday’s Law as shown in equation 9:

(9)

where i is current density. This boundary condition can be made dimensionless using equation

(3) and re-written as shown below by substituting, α .

(10)

The concentration profile for the product within the enzyme layer for different values of α is shown in Figure 4. This illustrates that α has a large effect on the concentration of the product at the electrode and thus the signal. In figure 5, a dimensionless calibration curve is shown for different values of α. It is obse ved that as α inc eases, the dimensionless sensitivity, s, which is the slope of the dimensionless calibration curve as defined in equation 11 below increases.

(11)

Figure 6 shows the dimensionless sensitivity, as a function of α and it is observed that after an initial increase in sensitivity a plateau is reached, at which point improving the ratio of charge transfer resistance to the internal mass transfer resistance does not further improve the sensitivity.

In summary α plays an important role in the determination of the sensitivity of a biosensor. Higher values for α are desirable in order to achieve higher sensitivities. Sensitivity is also inversely proportional to LOD which can be defined as the three times the standard deviation of the lowest concentration that can be detected divided by the sensitivity(29). The linear range of the biosensor depends on , and higher is preferable to obtain large linear ranges. However response time should also be factored into the decision when choosing . This can be achieved using the inequality described in equation 7. These guidelines are summarized in the table I.

Experimental

Materials and Methods

Formaldehyde solution (36.5%), β-Nicotinamide adenine dinucleotide hydrate (NAD+) and β-NADH disodium hydrate were purchased from Sigma. Methylene Blue hydrate was purchased from Fluka. Screen-printed carbon (SP-C) electrodes were obtained from PINE

Instruments (Raleigh, NC). A human aldehyde dehydrogenase (ALDH) that has been previously engineered to form proteinaceous hydrogels was expressed and purified as described in Kim et al and the kinetic parameters, which are listed in table II, were determined at a pH of 7.4 using the assay as previously described (30). All other chemicals were analytical grade and obtained from various commercial sources.

Preparation of Methylene Blue (MB) Film on SP Carbon Electrode Surface

The thin films of polymethylene blue (PMB) were electrolytically deposited on 0.12 cm2 surface area of screen printed (SP) working carbon electrode according to a procedure described previously (31). In short, methylene blue film was obtained from a solution containing 1.0 mM of methylene blue, 20 mM sodium borate buffer (pH 9.14) and 0.1 M KCl. The process of polymerization included 10 scans of cyclic voltammetry from +1.2 V to –0.4 V at a scan rate of

0.05 V/s The electrode was rinsed with water three times to remove excess MB, and was air dried before further modification. This immobilization produces relatively stable and reproducible preparations of MB films.

Electrochemical Formaldehyde Biosensor

ALDH catalyses the oxidation of formaldehyde to formic acid while NAD+ is reduced to

NADH according to reaction (12).

+ − + HCHO + NAD + 3H2O  HCOO + NADH + 2H (12a)

H+ + NADH + MB (ox)  NAD+ + MB (red) (12b)

MB (red) MB (ox) + 2e− + 2H+ (12c)

The reaction is monitored by the oxidation of NADH using methylene blue as an electrochemical mediator (12c). To prepare ALDH/NAD-functionalized electrodes, a hydrogel of ALDH was prepared by mixing lyophilized ALDH and 5 mM NAD+ solution in a ratio of 1:

0.004 (25 wt%). 0.5 - 3.2 mg of ALDH and 5 mM NAD+ were used for gel-film preparation on the working electrode surface. The fresh prepared gel of ALDH/NAD+ was put on the SP-C MB modified working electrode surface. The coating thicknesses of ALDH films were controlled

68 using Kapton tapes of known thickness and leveling the deposited gel to this thickness using a razor blade.

Electrochemical measurements (cyclic voltammetry and chronoamperometry) were performed using a potentiostat (Metrohom Autolab). All measurements were done at room temperature in a single compartment cell (volume 0.1 mL) with screen-printed carbon working and counter electrodes and a standard Ag/AgCl reference electrode. Amperometric measurements of background and analytical signals were carried out at an applied potential of

0.35 V vs. Ag/AgCl. The analytical signal was initiated by adding 5 µl of 50 mM formaldehyde prepared in 0.1M phosphate buffer (pH 7.4) on the top of gel membrane.

Results and Discussion

A formaldehyde biosensor was designed using an NAD(H)-dependent aldehyde dehydrogenase to demonstrate the use of dimensionless parameters defined in the model development section. The sensitivity, linear range and LOD of this sensor are inferior compared to formaldehyde sensors reported in the literature(2, 32). The guidelines outlined in table II were used to pin point experimental parameters that can be altered to improve this biosensor.

ALDH follows the ordered bi-bi kinetic mechanism as shown below in figure 7. The oxidized cofactor (NAD+), A, and substrate (formaldehyde), S, react with the enzyme to form reduced cofactor (electroactive product, NADH), Q, and product (formate), P. The rate equation for this mechanism is :

(13)

where ν is the rate of the reaction, cA is the concentration of the cofactor (NAD+), cs is concentration of the substrate (formaldehyde), Kia is the dissociation constant for cofactor A, Ks and KA are Michaelis constants for substrate and cofactor, respectively and Vmax is the product of kinetic parameter kcat and enzyme loading [E]. The formaldehyde sensor presented in this paper is operated at high concentrations of formaldehyde where substrate inhibition is observed.

Therefore the following form of the ordered bi-bi equation with substrate inhibition term, , is used to study this sensor (33).

(14)

Using equation 14 instead of Michaelis-Menten kinetics, equations similar to equations 1 and 2 are written below for the substrate, oxidized cofactor and the reduced cofactor (electroactive product).

(15)

(16)

(17)

A similar equation can be written for formate, shown as P in figure 7, if formate is detected at the electrode instead of the reduced cofactor. Equations 15-17 are made dimensionless using equation 18 below.

, , , (18)

(19)

(20)

(21)

Dimensionless form of the ordered bi-bi equation with substrate inhibition is shown in equations

19-21 where is the same as equation 6 and assuming and are equal:

(22)

At the electrode surface the flux of substrate is zero and the flux of cofactor and the electroactive product are defined by the oxidation kinetics at the electrode similar to equation (10). Again if we assume and are equal, we get the following boundary conditions.

(23)

At the enzymatic gel/solution interface the concentration of substrate is equal to its bulk concentration and the oxidized cofactor and the reduced cofactor are assumed to be trapped in the gel with zero flux at this boundary.

(24)

These boundary conditions are then made dimensionless using equation (18).

(25)

(26)

The kinetic constants, diffusion coefficients and other parameters used in this study are listed in

Table II.

Figure 8 shows experimental and predicted steady state current responses as a function of gel thickness. At gel thicknesses below 50 µm the response of the biosensor increases with increasing gel thickness. This suggests at this region the biosensor is limited by enzyme kinetics and thus . The plot goes through a maximum at 50 µm and the current decreases with increasing gel thickness. In this region the response of the biosensor is limited by the diffusion,

>>1. To fit this data an apparent enzyme concentration of 3.4 µM was used which is 1000 fold smaller than the concentration of enzyme present in the gel. This is predicted to be due to the unusually large ratio of enzyme concentration to cofactor concentration in the enzymatic gel and differences in enzyme activity compared to kinetics measured in solution. Previously

Wheeldon et al. reported decrease in activity of AdhD from Pyrococcus furiosus engineered to

72 form proteinacious hydrogel (34). The reason of this decrease was attributed to either structural change of the active site or diffusion limitations within the gel. The ratio of reduction at the electrode to diffusion, α, is also very important in biosensor design. When designing a biosensor it is preferable to operate at large α as described by figure 6 and table 1. This ensures that the biosensor response is not controlled by electrode kinetics.

Using the simulations, one can also construct a calibration curve for this biosensor, predict the sensitivity and linear range, and make suggestions to potentially improve these parameters. Figure 9 shows the calibration curve at an enzyme gel thickness of 50 µm. This calibration curve has an unusual shape. At low concentrations the current increases as a function of formaldehyde concentration up to 1.2 mM . The slope at this region is 74 nA/mM and it is determined by the value of α as discussed earlier. At formaldehyde concentrations between 1.2 mM and 50 mM a different slope is observed. This is followed by decay in response at formaldehyde concentrations higher than 50 mM. This decrease is probably due to inhibition of the enzyme at such high concentrations of substrate. In order to improve the performance of the biosensor, the guidelines outlined in the model development section can be utilized. To increase the sensitivity, and also the LOD of this sensor, α should be increased. Based on Figure 6, α can be increased to 10. This can be accomplished by either by choosing another mediator with higher di/dc value or by increasing the enzymatic gel thickness. Increasing the enzyme gel thickness would also affect and therefore the optimal thickness would need to be recalculated.

Conclusions

Many groups have used reaction-diffusion model to explain experimental behavior of enzymatic biosensors. However there are no clear guidelines on how to design a biosensor with a

73 target sensitivity, linear range and LOD. In this chapter such guidelines are outlined for a biosensor made with an enzyme that follows Michaelis-Menten kinetics. Dimensionless variables that affect the linear range, sensitivity and LOD have been identified. Plots showing the effects of these dimensionless variables on design targets are presented. The dimensionless number, α, has been identified to affect sensitivity and the LOD of a biosensor. The linear range and the response time were determined by , which represents the ratio of enzyme kinetics to internal mass transfer resistances. An experimental formaldehyde sensor with ordered bi-bi kinetics with substrate inhibition was designed to demonstrate the use of criteria outlined in the model development section. Additionally parameters that can be rationally altered to improve performance were discussed.

Acknowledgements

The authors would also like to thank Dr Yang Hee Kim for enzyme expression, purification and activity measurements, Dr Elliot C Campbell for kinetic data fitting and Damla Eroglu for help with modeling.

NOMENCLATURE

3 cA concentration of reactant A , mol/cm

3 cp concentration of product, mol/cm

* cp dimensionless product concentration

3 cs concentration of substrate, mol/cm

* cs dimensionless substrate concentration

3 csbulk concentration of substrate in bulk solution, mol/cm

csbulk* dimensionless concentration of substrate in bulk solution

2 DA diffusion coefficient of cofactor, cm /s

2 Dp diffusion coefficient of product, cm /s

2 DQ diffusion coefficient of product, cm /s

2 Ds diffusion coefficient of substrate, cm /s

F Faraday's constant, C/mol i current density, A/cm2

KA Michaelis constant for reactant A, mM

KS Michaelis constant for reactant S, mM

Kia dissociation constant for reactant A, mM

Kis inhibition constant for reactant S, mM

-1 kcat catalytic rate constant in Michaelis-Menten mechanism, s

L thickness of enzyme layer, cm n number of electrons transferred in reaction s dimensionless sensitivity t time, s

75 t* dimensionless time

Vmax product of the kinetic parameter kcat and enzyme loading, mM/s z distance from the electrode surface, cm z* dimensionless distance from the electrode surface

Greek

α dimensionless ratio of the charge transfer resistance to the internal mass transfer

resistance and the enzyme kinetics

ν rate of enzymatic reaction, mM/s

ΦA dimensionless ratio of the enzymatic reaction rate to diffusion rate of cofactor

Φp dimensionless ratio of the enzymatic reaction rate to diffusion rate of substrate

Φs dimensionless ratio of the enzymatic reaction rate to diffusion rate of product

τdesign desired response time for the biosensor, s

References

1. J. Wang, R. Krause, K. Block, M. Musameh, A. Mulchandani and M. J. Schöning, Biosensors and Bioelectronics, 18, 255 (2003).

2. M. J. Dennison, J. M. Hall and A. P. F. Turner, Analyst, 121, 1769 (1996).

3. J. Wu, D. M. Cropek, A. C. West and S. Banta, Anal Chem, 82, 8235 (2010).

4. X. J. Chen, A. C. West, D. M. Cropek and S. Banta, Analytical Chemistry, 80, 9622 (2008).

5. Y. Lei, P. Mulchandani, J. Wang, W. Chen and A. Mulchandani, Environmental Science & Technology, 39, 8853 (2005).

6. A. Sahin, K. Dooley, D. M. Cropek, A. C. West and S. Banta, Sensors and Actuators B: Chemical, 158, 353 (2011).

7. A. E. G. Cass, G. Davis, G. D. Francis, H. A. O. Hill, W. J. Aston, I. J. Higgins, E. V. Plotkin, L. D. L. Scott and A. P. F. Turner, Analytical Chemistry, 56, 667 (1984).

8. R. A. Sheldon, Advanced Synthesis & Catalysis, 349, 1289 (2007).

9. R. P. Deo, J. Wang, I. Block, A. Mulchandani, K. A. Joshi, M. Trojanowicz, F. Scholz, W. Chen and Y. Lin, Analytica Chimica Acta, 530, 185 (2005).

10. S. Huang, Y. Qu, R. Li, J. Shen and L. Zhu, Microchimica Acta, 162, 261 (2008).

11. S. C. Barton, Y. Sun, B. Chandra, S. White and J. Hone, Electrochemical and Solid-State Letters, 10, B96 (2007).

12. J. Wang, Electroanalysis, 17, 7 (2005).

13. M. Moniruzzaman, A. Sahin and K. I. Winey, Carbon, 47, 645 (2009).

14. I. R. Wheeldon, E. Campbell and S. Banta, J Mol Biol.

15. I. R. Wheeldon, J. W. Gallaway, S. C. Barton and S. Banta, Proceedings of the National Academy of Sciences, 105, 15275 (2008).

16. P. N. Bartlett and K. F. E. Pratt, Journal of Electroanalytical Chemistry, 397, 61 (1995).

17. V. Flexer, K. F. E. Pratt, F. Garay, P. N. Bartlett and E. J. Calvo, Journal of Electroanalytical Chemistry, 616, 87 (2008).

18. M. Lyons, Sensors, 6, 1765 (2006).

19. A. Kartono, Sulistan, E.,Mamat, M., Applied Mathematical Sciences, 4, 129901308 (2010).

20. R. Baronas, F. Ivanauskas and J. Kulys, Mathematical Modeling of Biosensors: An Introduction for Chemists and Mathematicians, Springer (2009).

21. A. Meena and L. Rajendran, Journal of Electroanalytical Chemistry, 644, 50 (2010).

22. A. Cambiaso, L. Delfino, M. Grattarola, G. Verreschi, D. Ashworth, A. Maines and P. Vadgama, Sensors and Actuators B: Chemical, 33, 203 (1996).

23. L. D. Mell and J. T. Maloy, Analytical Chemistry, 47, 299 (1975).

24. L. D. Mell and J. T. Maloy, Analytical Chemistry, 48, 1597 (1976).

25. D. Šimelevičius and R. Ba onas, Journal of Mathematical Chemistry, 47, 430 (2010).

26. M. Puida, . vanauskas and V. Lau inavičius, Journal of Mathematical Chemistry, 47, 191 (2010).

27. T. Schulmeister and D. Pfeiffer, Biosensors and Bioelectronics, 8, 75 (1993).

28. T. Rinken, Analytical Letters, 36, 1535 (2003).

29. A. Gustavo González and M. Ángeles Herrador, TrAC Trends in Analytical Chemistry, 26, 227 (2007).

30. Y. H. Kim, Campell E, Meredith M,Minteer S D, Banta S, Angewandte Chemie Int. Ed (in Press) (2012).

31. A. A. Karyakin, A. K. Strakhova, E. E. Karyakina, S. D. Varfolomeyev and A. K. Yatsimirsky, Bioelectrochemistry and Bioenergetics, 32, 35 (1993).

32. O. Demkiv, O. Smutok, S. Paryzhak, G. Gayda, Y. Sultanov, D. Guschin, H. Shkil, W. Schuhmann and M. Gonchar, Talanta, 76, 837 (2008).

33. L. Vladimir, Comprehensive Enzyme Kinetics, Kluwer Academic/ Plenum Publishers New York (2003).

34. I. R. Wheeldon, E. Campbell and S. Banta, Journal of Molecular Biology, 392, 129 (2009).

Tables and Figures

Table I Summary of guidelines to design an electrochemical biosensor with an enzymatic layer with target design criteria.

Target Design Dimensionless How to optimize? Refer to Figure

Criteria Number

Linear Range Maximize Figure 3

Response Time Determine based on Figure 3

eq (7)

Limit of Detection α Maximize up to α =10 Figure 5 and 6

Sensitivity α Maximize up to α =10 Figure 5 and 6

Table II Kinetic parameter and diffusion coefficients used in simulations

[E] 3.4 µM

-1 kcat 19.17 ± 16.89 s

KA 0.98 ± 1.28 mM

Kia 0.071 ± 0.154 mM

Ks 1.35 ± 1.28 mM

Kis 1.623 ± 0.916 mM

Diffusion Coefficient of Formaldehyde 1.15 x 10-9cm2/s

Diffusion Coefficient of NAD+, NADH 1.15 x 10-8cm2/s n 2 di/dc 80 nA/mM

Figure 1 Schematic showing the experimental parameters (bold outline) that affect the biosensor response and goals (dashed outline) of biosensor design.

Figure 2 Schematic of a biosensor with an enzymatic gel and direct electron transfer at the electrode.

0.30 0.20 0.15 0.25 0.10 0.05

Cp* at x*=0   0.20  0.00 s

0 50 100 150 200 250 0.15 Time(s) s

* at x*=0 at *

p 0.10

 0.05 s 0.00

0 5 10 15 20 25 30 Csbulk *

5 0 4  3

C 2  1  0

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Figure 5 Concentration profile for the product within the enzyme layer when =10.0 for different values of α. Negligible charge transfer resistance; α =10, large charge transfer resistance; α =0 and intermediate values of charge transfer; α =0.1 and α =0.01.

2.5 =1000 2.0 =100 =10 1.5

1.0 =1.0

* at z*=0

C  0.5 =0.1 0.0

0 5 10 15 20 25 Csbulk*

Figure 5 Dimensionless product flux at the surface as a function of dimensionless concentration of substrate for α=0.1, α=1, α=10, α=100.0 and α=1000.0 at =10.0 .

0.10

0.08

0.06 s

0.04

0.02

0.00 0 10 20 30 40 50 60 

Figure 6 Change in dimensionless sensitivity, s, as a function of change in α for =10.0.

Figure 7 Representation of ordered bi-bi equation with substrate inhibition.

300

Experimental 250 Steady State Model

200

150

Current (nA)Current 100

0 0 50 100 150 200 250 300 Gel Thickness (m)

Figure 8 Experimental (points) and predicted steady state current response at different enzyme gel thicknesses for a formaldehyde biosensor.

250

200

150

100

Current(nA)

0 0 20 40 60 80 100 120 140 Formaldehyde Concentration(mM)

Figure 9 Simulated calibration curve for formaldehyde.

Chapter 4 Electrochemical Reduction of Nitrite to Ammonia for Use in Biofuel

Production

Abstract

Biofuel production from renewable resources has recently garnered much interest, since natural photosynthesis routes for biofuel production can suffer from low efficiencies. The use of chemolithoautotrophic organisms coupled with electrochemical reactors, which would regenerate the redox mediator is a possible alternative for bioproduction. This would enable the use of renewable energy without the need for photosynthesis. A major challenge in application of this concept is the production of electrons for transfer to the biological system. We describe an electrochemical reactor for reduction of nitrite to ammonia, which is the natural mediator for electron transfer for the chemolithoautotrophic bacterium, Nitrosomonas europaea. Batch constant current electrolysis experiments were carried out to establish conditions leading to current efficiencies of nitrite reduction to ammonia that approach 100% for nickel and glassy carbon cathodes in media optimal for N. europaea growth. Linear sweep experiments were used to study the effect of the presence of isobutanol, a potential biofuel that could be produced by the cells. A divided flow-by porous electrode cell was also designed as a proof of principle to produce sufficient ammonia in two hours per day to supply ammonia for a continuously operating 5.0 L chemostat containing wild type N. europaea cells.

Introduction

As global energy consumption increases, biofuel production from renewable energy resources will help guarantee domestic energy security. Current methods employed for biofuel production frequently rely on natural photosynthesis, but these photosynthetic approaches suffer from low energy conversion efficiencies. The maximum theoretical biomass production efficiency from photosynthesis is 11.9%, and in real systems is closer to one percent (1). Additionally, difficulty in converting biomass to biofuels that are compatible with existing infrastructure presents a challenge. Clearly, there is a need to develop alternate approaches with improved energy to biomass/biofuel conversion efficiency.

Figure 1 illustrates an alternative approach in which electrical energy is used directly for bioproduction. The system would operate like a microbial fuel cell run in the opposite direction, where electrical energy is used to reduce carbon dioxide into useful chemicals using electrochemical energy. Coupled with the recent advances in sustainable electrical power production via solar energy, wind turbines, geothermal energy, etc, such a system has the potential to direct renewable energy into high energy organic compounds. Additionally the proposed approach would not have any impact on food sources and have low water and land use compared to photosynthetic technologies

Using electrical energy to drive bacteria to produce chemicals has been explored in microbial electrolysis cells in which cells are immobilized on an electrode and this improves electrolysis by acting as a catalyst and lowering the overpotential (2-4). The system proposed here features an electrochemical reactor (for energy capture) and a bioreactor (for bioproduction) that are spatially and temporally decoupled. This

92 decoupling allows for simultaneous optimization of both processes. One of the most important challenges in microbial electrosynthesis and microbial biofuel production from electrical energy is the transfer of electrons from electrode to the biocatalyst (typically microorganisms). Electrons can either be transferred directly or indirectly via use of mediators. In microbial electrosynthesis direct electron transfer has been reported in organisms such as Geobacter sulfurreducens (5, 6) and Shewanella putrefaciens (7) but this capability is not widely distributed across all microorganism. For direct electron transfer to take place, cells usually need to be immobilized on the electrode, which is advantageous in terms of retaining the biocatalyst. However, the immobilization at the electrode can lead to internal and external mass transfer limitations (8) and the system may be difficult to scale-up..

In contrast, scale-up of indirect electron transfer is easier than for direct electron transfer as immobilization is not necessary and mediators such as phenazines (9, 10), flavins (11), sulfur species (12) and H2 can be used for electron transfer. The choice of a mediator is critical for successful indirect electron transfer. When choosing a mediator, one can: 1) Exploit organisms with an inherent ability to naturally use mediators, 2)

Engineer improved mediators for an organism, or 3) Engineer an organism to accept electrons from a convenient mediator. Regardless of the approach utilized, the mediator needs to be chemically stable, have fast oxidation/reduction kinetics at the electrode and the organism while also not interfering with other metabolic processes (13).

Redox mediators that are capable of supporting chemolithoautotrophy (the

+2 +3 - - -2 + - approach outlined in Figure 1) are Fe /Fe , NO2 /NO3 , H2S/SO4 , H2/H and NH3/NO2

(14-16). Thermodynamically the minimum power required for bioproduction is the same

93 for all mediators but the rate of mediator regeneration and efficiency of biofuel production is determined by the specific biocatalyst. We can assume 50% efficiency in the electrochemical cell (assuming 100% current efficiency and 50% efficiency in cell potential due to overpotentials), however the efficiency of CO2 conversion in the bioreactor is still an unknown and will determine the overall efficiency of the process.

Biofuel production using Escherichia coli has been reported in literature (17). However, the carbon source for production in E.coli is glucose as opposed to CO2 proposed in this work.

In this study an ammonia-oxidizing chemolithoautotrophic organism,

Nitrosomonas europaea, was chosen as the biocatalyst and the ammonia/nitrite redox couple was chosen as the electron mediator to produce a biofuel such as isobutanol. As depicted in Figure 1, the electrochemical reactor is coupled with a N. europaea bioreactor to reduce the nitrite produced in the bioreactor back to ammonia (Rxn 1).

0 E =-0.165 V vs. SHE Rxn 1

Electrochemical reduction of nitrates and nitrites has been previously demonstrated for low-level radioactive waste treatment and drinking water treatment and production of industrial chemicals such as hydroxylamine (18, 19). In water treatment applications the common goal is to convert nitrates and nitrites to N2 as shown in reactions 2 and 3 below

0 E = 0.010 V vs. SHE Rxn 2

0 E = 0.406 V vs. SHE Rxn 3

A main challenge in this application is the selectivity of the reduction process to

N2. Since nitrogen can have oxidation numbers ranging from +5 to -3 many possible products, such as NH3, NH4OH, NO, NO2, are possible (20). The selectivity for the desired product depends on the cathode, electrolyte composition, and the operating conditions. To determine the reaction mechanism of nitrate reduction, studies in acidic solutions were conducted (21). It was determined that, in the presence of nitrous acid, the reaction proceeded efficiently as a result of an autocatalytic reduction mechanism (21).

For low-level radioactive waste treatment applications, the reduction takes place in basic solutions (22-26). Different cathode materials such as copper, nickel, platinum, glassy carbon, tin and bismuth were tested as well as bimetallic electrodes to optimize catalytic activity and N2 selectivity (22-26). Even though the exact mechanism has not been determined, it has been observed that ammonia can be the main product of nitrate/nitrite reduction under various operating conditions (26, 27). In drinking water treatment applications the reduction process is carried out in weakly alkaline solutions containing high concentrations of regeneration agent (sodium chloride, sodium bicarbonate, etc.) necessary for ion exchange processes (19, 27-31). Studies were conducted to understand the effect of addition of the regeneration agent, and cathodes that increase selectivity were identified (27, 28).

The main objective of this chapter is to design a proof of principle electrochemical reactor to reduce nitrite to ammonia with high current efficiencies in electrolytes optimal for N. europaea growth. The requirements for the development of an electrochemical reactor for recycling the redox mediator for microbial bioproduction may be significantly different from requirements for an electrochemical reactor for

95 nitrate/nitrite reduction in water treatment applications as identified and outlined in

Figure 2 These requirements include tolerance to biofuels produced (in our case isobutanol), stability in long term operation, minimum power requirements and tolerance to media necessary for the bacteria growth.

Figure 2 also presents the experimental parameters that affect these criteria, including catholyte and anolyte composition (pH, mediator concentration, divided/undivided cell configuration etc.) and the choice of cathode and anode. As can be seen in aforementioned studies, the reduction is highly sensitive to electrolyte composition and especially pH. Therefore current efficiencies and selectivity need to be measured in the media solution optimal for biocatalyst growth (in our case N. europaea). This media contains trace amounts of metals and is maintained at a pH between 7.0 and 8.0. A few groups studied nitrate reduction in buffered and unbuffered catholytes at different initial pHs with and without pH control. High nitrate destruction efficiencies were observed at all pHs (29). Without pH control it was observed that the pH increased quickly to ~12 due to OH- production as shown in Rxns 2 and 3 (30). At low pH the nitrate reduction rate was slower (30). The choice of mediator concentration in the electrolyte is also an important parameter for the performance of the electrochemical cell as well as the bioreactor. Linear sweep and flow cell experiments were used to describe the effect of the mediator concentration on flow cell design. The effect of the target biofuel (isobutanol) was investigated using linear sweep voltammetry. In this study an undivided cell was used for batch constant current electrolysis studies since only a small fraction of the initial nitrite concentration is reduced to ammonia. However in the flow-by reactor, high concentrations of ammonia were produced, therefore a cation exchange membrane was

96 employed to prevent ammonia oxidation back to nitrite. The anolyte and anode are also important for the overall system performance, but this is not a focus of the present study.

Nickel, copper and glassy carbon were chosen as potential cathodes in this study based on results described in the literature. Copper was found to be highly active for nitrate reduction both in basic and weakly alkaline solutions, whereas nickel seemed to be more selective for nitrite reduction (22, 28). Glassy carbon was investigated as a cathode candidate because of its inertness in biological systems. Performance and current efficiencies of these materials were studied in batch electrolysis experiments and in flow- by porous electrodes. Different anode materials such as stainless steel cloth and mesh, nickel plate and dimensionally stable anodes were also evaluated for their effect on cell potential and long term performance in the flow-by electrochemical reactor.

The choice of cathode and anode plays an important role in determining power requirements. Assuming oxygen evolution at the anode, the overall cell reaction for the electrochemical reactor can be written as below:

Ecell= -0.497 V Rxn 4

Using the Nernst equation, the thermodynamic cell potential for Rxn 4 was determined to be -0.497 V at a pH of 7.0. This potential combined with overpotentials measured at the cathode and anode dictate the power requirements of the electrochemical reactor which is predicted to be the main cost of operation for the overall system.

Experimental

Chemicals and Reagents

Sodium phosphate dibasic, sodium phosphate monobasic, sodium nitrite, sodium nitrate, ammonium sulfate and 2 methyl-1-propanol (heretofore referred to as isobutanol) were purchased from Sigma-Aldrich. Sodium chloride (A.C.S reagent grade) was purchased from EMD Chemicals Inc. and sodium hydroxide 50% w/w and hydrochloric acid were purchased from Fisher Scientific. Growth media used in constant current electrolysis experiments contained the following chemicals (mg/L): MgSO4·7H2O (200),

CaCl2·2H2O (20), K2HPO4 (87), Na2MoO4·2H2O (0.01), MnSO4·H2O (0.017),

CoCl2·7H20 (0.0004), CuCl2·2H2O (0.17), ZnSO4·7H2O (0.01), and chelated iron (1) (32,

33).

Linear Sweep Voltammetry

Linear sweep voltammetry experiments were conducted using a µAutolab

Potentiostat (Ecochemie, Netherlands). Glassy carbon rotating disk electrodes (3.0 mm diameter) were produced in-house as described in Gallaway et al. (34). After each experiment they were polished with alumina slurry to a mirror-like finish and rinsed with deionized water. Nickel rotating disk electrodes were produced by freshly plating nickel using Watt’s solution on latinum otating disk elect odes (4.0 mm diameter). A

Ag/AgCl reference electrode and platinum wire counter electrode were used in these experiments. Phosphate buffer (100 mM) containing various concentrations of nitrite, nitrate and isobutanol were prepared fresh before each experiment and the exact concentrations used are listed in the captions for each figure. Each experiment was

98 performed in duplicate with a sweep rate of 5.0 mV/s to scan between 0.2 and -1.6 V vs.

Ag/AgCl

Constant Current Electrolysis Experiments

An undivided, three-electrode cell setup was used for constant current electrolysis experiments. Copper (1.5 mm) and nickel (2.0 mm) wire were both purchased from Alfa

Aesar (Puratonic 99.999 %). Reticulated vitreous carbon (RVC; 10 pores per inch (ppi)) was purchased from Erg Materials and Aerospace (California, USA). All electrodes used as working electrode had a surface area of 13 cm2. A Ag/AgCl was used as the reference electrode and 52 mesh woven platinum mesh purchased from Alfa Aesar was used as the counter electrode. To obtain sufficient ammonia concentrations to allow for quantitative analysis of current efficiency, constant current was applied for 1 hr using µAutolab

Potentiostat, Princeton Applied Research Potentiostat/Galvanostat, and Autolab

Biopotentiostat instruments depending on the applied current range. All experiments were repeated at least three times. Samples were collected and filtered using 0.22 µm filters for ammonia measurements. Gas-sensing ammonia electrodes (Thermo Scientific) were used to determine ammonia concentrations (35). All ammonia measurements were performed in duplicate.

Flow Cell Experiments

The schematic of the home-made flow-by electrolysis cell is shown in Figure 3.

Glassy carbon and nickel porous electrodes were used as the cathode and nickel plate

(Alfa Aesar), stainless steel cloth (mesh size 325, Small Parts) and dimensionally stable electrodes (mesh ID 11873, De Nora) were used as anodes. The anode and cathode were

99 separated by a cation exchange membrane purchased from Snowpure (California, USA).

80 ppi glassy carbon electrodes with an interfacial area of 49 cm-1 were obtained from

Erg Materials and Aerospace (California, USA) with the following dimensions (0.5 cm x

10 cm x 5.0 cm). Contact to these electrodes was established using toray paper (Fuel Cell

Store) and conductive carbon cement adhesive (SPI Supplies, West Chester, PA). Porous nickel electrodes (90 ppi, 420 g/m2, 580 micron pore size with 1.7 mm thickness) were supplied by INCOSP (Canada). Power supplies (Agilent Technologies and GW Instek

Group) were used to apply the required currents. Current densities reported in this chapter were set based on the cross-sectional area (50 cm2) of the porous electrodes. Current densities based on estimate of real area (volume times interfacial area) of the electrode are also included in parenthesis. A peristaltic pump (Masterflex, Cole Palmer) was used to control the flow of catholyte and anolyte at a rate of 0.8 ml/s. The composition of the catholyte and anolyte used in each experiment is indicated below the figure. Each experiment was conducted once and the ammonia measurements were done in duplicate.

Results and Discussion

Linear Sweep Voltammetry

Linear sweep voltammetry experiments were used to characterize the reduction behavior of nitrite and nitrate, including the effect of nitrite concentration as well as potential fouling by isobutanol. Since nitrite is the primary product of ammonia oxidation by N. europaea (36) , electrochemical reduction of nitrate is not necessary for microbial bioproduction . However electrochemical experiments with nitrate solutions were

100 performed for comparison and to allow for reconciliation of the findings in this chapter with existing literature. In Figure 4, linear sweeps of 1.0 M sodium nitrite in 100 mM phosphate buffer at a pH of 7.0 are shown for glassy carbon and nickel rotating disk electrodes. A mass transfer limited plateau starting from ~ -0.6 V vs. Ag/AgCl is observed for nickel. A plateau was not seen for glassy carbon, possibly due to the shift in overpotential to more negative potentials, leading to an overlap with hydrogen evolution.

The limiting current was observed to increase linearly with the square root of rotation speed. Given the Levich equation,

(1) the plot has a slope of 1.5 mAs1/2/cm2rad1/2 and goes through the origin as shown in the inset to this figure. If D, the diffusion coefficient, is assumed to be 2.0 x10-5 cm2/s (37),

ν, the kinematic viscosity, is assumed to be 1.0 x10-2 cm2/s and n = 6, the calculated concentration difference c from the Levich equation is 2.6x10-3 M.

In order to determine whether the plateau observed for nickel experiments is due to the mass-transfer rate of nitrite to the electrode, linear sweeps in solutions with different concentrations of nitrite were performed as shown in Figure 5. To keep the conductivity and cation concentration constant (1.1 M Na+), different amounts of sodium chloride were added to each solution used in this experiment. Thus the IR drop differences that might result due to different electrolyte conductivities were minimized.

The limiting current did not change with increasing concentration of nitrite above 20 mM which is consistent with literature (37, 38) and implies that the limiting current is not due to the mass-transfer rate of nitrite to the electrode. Previous groups that studied the

101 reduction mechanism of nitrite suggested that the first step of the reduction was formation of NO adlayers on the electrode surface followed by formation of NH3, NH2O or other products (37). The cause of the mass-transfer limitation has not yet been determined.

In Figure 6 polarization curves obtained with solutions of 100 mM phosphate buffer with 1.0 M sodium chloride, 1.0 M sodium nitrate and 1.0 M sodium nitrite are shown for a nickel electrode. The reduction current for nitrite is higher than the reduction current for nitrate on both cathode materials (glassy carbon polarization curves are not shown). This was observed previously by other authors and suggests that nitrite reduction is kinetically faster than nitrate reduction (28).

Figure 7 shows the effect of 50 mg/L isobutanol addition on nitrite reduction, when isobutanol is added to the nitrite solutions, no significant change in the current response was observed. The concentration of isobutanol used in this study is based on the half maximal inhibitory concentration (50 mg/L) of isobutanol for N. europaea (data not shown) because this is anticipated to be the maximum concentration of isobutanol concentration in the recycled stream that will be electrochemically processed (c.f., figure

1).

Based on these results we can conclude that a mass transfer limited plateau is observed for nickel. This plateau is not due to nitrite concentration but the cause is not yet determined. The data for nitrate reduction suggests that compared to nitrite reduction this process is kinetically slower and shifted towards the H2 evolution region. The studies

102 with the biofuel suggested that addition of isobutanol did not affect nitrite reduction behavior.

Constant Current Electrolysis

Constant current experiments were performed to determine current efficiencies of different cathode materials for nitrite and nitrate reduction in 100 mM phosphate buffer at a pH of 7.0 and for nitrite reduction in media solution that includes trace metals. Current efficiency was defined as the ratio of charge spent for nitrite reduction to ammonia to overall charge passing through the system as shown in equation 2 below:

Current Efficiency % (2)

where is the charge for nitrite reduction to ammonia, is the total amount of

charge passed through the system, is the concentration of ammonia measured at the end of each experiment, V is the volume of the electrolysis solution, n is the number of electrons, which is six for reduction of nitrite to ammonia as shown in reaction 1, F is

a aday’s constant, t is time and I is current applied. Figure 8 shows the current efficiencies for nickel, copper and glassy carbon in phosphate buffer solution containing

1.0 M sodium nitrite. This figure suggests that current efficiencies between 80 and 100 % are achieved using all three electrodes. An undivided cell was used for these experiments, such high current efficiencies obtained suggest that oxidation of ammonia at the anode was not an issue in this set-up with low conversion of nitrite to ammonia. Current efficiency data for nitrite reduction in media solution and nitrate reduction in buffer solutions are summarized in Table 1. When calculating the current efficiency of nitrate

103 reduction to ammonia, equation 2 is used with number of electrons increased from 6 to 8.

As seen in this table, the copper cathode was only used in nitrite-reduction experiments.

Formation of a dark surface film on copper was observed, and therefore this cathode material was not further investigated. Previously such layers on copper were reported by several groups and were claimed to be due to formation of CuH and CuH2 (23). Even though current densities used in these experiments might seem small when high surface area porous electrodes are used as described in the next section they lead to small reactors that are comparable to commercial water electrolyzers (39).

It should also be noted that the current efficiencies for nitrate reduction were lower than current efficiencies for nitrite reduction. A shift to more negative potentials, where H2 evolution occurs, was observed in the case of nitrate reduction (data not shown) which would explain the decrease in current efficiency. This shift to more negative potentials was also noted in linear sweep experiments of previous section. Current efficiencies of nitrite reduction in media solution were found to be slightly lower than current efficiencies in buffer solution, presumably due to ammonia volatilization

(reducing the ammonia measurement). This is thought to be due to the low buffering capacity of the media solution as compared to the buffer solution. Since there is minimal buffering in the media, reaction 1 leads to an accumulation of OH- resulting in an increase in pH to ~12 in the growth media whereas in the buffer solution, similar experiments lead to a final pH of ~9. At these high pH values the ammonia–ammonium- ion equilibrium is shifted toward ammonia gas, which can escape the system during experiments. In order to confirm this assumption experiments with the media were conducted in the divided flow-cell. pH of the catholyte was kept between 8.4 - 8.6 by use

104 of a pH controller and addition of 5.0 M hydrochloric acid. Current efficiency of 100 %

± 9.0 was obtained after a single pass through the flow-cell as reported elsewhere (40).

Experiments to determine the overpotential required by different electrode materials were also conducted to screen which cathode would be most suitable for minimum power requirements. The overpotential required for nitrite reduction using glassy carbon, nickel and copper cathodes were determined using constant current experiments in an undivided cell. It was determined that the lowest overpotentials were required when nickel cathodes were used and the highest overpotentials were obtained at glassy carbon electrodes (manuscript in preparation). Figure 9 shows the potential of a nickel rotating disk electrode at different current densities. The overpotential at the cathode can be calculated by subtracting the thermodynamic potential from the operating potential. For example at an applied current density of 5.0 mA/cm2 the operating potential is -0.56 V vs. Ag/AgCl and the thermodynamic half cell potential is 0.120 V vs.

Ag/AgCl (0.317 V vs. SHE). Therefore the cathode overpotential is (-0.56 - 0.120) = -

0.68 V, suggesting that cathode optimization studies are warranted.

At the anode, oxygen evolution takes place. This reaction has been optimized for various industrial processes such as water electrolysis in alkaline solutions and metal electrowinning in acidic electrolytes (39). Overpotentials as low as 0.400 V in sulfuric acid solutions were reported with composite electrodes (41). Using these overpotentials

(680 mV for the cathode and 400 mV for the anode) and the thermodynamic half-cell potentials at the appropriate pH, one can estimate that a cell potential less than 2.0 V may be feasible in a practical system performing nitrite reduction to ammonia. However, as

105 the overall system is developed, improvements in the cathode may indeed lead to further reductions in potential.

Based on these constant current experiments glassy carbon and nickel cathodes prove to be viable candidates for reduction of nitrite to ammonia in N. europaea media in the flow-by reactor. A cell potential of 2.0 V is predicted for the flow-by cell based on measured cathode overpotentials.

Flow Cell

Based on linear sweep and constant current electrolysis data and the requirements to feed a 5.0 L bioreactor, a divided flow cell shown in Figure 3 was designed. Constant current electrolysis results suggest that nitrite reduction to ammonia can be performed with high current efficiencies. The divided flow-by reactor was used to determine whether such high current efficiencies could be maintained at high conversions of initial nitrite concentrations. Additionally the flow system was used to identify any issues that will be important in long term operation of an electrochemical reactor used for recycling redox mediator for microbial biofuel production. A flow-by system was preferred over a flow-through system since ohmic potential drops can be minimized in these systems (42).

Porous glassy carbon and nickel electrodes were used as cathode materials in this flow cell. Previously the advantages of using porous electrodes as opposed to plate electrodes for nitrate reduction in low-level radioactive waste application was demonstrated (18,

43). High current efficiencies were reported with both cathode materials. The current efficiency of single pass nitrite reduction at different superficial current densities varying from 4.4 to 49 mA/cm2 using a glassy carbon electrode was observed to be between 90-

106

100 %. Such current densities may be too low for economical scale-up, but the media

(i.e., the electrolyte) may also be re-engineered in the future to allow for high current densities, while also sustaining the bacteria in the bioreactor. This optimization is necessarily coupled with microbiological characterization. Since the flow-by cell was operated to have ~10 % conversion of nitrite to ammonia in a single pass, the catholyte was recycled multiple times as shown in Figure 3 to achieve desired conversions. Figure

10 shows the current efficiency as a function of nitrite conversion to ammonia for glassy carbon and nickel electrodes at different initial nitrite concentrations. Current efficiencies of 80-100 % are observed for both materials at high concentrations (100 mM sodium nitrite), which is consistent with constant current electrolysis experiments.

The concentration of nitrite in the catholyte is an important experimental parameter that affects the performance of the flow cell and the overall performance of the system.

As observed in this plot, at high conversions (corresponding to lower concentrations of nitrite), current efficiency decreases. This is assumed to be due to mass transfer limitations that occur at low concentrations of nitrite.

The performance of the anode and cathode in long term operation as well as changes in catholyte composition need to be determined. We have verified that the cathode performance does not deteriorate when exposed to media effluent from the bioreactor

(40), an important milestone for establishing an integrated system. Different materials such as stainless steel, nickel plate and dimensionally stable anodes were tested for suitability as anode materials. The choice of anode was not observed to affect the current efficiency however significant changes in cell potentials were observed. Furthermore

107 both nickel plate and stainless steel anodes showed signs of corrosion after long term usage.

As shown in figure 3 the flow-by system was designed to have a cation selective membrane between the anode and the cathode to prevent oxidation of nitrite to nitrate or ammonia to nitrite at the anode during long term experiments. In this configuration the choice of anolyte was observed to affect the cell potential and exit pH of the catholyte. In the previous section based on overpotentials measured at the cathode and overpotentials presented in the literature for oxygen evolution an overall cell potential of 2.0 V was predicted. Experiments were conducted in a batch divided cell and a cell potential of 2.5

V was achieved at 5.0 mA/cm2 without an optimized anode. However, in the flow-by reactor the lowest cell potential achieved was 4.0 V. We believe the difference is largely due to IR losses and can be minimized with improved design of the flow cell and the media (which as discussed above can be further optimized). In the divided system the choice of anode is also very important, when phosphate buffer at a pH of 7.0 was used as the anolyte, the concentration of H+ (10-7 M) was not sufficient to compensate for the 7.0

- + -2 OH per NH3 produced at the cathode, because it is believed that Na (10 M) is the primary charge carrier through the cation exchange membrane. Thus significant pH changes in the catholyte effluent were observed. pH changes were less significant at short times when an acid electrolyte was used. This is illustrated in Figure 11 where changes in catholyte pH for different anolyte solutions varying from phosphate buffer at pH 7.0 to 3.0 M sulfuric acid are shown.

For long term, operation we recognize that the system may require an acid source to maintain the proper pH. Ammonia oxidation by N. europaea nominally produces 2.0

108 moles of H+ per mole of ammonia converted to nitrite; however, when carbon fixation and biomass production requirements are considered (using a yield value of 0.14 mg biomass/mg ammonium as N oxidized (44) , this value decreases to 1.8 moles H+ per mole of ammonia oxidized (44). Based on Rxn 4, 2.0 moles of OH- are produced for every mole of ammonia produced. The imbalance between the two processes would result in acid addition to the overall process in long term operation, but the precise amount of protons must be determined with the genetically modified organism actively producing isobutanol in the bioreactor. As additional process data are available, acid consumption will be an important factor for establishing process feasibility. Preliminary studies suggest however, that electrical power requirements for the electrochemical reaction may be the primary economic consideration. Of course, the overall promise of such a microbial biofuel production depends on many factors, especially the biochemical efficiency of the bacteria to utilize electrons for biofuel production (a certain fraction of these electrons are also required for cell growth and maintenance).

Flow cell experiments presented here suggest nitrite reduction to ammonia in N. europaea media is highly efficient when nickel and glassy carbon electrodes are used.

Some of the issues that might be very important in long term operation were identified.

Optimization of the designed cell is warranted to reduce cell potential to predicted potentials of 2.0 V.

Conclusions

In this chapter, development of a proof of principle flow-by electrochemical reactor for regeneration of redox mediator of chemolithoautotrophic bacteria was

109 described. Requirements for such a reactor were outlined and the parameters effecting these requirements were identified. Based on previous nitrate/nitrite reduction literature three cathode materials (glassy carbon, nickel and copper) were tested. All three cathode materials showed high current efficiencies for nitrite reduction to ammonia in buffered solution. However, the formation of a surface layer was observed on the copper electrode, and therefore it was not investigated as extensively. Glassy carbon and nickel cathodes also showed high current efficiencies in the media solution. The addition of the biofuel of interest, isobutanol, to the electrolyte did not affect the linear sweep experiments. A divided flow-by system consisting of a porous cathode and a planar anode separated by a cation exchange membrane was designed, and scale-up to a flow system was demonstrated. Anolyte and choice of anode were found to affect the cell potential and catholyte pH. Various anode materials were tested such as stainless steel mesh and cloth, nickel plate, porous glassy carbon and dimensionally stable anodes. All these materials, except dimensionally stable anodes, showed signs of deterioration after long-term operation.

Acknowledgements

This research was supported by the DOE ARPA-E Electrofuels program (DE-0000060).

110

References

1. D. Walker, Journal of Applied Phycology, 21, 509 (2009).

2. K. Rabaey and R. A. Rozendal, Nat Rev Micro, 8, 706 (2010).

3. K. P. Nevin, T. L. Woodard, A. E. Franks, Z. M. Summers and D. R. Lovley, MBio, 1 (2010).

4. P. Lindberg, Park, S., Melis, A., Metabolic Engineering, 12, 70 (2010).

5. D. R. Bond and D. R. Lovley, Appl. Environ. Microbiol., 69, 1548 (2003).

6. F. Caccavo, Jr, D. J. Lonergan, D. R. Lovley, M. Davis, J. F. Stolz and M. J. McInerney, Appl. Environ. Microbiol., 60, 3752 (1994).

7. H. J. Kim, H. S. Park, M. S. Hyun, I. S. Chang, M. Kim and B. H. Kim, Enzyme and Microbial Technology, 30, 145 (2002).

8. K. Rabaey, P. Girguis and L. K. Nielsen, Current Opinion in Biotechnology, 22, 371 (2011).

9. K. Rabaey, N. Boon, M. Höfte and W. Verstraete, Environmental Science & Technology, 39, 3401 (2005).

10. K. Rabaey, N. Boon, S. D. Siciliano, M. Verhaege and W. Verstraete, Appl. Environ. Microbiol., 70, 5373 (2004).

11. E. Marsili, D. B. Baron, I. D. Shikhare, D. Coursolle, J. A. Gralnick and D. R. Bond, Proceedings of the National Academy of Sciences, 105, 3968 (2008).

12. K. L. Straub and B. Schink, Appl. Environ. Microbiol., 70, 5744 (2004).

13. M. H. Osman, A. A. Shah and F. C. Walsh, Biosensors and Bioelectronics, 26, 953 (2010).

14. H. D. Peck, Jr., Annu Rev Microbiol, 22, 489 (1968).

15. D. P. Kelly, Archives of Microbiology, 171, 219 (1999).

16. I. Suzuki, Annu Rev Microbiol, 28, 85 (1974).

111

17. S. Atsumi, T. Hanai and J. C. Liao, Nature, 451, 86 (2008).

18. K. Jha and J. W. Weidner, Journal of Applied Electrochemistry, 29, 1305 (1999).

19. Z. Mácová and K. Bouzek, Journal of Applied Electrochemistry, 35, 1203 (2005).

20. M. T. de Groot and M. T. M. Koper, Journal of Electroanalytical Chemistry, 562, 81 (2004).

21. G. Horányi and E. M. Rizmayer, Journal of Electroanalytical Chemistry and Interfacial Electrochemistry, 140, 347 (1982).

22. L. Durivault, O. Brylev, D. Reyter, M. Sarrazin, D. Bélanger and L. Roué, Journal of Alloys and Compounds, 432, 323 (2007).

23. Horanyi, G., Rizmayer and E. M., Electrocatalytic reduction of NO[2] and NO[3]ions at a platinized platinum electrode in alkaline medium, Elsevier, Amsterdam, PAYS-BAS (1985).

24. H.-l. Li, D. H. Robertson, J. Q. Chambers and D. T. Hobbs, Journal of The Electrochemical Society, 135, 1154 (1988).

25. S. Cattarin, Journal of Applied Electrochemistry, 22, 1077 (1992).

26. N. Chebotareva and T. Nyokong, Journal of Applied Electrochemistry, 27, 975 (1997).

27. Z. Mácová, K. Bouzek and J. Še ák, Journal of Applied Electrochemistry, 37, 557 (2007).

28. K. Bouzek, M. Paidar, A. Sadílková and H. Bergmann, Journal of Applied Electrochemistry, 31, 1185 (2001).

29. M. R. Connor and J. C. Liao, Curr Opin Biotechnol, 20, 307 (2009).

30. J. W. Peel, K. J. Reddy, B. P. Sullivan and J. M. Bowen, Water Research, 37, 2512 (2003).

31. M. Paida , . Rouša and K. Bouzek, Journal of Applied Electrochemistry, 29, 611 (1999).

32. K. Chandran and N. G. Love, Appl. Environ. Microbiol., AEM.01940 (2008).

112

33. W. O. Khunjar, S. A. Mackintosh, J. Skotnicka-Pitak, S. Baik, D. S. Aga and N. G. Love, Environmental Science & Technology, 45, 3605 (2011).

34. J. Gallaway, I. Wheeldon, R. Rincon, P. Atanassov, S. Banta and S. C. Barton, Biosensors and Bioelectronics, 23, 1229 (2008).

35. A. W. W. A. American Public Health Association, Water Pollution Control Federation, (APHA/AWWA/WPCF), Standard Methods for the Examination of Water and Wastewater, in, Washington D.C. (1992).

36. L. Y. Stein and D. J. Arp, Appl Environ Microbiol, 64, 4098 (1998).

37. M. A. Hasnat, M. A. Rashed, M. S. Alam, M. M. Rahman, M. A. Islam, S. Hossain and N. Ahmed, Catalysis Communications, 11, 1085 (2010).

38. A. G. Muñoz and J. W. Schultze, Electrochimica Acta, 49, 293 (2004).

39. H. Wendt, H. Vogt, G. Kreysa, D. M. Kolb, G. E. Engelmann, J. C. Ziegler, H. Goldacker, K. Jüttner, U. Galla, H. Schmieder and E. Steckhan, in Ullmann's Encyclopedia of Industrial Chemistry, Wiley-VCH Verlag GmbH & Co. KGaA (2000).

40. A. Sahin, Khunjar, W.O., West, A.C., Chandran, K., Banta, S. , Indirect “ lect o”lithoautot o hy – Bypassing Photosynthesis for Bioproduction, in (2011).

41. S. Schmachtel, M. Toiminen, K. Kontturi, O. Forsén and M. Barker, Journal of Applied Electrochemistry, 39, 1835 (2009).

42. T. Risch and J. Newman, Journal of The Electrochemical Society, 131, 2551 (1984).

43. M. Paidar, K. Bouzek and H. Bergmann, Chemical Engineering Journal, 85, 99 (2002).

44. K. Chandran and B. F. Smets, Water Research, 35, 3153 (2001).

113

Tables and Figures

Table 1 Current efficiencies after 1hr electrolysis experiments in an undivided cell with nickel, glassy carbon and copper cathodes in different solutions at current densities 1.0-

20 mA/cm2.

Cathode Current Nitrite Nitrate Media

Density 100 mM 100 mM Media and1.0

(mA/cm2) Phosphate Phosphate M Sodium

Buffer pH 7.0 Buffer pH 7.0 Nitrite

and 1.0 M and 1.0 M

Sodium Nitrite Sodium

Nitrate

Nickel 1 84 ± 5.8

5 96 ± 18 59 ± 3.4 87 ± 1.5

10 99 ± 5.7 63 ± 2.1 76 ± 4.8

20 100 ± 4.4 62 ± 2.0 77 ± 1.8

Glassy Carbon 5 90 ± 3.8 68± 13 78 ± 1.7

10 86 ± 0.6 60 ± 12 83 ± 6.9

Copper 1 100 ± 24

5 95 ± 10.5

10 86 ± 2.2

20 75 ± 6.1

114

115

116

Figure 3 Schematic of the divided flow-by electrolysis cell consisting of a porous cathode and a planar anode separated by a cation exchange membrane.

117

Figure 4 Panel A and B show polarization curves obtained on glassy carbon and nickel rotating disk electrodes in 1 M sodium nitrite and 100 mM phosphate buffer at a pH of

7.0, respectively, at different rotation speeds and at a scan rate of 5.0 mV/s. The inset shows the limiting current as a function of square root of rotation speed for nickel rotating disk electrode.

118

Figure 5 Polarization curve of a nickel rotating disk electrode in 100 mM phosphate buffer at a pH of 7.0 with 0, 5.0, 20, 100, 1000 mM sodium nitrite and required amount of sodium chloride to achieve 1.1 sodium ion concentration. Inset shows the current density as a function of nitrite concentration at -0.8 V vs. Ag/AgCl.

119

120

Figure 7 Current as a function of potential for different rotation speeds for nitrite reduction on nickel (sweep rate was 5.0 mV s-1) in 100 mM phosphate buffer at a pH of

7.0 and 1 M sodium nitrite and 100 mM phosphate buffer pH 7 .0 and 1 M sodium nitrite and 50 mg/L isobutanol at different rotation speeds .

121

122

Figure 9 Constant current experiments in 100 mM phosphate buffer at a pH of 7.0 and

2 2 2 1.0 M sodium nitrite on a nickel RDE at 1 mA/cm , 5 mA/cm and 10 mA/cm .

123

124

Figure 11 Change in the catholyte pH for different anolytes in the flow-by (divided cell):

50 mM phosphate buffer at a pH of 7.0 and 50 mM sodium nitrite (filled circles), 0.5 M sulfuric acid (empty circles), 2.0 M sulfuric acid (triangles) and 3.0 M sulfuric acid

(rectangles) operating at 49 mA/cm2 (2 mA/cm2 based on estimate of real area).

125

Chapter 5 Biomass Production from Electricity Using Ammonia as an Electron

Carrier in a Reverse Microbial Fuel Cell1

Abstract

The storage of renewable electrical energy within chemical bonds of biofuels and other chemicals is a route to decreasing petroleum usage. A critical challenge is the efficient transfer of electrons into a biological host that can convert this energy into high energy organic compounds. In this chapter, we describe an approach whereby biomass is grown using energy obtained from a soluble mediator that is regenerated electrochemically. The net result is a reverse microbial fuel cell (rMFC) that fixes CO2 into biomass using electrical energy. We selected ammonia as a low cost, abundant, safe, and soluble redox mediator that facilitated energy transfer to biomass. Nitrosomonas europaea, a chemolithoautotroph, was used as the biocatalyst due to its inherent capability to utilize ammonia as its sole energy source for growth. An electrochemical reactor was designed for the regeneration of ammonia from nitrite, and current efficiencies of 100% were achieved. Calculations indicated that overall bioproduction efficiency could approach 2.7 ± 0.2% under optimal electrolysis conditions. The application of chemolithoautotrophy for industrial bioproduction has been largely unexplored, and results suggest that this and related rMFC platforms may enable biofuel and related biochemical production.

1 This work is done in collaboration with Wendell O Khunjar.

126

Introduction

The production of biofuels and related chemicals from renewable resources has emerged as one of the great engineering challenges of the 21st century (1-4). The exploitation of photosynthetic organisms has been the predominant paradigm used for producing biomass, biofuels, and related chemicals. However, this approach is intrinsically limited by the low energy capture and transfer efficiency of photosynthesis.

Zhu et al. (2008) have suggested that the theoretical maximum efficiency of solar energy conversion to biomass for C3 and C4 plants is 4.6% and 6.0% respectively at an

0 atmospheric carbon dioxide (CO2) concentration of 380 ppmv and 30 C (5), yet the highest reported conversion efficiencies for C3 and C4 plants are 2.4 and 3.7% respectively (6), with a practical average conversion efficiency under 1% (7). These progressively lower conversion efficiencies in applied systems are the cumulative result of energy losses during chlorophyll-based light absorption, energy transfer during carbohydrate synthesis, and respiration (both photorespiration and mitochondrial/bacterial respiration) (5, 8). Further complicating the use of photosynthesis for bioproduction is the difficulty in processing lignocellulosic biomass into a biofuel product that is compatible with existing infrastructure.

Circumventing these challenges will require the development of new technologies that simultaneously improve energy capture and transfer to biosynthetic pathways optimized for production of useful compounds (9-14). One way to address this challenge is through the development of reverse microbial fuel cells (rMFCs). In a standard microbial fuel cell, organisms oxidize organic fuels and transfer electrons into an electrochemical system so that fuels are converted to electrical energy. In a rMFC, this

127 process is reversed so that electrical energy is used by cells to drive carbon dioxide fixation to high energy organics. The critical challenge for this approach is that energy must be efficiently transferred from an electrode into a biological host which is capable of using this energy for biosynthesis. rMFC platforms could have a significant impact in the biofuel and biochemical arena as they would be capable of using electricity generated from all renewable sources including wind, geothermal, hydroelectric, nuclear, and solar.

They could be used in a variety of global locations, and they could be used for long term storage of excess electrochemical energy.

One approach to creating rMFCs is to use direct electron transfer from an electrode to the cells. This has been termed microbial electrosynthesis (15). While this energy transfer can be accomplished directly, where electroactive cells in a biofilm utilize electrons from an electrode for anabolism (15, 16), diffusion issues and the requirement for 2-D biofilms make direct microbial electrosynthesis a challenging proposition.

An alternative approach to creating rMFCs is to use soluble electron mediators that facilitate spatial and temporal decoupling of energy capture and bioproduction. This can allow both processes to be operated and optimized separately. And, the use of a mediator enables the use of planktonic cells in the bioreactor and these 3-D systems will be far more amenable to scale-up of the process. In the mediated approach, electrons are first transferred from an electrode to a soluble mediator and then the mediator would be oxidized by the cell. Inorganic compounds that are linked with chemoautotrophy and can be electrolytically regenerated, like hydrogen (H2) as well as those involved with the

- nitrogen, iron and sulfur biogeochemical cycles (i.e. ammonia (NH3), nitrite (NO2 ), iron

2+ (Fe ), hydrogen sulfide (H2S)) are attractive options for use in this platform since they

128 can facilitate the construction of multi-carbon organics from carbon dioxide using naturally occurring carbon fixation pathways. These inorganic compounds naturally yield sufficient energy to support biomass growth and can be reduced via electrolysis

(Table A1).

The rMFC has recently been demonstrated using electrochemically produced formate coupled with genetically modified Ralstonia eutropha cells that were able to produce isobutanol (17). In this version of the rMFC, carbon dioxide was fixed both at the electrode into formate and by the cells, and the net result of the rMFC was biofuel production from electrical energy and CO2.

In this chapter, we address the feasibility of using an alternative electron transfer mediator and chemolithoautotrophy for primary production. We demonstrate sustained biomass production in a rMFC that has two components: 1) an electrochemical reactor that produces ammonia from nitrite using electrical energy, and 2) a biological reactor that containing a naturally chemolithoautotrophic, ammonia-oxidizing bacterium,

Nitrosomonas europaea (Figure 1). This organism was selected as it is a well-studied autotrophic ammonia oxidizing bacterium whose genome has been sequenced (18). We report stable long term operation of the rMFC and bioreactor, which facilitated fixation of

CO2 via the Calvin-Benson-Bassham (CBB) cycle using energy derived solely from ammonia. These results suggest that this approach can be expanded to produce biofuels and other chemicals via genetic modification of the N. europaea cells in the bioreactor without the need for photosynthesis.

129

Experimental

Strain and growth conditions

Nitrosomonas europaea ATCC 19718 was cultured in a chemostat (2 L; dilution rate = 0.2 day-1) utilizing modified autotrophic medium (designated SynM) comprising

(mg/L): MgSO4·7H2O (200), CaCl2·2H2O (20), K2HPO4 (87), KH2PO4 (8820), EPPS

(2520) Na2MoO4·2H2O (0.01), MnSO4·H2O (0.017), CoCl2·7H20 (.0004), CuCl2·2H2O

(0.17), ZnSO4·7H2O (0.01), chelated iron (1), NaNO2 (690) and (NH4)2SO4 (2,640) (19).

Sterile aeration was provided via ai um s fitted with 0.22 μm HEPA® filters (gas flow rate = 7.5 L/min). pH (7.5  0.01) was controlled via automated addition of NaHCO3 (80 g/L). Culture purity was confirmed via direct cell counts and DNA sequencing (20).

Growth experiments with electrochemically produced media

Upon achieving steady state removal of ammonia (defined as three consecutive days with an effluent NH3 concentration that was not statistically different at the 95% confidence interval from the three previous time-points; defined as Period 1), the autotrophic media described previously was replaced with spent media (from Period 1) that was electrochemically regenerated once or twice as described below (defined as

Periods 2 and 3 respectively). Reactor performance was then monitored for changes in cell counts, nitrogen species (ammonia, nitrite and nitrate), biokinetic and stoichiometric parameters. Statistical comparison of parameter estimates (yield coefficients, growth rates and half saturation constants) from Periods 1 through 3 was performed using univariate analysis of variance (ANOVA). The significance level (α) used was 0.05 and

130 the corresponding P-values (measure of consistency among experimental values) are reported where appropriate.

Nitrogen mass balances were constructed from running averages of ammonia, nitrite and cell count profiles, before during and after experiments to account for the changes in overall nitrogen loading. Carbon mass balances were also constructed by estimating the mass of CO2 transferred from aeration using a 2 film model to estimate

KLA,CO2from the measured oxygen mass transfer coefficient (KLA,O2 = 0.0054 1/s)(21), and the mass of bicarbonate added for pH control as well as carbon lost from cell purge.

All experiments were conducted in duplicate.

Biomass activity measurements

Oxygen uptake rates (OUR) were determined in duplicate for chemostat cultured

N. europaea cells using an YSI model 5331 oxygen electrodes (YSI Incorporated,

Yellow Springs, OH) connected to a dual channel meter (model 5300A; YSI

Incorporated, Yellow Springs, OH) such that DO readings were recorded every 10 seconds using an automated data acquisition system (Labview 8.0). Specific OUR

(sOURmax) values were determined by dividing the OUR values by the cell concentration of each sample. Yield coefficients, growth rates and half saturation constants were estimated using extant batch procedures described elsewhere (22).

dOU 1 f S,AOB  S  AOB     NH3 X (1) dt f max,AOB K  S AOB S,AOB  NH3 NH3 

where

131

OUAOB = active oxygen uptake due to ammonia oxidation to nitrite (mg/L)

µmax,AOB = maximum specific growth rate of Nitrosomonas europaea (1/day), fS,AOB = yield coefficient for Nitrosomonas europaea (mg biomass/mg NH3-NOD)

SNH3 = ammonia concentration (mg NH3-NOD/L),

XAOB = biomass concentration (mg biomass as COD/L),

KNH3 = half saturation constant (mg NH3-NOD/L).

NOD = nitrogenous oxygen demand (1 mg NH3 = 3.43 mg NOD when ammonia oxidation to nitrite is considered)

Wet chemistry and analytical chemistry

Cell counts were performed using a Brightline hemocytometer (Hausser

Scientific, Horsham PA). Samples for nitrogen analyses were filtered through 0.45 µm filters prior to immediate analyses for nitrite (Griess reagent) and ammonia (gas-sensing electrode, ThermoScientific, Waltham MA) (23). Trace metal analyses (Mg, Ca, Fe, Cu) were performed on acidified (2% HNO3) filtered samples (0.45 μm) using inductive coupled plasma atomic emission spectroscopy (Horiba ACTIVA-M ICP-AES, Edison

NJ).

Linear Sweep Voltammetry

Linear sweep voltammetry experiments were conducted using a µAutolab

Potentiostat (Ecochemie, Netherlands). Glassy carbon rotating disk electrodes (3 mm diameter) were produced in-house as described in Gallaway et al. (24). Nickel and copper were electrochemically deposited on a 4 mm diameter platinum rotating disk electrode.

132

An Ag/AgCl reference electrode and a platinum wire counter electrode were also used. A sweep rate of 5 mV/s was used to scan between 0.2 to -1.6 V versus Ag/AgCl.

Rotating Disk Electrode Constant Current Experiments

A three electrode cell described above consisting of a Ag/AgCl reference electrode, a platinum counter electrode and a nickel, glassy carbon or copper rotating disk electrode was also used in constant current experiments. The electrode was rotated at

400rpm and constant current of 2 mA/cm2 was applied for 325 s using µAutolab

Potentiostat (Ecochemie, Netherlands).

Constant Potential Experiments

A three electrode cell similar to the one described above was used in constant applied potential experiments. Instead of a rotating disk electrode a 5 pores per inch (ppi) porous glassy carbon electrode with an estimated area of 13.2 cm2 was used as the cathode. µAutolab Potentiostat was used to apply constant potentials between 0.9 to 1.6

V vs. Ag/AgCl for 1 hr. At the end of the experiments the samples were collected for ammonia measurements.

Flow Cell Experiments

The home-made flow-by electrolysis cell described in Sahin et al. was used for flow cell experiments (25). 80 pores per inch (ppi) glassy carbon electrode were used as the cathode and a dimensionally stable electrode (mesh ID 11873, De Nora) was used as anode. The anode and cathode were separated by a cation exchange membrane purchased from Snow pure (California, USA). Glassy carbon electrodes were obtained from Erg

133

Materials and Aerospace (California, USA) with the following dimensions (0.5 cm x 10.0 cm x 0.5 cm). Contact to these electrodes was established using Toray paper (Fuel Cell

Store) and conductive carbon cement adhesive (SPI Supplies, West Chester, PA). Power supplies (Agilent Technologies and GW Instek Group) were used to apply 2.46 A which is equivalent to 49.2 mA/cm2 based on cross sectional area of the flow cell. A peristaltic pump (Masterflex, Cole Palmer) was used to flow the catholyte and anolyte at a rate of

0.8 ml/s. pH controller purchased from Omega (USA) and was used to keep the pH between 8.4 and 8.6 at the catholyte. Experiments were run until 80% conversion was achieved and samples were collected throughout the experiment for ammonia measurement.

Results

Successful operation of a rMFC platform requires the selection of soluble mediators that not only support biosynthesis but can be efficiently recycled. In this study, we utilize the ammonia/nitrite couple as the soluble mediator. Consequently, experiments were designed to, 1) further our understanding ammonia regenerated via electrolysis and,

2) demonstrate that electrochemically derived ammonia can support biomass production.

In confirming that biomass can be produced using this approach, it is our intention to showcase a flexible bioproduction strategy that could be expanded for the production of biofuels and biochemical via genetic engineering strategies.

Electrochemical reduction of nitrite to ammonia

134

Since nitrogen can have oxidation numbers ranging from +5 to -3, the kinetics and selectivity of the nitrite reduction reaction are highly dependent on various criteria including the electrolytes, cathode materials, operating conditions (25). In this work, copper, nickel and glassy carbon electrodes were screened as cathode materials for catalyzing the electrochemical reduction of nitrite to ammonia (EpH 7.0 = 0.12 V vs.

Ag/AgCl). Results from linear sweep experiments using a phosphate buffer solution (pH

7; 50 mM) supplemented with sodium nitrite (50 mM) indicated that the onset of nitrite reduction was different for each material (Figure 2). Constant current experiments

(2mA/cm2) indicated that the nickel cathode (0.65 V) had the lowest overpotential

(additional potential required over thermodynamic potential) followed by copper (0.85 V) and glassy carbon (1.13 V). Even though glassy carbon displayed the highest overpotential among the three cathode materials tested, it was selected for further study due to its biocompatibility, inertness and long term performance.

Electrochemical regeneration of ammonia from spent culture media

Further experiments investigated the feasibility of electrochemically producing ammonia from spent growth media from N. europaea cultures using the glassy carbon cathode. Initial current efficiencies (ratio of current for nitrite reduction to ammonia to overall current) of 104.5% ± 9.0 were observed in the presence of high nitrite concentration (50 mM); however, as the nitrite concentration decreased to 22% of the initial concentration (11 mM), the current efficiency decreased to 58.4% ± 4.7 (Figure 3).

This decrease is attributed to mass transfer limitations at high conversions of nitrite and can be avoided by further optimizing the operating conditions at higher conversions. To demonstrate this latter point, constant applied potential experiments with an undivided

135 three electrode cell set-up were performed using 20 mM nitrite in phosphate buffer. Near

100% current efficiency was obtained under optimal operating conditions with lower nitrite concentrations (Figure 3 inset). These findings demonstrate that electrolysis is an effective approach for converting renewable electrical energy into chemical energy

+ (NH4 /NH3).

Long term performance of the flow cell components

No signs of corrosion and electrode passivation at the anode and cathode or precipitation in the catholyte and anolyte were observed in long term experiments using spent N. europaea culture media. However, an increase in catholyte pH was initially observed. This pH increase was due to production of OH- when nitrite is reduced to ammonia at the cathode and could not be offset by the proton content of the anolyte used in this experiment (phosphate buffer at pH 7.0). To prevent this increase, pH control of the catholyte via addition of hydrochloric acid (5 M) was performed. This resulted in

~3% dilution of the catholyte. Alternatively, use of an acidic anolyte and a proton exchange membrane would compensate for OH- produced at the cathode and would eliminate catholyte dilution in future systems.

Operation of the reverse microbial fuel cell (rMFC)

Once efficient electrochemical generation of ammonia from nitrite was demonstrated, experiments were performed to verify biomass growth using the regenerated ammonia. Results confirmed that this approach is a viable biosynthesis technology with electrochemically produced ammonia being successfully used to cultivate wild-type N. europaea (Figure 4 and Figure A1) in replicate continuous flow

136 reactors. Ammonia removal efficiency was maintained above 96 % during all periods of operation (Period 1 (growth on synthetic media prepared from ACS grade chemicals) =

97.2 ± 1.5 %; Period 2 (growth on electrochemically generated ammonia from period 1 effluent) = 97.9 ± 1.3 %; Period 3 (growth on electrochemically generated ammonia from period 2 effluent) = 95.5 ± 2.7 %) while observed biomass yields during these experiments were statistically identical (Period 1 = 0.06 ± 0.01 mg biomass/mg N; Period

2 = 0.06 ± 0.02 mg biomass/mg N; Period 3 = 0.06 ± 0.01 mg biomass/mg N; P = 0.99), demonstrating that the free energy efficiency of ammonia utilization for biomass construction (ratio of energy present as organic carbon divided by energy produced by ammonia oxidation to nitrite (26)) was steady (Figures 1 and A1; Tables 1 and A2).

Biokinetic and stoichiometric parameters describing growth (maximum specific growth

(μmax; n = 54), biomass yield coefficient (fS; n = 22), half saturation constant (KNH3; n =

22)) were also statistically unchanged across all culturing periods ((Table 1 and A2; for

μmax, P=0.11; For KNH3, P=0.15; for fS, P=0.48), and were within ranges previously reported by other researchers (27-32). Collectively these results demonstrate that the application of electrochemically processed ammonia did not impact the aerobic metabolism of N. europaea. These finding are not unexpected since all nutrients

(ammonia, oxygen, inorganic carbon, trace metals) were provided in excess during chemostat cultivation and during short-term assays.

Discussion

Coupling electrolysis and biosynthesis in a rMFC using mediated electron transfer requires an electron mediator that is inexpensive, highly soluble, non-flammable, and amenable to long term storage. In addition, this mediator must be compatible with the

137 electrochemical cell with low power requirements for regeneration and it must also be capable of being efficiently utilized by the biocatalyst. Bacteria that have evolved to obtain energy from natural redox mediators will have optimized this process, and are likely ideal for use in rMFC systems. Five inorganic redox active compounds that are

- 2+ capable of supporting lithoautotrophy were considered for this purpose (NH3, NO2 , Fe ,

H2S, H2). From a thermodynamic perspective, the minimum power required for biomass production, which is equal to the Gibbs free energy of formation of the biomass, is equivalent for these five mediators; however, since the energy state of electrons varies per mediator, the rate of reduced mediator consumption and subsequent regeneration is governed by the biocatalyst ability to synthesize biomass using the energy from the mediator. This ability can be quantified by examining biomass growth characteristics such as specific growth rates and yield coefficients for each system. Comparison of biomass growth kinetics and yields associated with the five mediators indicates that H2,

2+ H2S and Fe oxidizers can produce biomass more rapidly than ammonia and nitrite oxidizers (Table A3); however, the low water solubility, high flammability and toxic nature of H2 and H2S require complex storage and delivery capabilities, rendering use of

2+ H2 and H2S in rMFCs challenging (Table A4). Similarly, Fe is unstable under aerobic conditions and will undergo rapid abiotic oxidation to Fe3+, making storage and sustained

- use of this mediator potentially difficult. In contrast, NH3 and NO2 are highly water soluble, less toxic/flammable than H2S or H2 and can be stored under aerobic conditions.

Additionally, these compounds are ubiquitous as environmental pollutants, rendering them inexpensive and suitable for use in chemolithoautotrophic bioreactors.

138

The power required for electrochemical regeneration of the chosen mediator

(ammonia in this case) will be impacted by the cell overpotential. Using the experimentally determined overpotential (Figure 2) for glassy carbon (1.13 V) electrodes

2 operating at 2 mA/cm and an overpotential of ~0.400 V for oxygen evolution (33), and the thermodynamic cell potential of -0.497 V a cell potential of -2.0 V was calculated.

Using a aday’s law and assuming 100% cu ent efficiency, this suggests that a cha ge of

162 Ahr is required for the reduction of 1 mol of nitrite to ammonia, resulting in a power requirement of 0.324 kWhr (162 Ahr x 2 V). Using the experimentally measured biomass yield of 0.06 ± 0.01 mg biomass/mg N (Table 1), the power requirement for biomass production in this system was estimated to be 0.386 kWhr/g biomass (1.93 cents per g biomass assuming 0.05$kWhr).

In this study, the free energy efficiency of ammonia conversion to biomass was experimentally determined to be 4.81 ± 0.37% (Table 1). This efficiency was calculated by computing the ratio between the total amount of energy yielded from ammonia oxidation to nitrite during cultivation (230 kJ/mol N) to the energy conserved in biomass produced in these experiments (305 kJ/mol biomass C) (26). Assuming an energy capture efficiency of 56.5% during electrolysis (as calculated from overpotential experiments), we calculated that the overall energy efficiency of biomass production in this rMFC combined system could approach 2.71 ± 0.21%. This was calculated by multiplying the energy capture efficiency of electrolysis times the free energy efficiency of N. europaea growth. If one further assumes that the electrical energy used during electrolysis is derived from solar sources (conversion efficiency of 10% (34)) the biomass production efficiency of this ammonia-based rMFC system could be calculated by multiplying the

139 conversion efficiency of solar capture times the overall energy efficiency of biomass production. In this study, the biomass production efficiency approached 0.27 ± 0.02%.

This level of efficiency is within the same order of magnitude as photon to biomass conversion efficiencies approaching 1% that is observed for contemporary photosynthetic systems (7). It should be noted that while these calculations do not account for aeration and pumping energy requirements, they do take into account the energy requirement associated with electrolysis, which is expected to dominate the energy balance of this system. These results indicate that despite the high energy requirement for carbon fixation that is endemic to all lithoautotrophs, the current iteration of this rMFC platform can already yield high molecular weight organic material at a level that is comparable to the conventional photosynthetic paradigm.

Continuous operation of this ammonia based rMFC would need to resolve issues associated with nitrogen losses and inorganic carbon and proton supply. In the present study (2L scale), total nitrogen losses accounted for 5.4 mg N/day (2 mg N/day as biomass and ~ 3.4 mg N/day as gaseous N oxides (35)). This nitrogen can be offset through the supplementation of exogenous ammonia obtained from municipal or industrial (e.g. animal feeding operations) sources or from ammonia purchased at the market value ($0.30 ± 0.14/kg N based on 10 year average from 2000 to 2009 (36)).

Similarly, inorganic carbon (IC) needs to be supplied to ensure sufficient substrate for biomass fixation (0.086 mol C per mol of nitrogen processed). Sodium

2- bicarbonate/carbonate mineral ($0.09 ± 0.03/kg CO3 based on 10 year average from

2000 to 2009(36)) or CO2 enriched air ($0.02/kg CO2)(37) can be used to deliver this critical macronutrient. In this study, exogenous inorganic carbon (320 mg C/day) was

140 added using a saturated sodium bicarbonate solution. Proton supplementation will also be needed to replenish those lost in the biomass purge stream.

We recognize that neither electrolysis of nitrite to ammonia nor N. europaea growth on ammonia is novel; however, the coupling of both processes to produce biomass provides a significant proof of concept towards using mediated rMFCs as a viable strategy for producing organics from carbon dioxide using electrical energy. This approach can potentially be extended for direct production of high molecular weight organics, like aldehydes, alcohols, alkanes or alkenes directly from CO2 (11, 14). To accomplish this, genetic modifications of lithoautotrophs aimed at directly producing and secreting high energy organics will be needed. This will enable cells to directly produce biochemicals or biofuels without the need for further processing as is required for the utilization of biomass obtained from photosynthesis. Obviously in these future application, the overall process efficiencies and yields will depend on the ability of the organism to channel energy and carbon away from biomass synthesis and towards biofuel and biochemical production pathways. Tolerance of produced toxicants could also constrain production. In silico simulations examining metabolic fluxes could provide insights into strategies for maximizing biosynthesis while minimizing unwanted cellular processes (38-40). Thus, the efficiency of electrical energy conversion to biofuel using a similar approach would not be necessarily restricted to the efficiencies obtained with this proof-of-principle study. Collectively, these results show that although in its infancy, rMFCs offer great promise as an alternative to photosynthetic primary production. Future work will need to explore the use of alternative mediators and genetically modified biocatalysts for the purpose of generating biomass or biofuel products.

141

References

1. M. R. Connor and J. C. Liao, Curr Opin Biotechnol, 20, 307 (2009).

2. H. Alper and G. Stephanopoulos, Nat Rev Microbiol, 7, 715 (2009).

3. F. Zhang, S. Rodriguez and J. D. Keasling, Curr Opin Biotechnol, 22, 775 (2011).

4. G. Stephanopoulos, Science, 315, 801 (2007).

5. Z.-G. Zhu, Long, S.P., Ort, D.R., Current Opinion in Biotechnology, 19, 152 (2008).

6. C. L. Beadle and S. P. Long, Biomass, 8, 119 (1985).

7. D. A. Walker, Journal of Applied Phycology, 21, 509 (2009).

8. A. W. D. Larkum, Current Opinion in Biotechnology, 21, 271 (2010).

9. D. Gust, Moore, T.A., Moore, A.L., Accounts of Chemical Research, 42, 1890 (2009).

10. S. Atsumi, Hanai, T., Liao, J.C., Nature, 451, 86 (2008).

11. S. Atsumi, Higashide, W., Liao, J.C., Nature Biotechnology, 27, 1177 (2009 ).

12. A. F. Cann and J. C. Liao, Appl Microbiol Biotechnol, 81, 89 (2008).

13. K. M. Smith, K. M. Cho and J. C. Liao, Appl Microbiol Biotechnol, 87, 1045 (2010).

14. P. Lindberg, Park, S., Melis, A., Metabolic Engineering, 12, 70 (2010).

15. K. P. Nevin, T. L. Woodard, A. E. Franks, Z. M. Summers and D. R. Lovley, MBio, 1 (2010).

16. K. Rabaey, P. Girguis and L. K. Nielsen, Curr Opin Biotechnol, 22, 371 (2011).

17. H. Li, P. H. Opgenorth, D. G. Wernick, S. Rogers, T. Y. Wu, W. Higashide, P. Malati, Y. X. Huo, K. M. Cho and J. C. Liao, Science, 335, 1596 (2012).

142

18. P. Chain, Lamerdin, J. Larimer, F., Regala, W. Lao, V., Land, M., Hauser, L., Hooper, A.B., Klotz, M.G., Norton, J.M., Sayavedra-Soto, L.A., Arciero, D. M., Hommes, N.G., Whittaker, M.R., Arp, D.J., Journal of Bacteriology, 185, 2759 (2003).

19. K. Chandran, Love, N.G., Applied and Environmental Microbiology, 74, 2447 (2008).

20. R. Yu, Chandran, K., BMC Microbiology, 10 (2010).

21. E. L. Cussler, Diffusion: Mass Transfer in Fluid Systems Cambridge University Press (1997).

22. K. Chandran, Smets, B.F., Water Research, 39, 4969 (2005).

23. A. W. W. A. American Public Health Association, Water Pollution Control Federation, (APHA/AWWA/WPCF). Standard Methods for the Examination of Water and Wastewater. , Washington D.C. (1992).

24. J. Gallaway, I. Wheeldon, R. Rincon, P. Atanassov, S. Banta and S. C. Barton, Biosensors and Bioelectronics, 23, 1229 (2008).

25. A. Sahin, Wan-Ting Lin, W. O. Khunjar, K. Chandran, S. Banta and A. C. Westa, Submitted to Journal of The Electrochemical Society (2011).

26. T. Hoffman, Lees, H., Biochem J., 54, 579 (1952).

27. J. H. Ahn, Yu, R., Chandran, K., Biotechnology and Bioengineering, 100, 1078 (2008).

28. B. E. Rittmann, and P. L. McCarty., Environmental Biotechnology: Principles and Applications, McGraw-Hill, Boston, MA (2001).

29. G. Munz, C. Lubello and J. A. Oleszkiewicz, Chemosphere, 83, 720 (2011).

30. D. Guven, Schmidt, I., Process Biochemistry, 44, 516 (2009).

31. M. G. Henze, C. P. L. Jr., Gujer, W., Marais, G. v. R., Matsuo, T., Final Report: Activated Sludge Model No. 1, in, London UK (1987).

32. K. Chandran and B. F. Smets, Water Res, 35, 3153 (2001).

143

33. S. Schmachtel, M. Toiminen, K. Kontturi, O. Forsén and M. Barker, Journal of Applied Electrochemistry, 39, 1835 (2009).

34. M. Gratzel, Philos Transact A Math Phys Eng Sci, 365, 993 (2007).

35. W. O. Khunjar, Jiang, D., Chandran, K., In preparation for submission to Environmental Microbiology (2011).

36. T. D. Kelly and G. R. Matos, Historical Statistics for Mineral and Material Commodities in the United States, in, U. S. G. Survey Editor (2010).

37. J. Dooley, R. Dahowski and C. Davidson, On the Long-Term Average Cost of CO2 Transport and Storage, in, U. S. D. o. Energy Editor, Richland, Washington (2008).

38. N. R. Boyle, Morgan, J.A., Metabolic Engineering, 13, 150 (2011).

39. Y. S. Jang, J. M. Park, S. Choi, Y. J. Choi, D. Y. Seung, J. H. Cho and S. Y. Lee, Biotechnol Adv (2011).

40. A. V. Pandit and R. Mahadevan, Microb Cell Fact, 10, 76 (2011).

144

Tables and Figures

Table 1 Summary of biokinetic and stoichiometric parameters describing growth of wild- type Nitrosomonas europaea on electrochemically regenerated media1.

Maximum Half saturation Biomass yield coefficient Free energy

specific growth coefficient (fS;mg biomass as efficiency

rate μ (KNH3;mg N/L) oxygen equivalents/mg (%)

μmax;1/day) N)

Period 1a 0.50 ± 0.15 2.51 ± 0.65 0.06 ± 0.01 4.4 ± 1.6

Period 2b 0.36 ± 0.07 2.57 ± 0.63 0.05 ± 0.02 5.1 ± 1.0

Period 3c 0.24 ± 0.08 3.19 ± 1.34 0.05 ± 0.02 4.9 ± 0.6 an = 8, bn=6, cn= 8

1replicate results presented in Table S1; mean ± 95% confidence interval

145

Figure 1 Overview of a reverse microbial fuel cell which uses the ammonia/nitrite redox couple as a mediator and ammonia-oxidizing N. europaea cells as biocatalysts.

146

Figure 2 Linear sweeps of nickel, copper and glassy carbon rotating disk electrodes in 50 mM

Phosphate Buffer + 50 mM Sodium Nitrite at pH 7.0 at 400rpm. Inset : constant current

(2mA/cm2) experiments in 50 mM Phosphate Buffer + 50 mM Sodium Nitrite at pH 7.0 with nickel, copper and glassy carbon rotating disk electrodes at 400 rpm.

147

148

Period 2 that was electrochemically regenerated. COD represents the chemical oxygen demand associated with complete oxidation to CO2. Replicate results are presented in Figure A1.

149

Appendix A

Table A1 Half cell reactions and thermodynamic cell potential for inorganic electron

mediators.

Mediator Reactions at anode and cathode Thermodynamic Cell Gibbs Free Couple Potential (V) vs. SHE Energy (kJ/mol donor)1 - NO2 Cathode E= -0.165 -287

,NH3 Anode E= 1.23

(pH 7.0) Overall Reaction Ecell= -0.497 (after

Nernst Correction)

- NO3 Cathode E= 0.01 -235 - ,NO2 pH 7.0 Anode E= 1.23

Overall Reaction Ecell= - 0.40

Fe+3, Fe+2 Cathode -34 E= 0.77 (pH 1.9) Anode

E= 1.23 Overall Reaction

Ecell= -0.35 (after Nernst Correction) - SO4 ,H2S Cathode -988

pH 7.0 E= -0.052 Anode

E= 1.23 Overall Reaction Ecell= - 1.02 + H ,H2 Cathode E= 0.0 -237

pH 7.0 Anode E= 1.23

Overall Reaction Ecell= - 1.23

1-Negative sign denotes free energy released from aerobic oxidation of reduced form of

mediator

150

Table A2 Summary of biokinetic and stoichiometric parameters describing growth of wild-type Nitrosomonas europaea on electrochemically regenerated media1.

Maximum specific growth rate Free energy efficiency

µmax; 1/day) (%)

Period 1a 0.59 ± 0.19 5.5 ± 0.5

Period 2b 0.40 ± 0.11 5.9 ± 1.0 an =18, bn=14

1mean ± 95% CI

151

Table A3 Thermodynamic and biokinetic properties of common inorganic electron mediator couples.

Redox Couple Maximum Biomass yield pH Representative specific coefficient2 range biocatalyst considered3 growth (Y; mg 3 rate1 biomass (µmax;1/day produced/mg ) substrate consumed) - NH3/NO2 1 0.12 6 to 8 Nitrosomonas europaea

- - NO2 /NO3 0.6 0.02 6 to 8 Nitrobacter winogradskyi

Fe2+/Fe3+ 1.4 to 43.2 0.02 to 0.28 2 to 4 Acidithiobacillus ferrooxidans

2- H2S/SO4 2.5 to 7.6 0.08 to 0.11 6 to 8 Thiobacillus tepidarius

+ H2/H 7.4 to 10.1 0.51 to 0.55 7 Ralstonia eutropha H16

C6H12O6/CO2 25.2 0.36 to 0.60 6 to 8 Escherichia coli

1Further details provided in (1), (2-4) 2 - 2+ Biomass is denoted as C5H7O2N; Substrate defined as NH3, NO2 , H2S, Fe , H2, C6H12O6 3Further details provided in (5-12)

152

Table A4 Physical, thermodynamic and biokinetic properties of ammonia, nitrite, iron (II), hydrogen sulfide and hydrogen. - 2+ NH3 NO2 Fe H2S H2

Solubility in water 300 820 570 30 0.019

(g/L)1,2,3

Boiling Point -33.3 320 2861 -60 -253

(0C) 1

Autoignition temperature 690 N/A N/A 260 560

(0C) 1

OSHA permissible 50 N/A NR 14 NR5

exposure limit

(mg/m3)4

1- Values (13, 14)

2- Values are for iron sulfate(13)

3 – vol/vol at 15.60C(15)

4PEL - OSHA Permissible Exposure Limit (PEL) for general industry(16-18)

NR- OSHA does not report a PEL; N/A - not applicable

5-Can act as asphyxiant when oxygen levels are below 19.5% by volume

153

Table A5 Current Efficiency in the flow reactor in spent media from wild-type and genetically N. europaea.

Percentage of Nitrite Spent Media Spent Media and

reduced to Ammonia Isobutanol

11 104.5±9.0 95.2±13.8

65 58.4±4.8 59.3±12.6

154

Growth on electrochemically

produced NH3 Period 1 Period 2 100 0.16

90 Total N Load = Total N Load = 0.14 80 259.6 ± 13.1 mg N/day 209.7 ± 53.5 mg N/day 0.12 70 60 0.10

(%) 50 0.08 40 0.06 removed) 30 Removal efficiency 0.04

20 profile

Ammoniaremoval efficiency ObservedBiomass Yield 10 Observed yield 0.02 biomass(mg as COD/mg N profile 0 0.00 10 15 20 25 30 35 40

Time (day)

155

120

100

Current Efficiency % Current

20 Spent media from wild-type N. europaea Spent media from genetically modified N. europaea

0 0 5 10 15 20 25 30 35 40

Concentration of Nitrite Reduced to Ammonia (mM)

Figure A2 Current efficiency of nitrite reduction to ammonia in spent media from wild- type and genetically N. europaea.

156

References

1. M. T. Madigan and T. D. Brock, Brock biology of microorganisms, p. xxviii, Pearson/Benjamin Cummings, San Francisco, CA (2009).

2. D. White, The Physiology and Biochemistry of Prokaryotes., Oxford University Press, Oxford, United Kingdom (2000).

3. D. H. Bergey and J. G. Holt, Bergey's manual of determinative bacteriology, p. xviii, Williams & Wilkins, Baltimore (1994).

4. C. G. Friedrich, D. Rother, F. Bardischewsky, A. Quentmeier and J. Fischer, Applied and Environmental Microbiology, 67, 2873 (2001).

5. T. Kanagawa and E. Mikami, Applied and Environmental Microbiology, 55, 555 (1989).

6. WEF Press, Nutrient Removal: WEF Manual of Practice No. 34, McGraw Hill, Alexandria (2010).

7. A. P. Wood and D. P. Kelly, Archives of Microbiology, 144, 71 (1986).

8. C. Mignone and E. R. Donati, Biochem Eng J, 18, 211 (2004).

9. M. Nemati, S. T. L. Harrison, G. S. Hansford and C. Webb, Biochem Eng J, 1, 171 (1998).

10. A. Ishizaki and K. Tanaka, J Ferment Bioeng, 69, 170 (1990).

11. G. W. Luli and W. R. Strohl, Appl Environ Microbiol, 56, 1004 (1990).

12. R. S. Siegel and D. F. Ollis, Biotechnol Bioeng, 26, 764 (1984).

13. D. L. Perry and S. L. Phillips, Handbook of inorganic compounds, CRC Press, Boca Raton (1995).

14. Airgas, Hydrogen Sulfide Material Safety Data Sheet Number 001029, in (2010).

15. Airgas, Hydrogen Material Safety Data Sheet Number 00102, in (2010).

16. OSHA, Occupational Safety and Health Guidelines, in (2011).

157

17. OSHA, Chemical Sampling Information, in (2011).

18. NIOSH, NIOSH Pocket Guide to Chemical Hazards, in (2011).

158

Chapter 6: Performance of Glassy Carbon and Nickel Cathodes used for Reduction

of Nitrite to Ammonia for use in Biofuel Production

Abstract

Use of chemolithoautotrophic bacteria coupled with an electrochemical reactor to

produce biofuels is a promising alternative to existing biofuel production technologies.

Previously we have discussed the development of proof-of principle electrochemical flow

reactor for reduction of nitrite to ammonia which is the natural redox couple for

chemolithoautotrophic bacteria Nitrosomonas europaea. In this chapter we studied the

mechanism of nitrite reduction to ammonia in buffered electrolytes in the pH range

between 6.0 and 8.0 using linear sweep voltammetry and electrochemical impedance

spectroscopy. A limiting current plateau was observed for both cathode materials used.

The limiting current plateau was determined to be due to flux of protons to the electrode

surface which is controlled by the diffusion of phosphate buffer to the surface.

Additionally performance of nickel and glassy carbon cathodes have been investigated

using marathon studies. It was observed that the overpotential of glassy carbon electrode

decreased by ~500 mV over 10 days which would correspond to 24% decrease in power

requirements for the electrochemical reactor which is expected to be the dominating cost

for overall process.

Introduction

Sustainable biofuel production from renewable energy resources is an area of active research. Technologies such as ethanol production from corn (1), biodiesel production from microalgae (2) and ethanol production from switchgrass (3) are being developed.

159

One of the challenges in developing these technologies is the low efficiencies related to photosynthesis. Biomass production from photosynthesis has a maximum theoretical efficiency of 11.9 % and in real systems only about one percent efficiency has been achieved (4). Recently, efforts have been made to find alternative biofuel production methods to avoid these low efficiencies. One such alternative method is the use of chemolithoautotrophic organisms that can be genetically engineered to synthesize biofuels. Chemolithoautotrophs use chemical reactions as their energy source instead of photons captured during photosynthesis. When coupled to an electrochemical system that can drive the chemical reaction chemolithoautotrophs can convert CO2 to biofuel providing an alternative method that bypasses photosynthesis. The reducing equivalents required for this process can come directly from an electrode, direct electron transfer, or from a redox couple, mediated electron transfer. Organisms such as Geobacter sulfurreducens (5, 6) and Shewanella putrefaciens (7) have been reported to participate in direct electron transfer. The main challenge in designing a system that involves direct electron transfer is the scale-up. For direct electron transfer, organisms need to be immobilized at the electrode surface which leads to internal and external mass transfer limitations. On the other hand when a redox couple is used as a mediator the electrochemical reactor and bioreactor can be spatially and temporally decoupled which allows for simultaneous optimization of both processes. A list of redox couples capable of

- +2 +3 - - - use in chemolithoautotrophic organisms are CHOO /CO2, Fe /Fe , NO2 /NO3 , H2S/SO4

2 + , H2/H and NH3/NO2 (8-11). Desirable properties for a mediator couple include fast oxidation/reduction kinetics at the electrode as well as in the bacteria, low cost, abundance, safety and solubility.

160

Previously we have demonstrated coupling of a bioreactor containing chemilithoautotrophic bacteria Nitrosomonas europaea with an electrochemical reactor

- that is used to recycle the redox couple used by this organism, NH3/ NO2 (12). Li et al. demonstrated the production of isobutanol and 3-methyl-1-butanol using Ralstonia

- eutrophia which is a lithoautrophic organism that uses the redox couple CHOO /CO2 (11).

For both these systems power requirements for the electrochemical system are expected to be the dominating cost of the overall process and therefore optimization and design of the electrochemical reactor used in mediator reduction is essential. For the reactor that is coupled with N. europaea bioreactor this involves study of nitrite reduction to ammonia at buffered neutral electrolytes.

There are only a few studies that focus on nitrite reduction in buffered neutral solutions but a lot can be learned from the immense literature present on nitrate reduction.

Catalytic activity of different cathode materials such as copper, nickel, platinum, glassy carbon, tin and bismuth as well as some bimetallic cathodes have been investigated for nitrate reduction for low level radioactive waste water treatment (13-17). The goal in this application is to reduce nitrates to nitrogen gas which is harmless for the environment.

However the main challenge is the selectivity of the reduction process. Nitrogen has oxidation number ranging from +5 to -3. Therefore there are a large number of side products form based on the electrolyte composition and pH, choice of cathode and operating conditions. In the basic electrolytes found in low level radioactive waste treatment nitrate reduction to nitrite was identified as the rate determining step (18, 19).

The final product of electrolysis depends on the steps following this nitrate reduction to nitrite. An undesired final product in most studies was found to be ammonia (17, 20). In

161 acidic electrolytes the reduction proceeds very smoothly as a result of homogenous autocatalytic reaction in the electrolyte (21). In weakly alkaline and neutral solutions nitrite reduction is investigated due to applications of nitrate removal from electrolytes used in ion exchange process for drinking water treatment. These electrolytes contain high concentrations of regeneration agents such as sodium chloride and sodium bicarbonate.

Bouzek et al. reported in these electrolytes the reduction kinetics of nitrate is limited by an unspecified surface phenomena (22).

Electrochemical and surface analysis techniques such as rotating ring disk electrode

(RRDE), Fourier transform infrared spectroscopy (FTIR), differential electrochemical mass spectroscopy (DEMS), electrochemical quartz crystal microbalance (EQCM), on- line electrochemical mass spectroscopy (OLEMS), surface enhanced infrared absorption spectroscopy (SEIRAS) were employed by different groups to determine the intermediates involved in reduction and the mechanism. Using EQCM Tada and Schimazu determined surface coverage of absorbed nitrate during reduction on Sn-modified platinum electrodes and concluded that the rate determining step is reaction of absorbed nitrate with hydronium ion (19). Reyter et al. observed the same absorption phenomena using EQCM with copper electrodes and reported two potential regions with different reactions in the first one nitrite is reduced to hydroxylamine and at more negative potentials direct reduction of nitrite to ammonia was observed. It was also reported that, during long-term electrolysis, hydroxylamine was not detected as a product because it is rapidly reduced to ammonia (23). Groot and Koper identified large amounts of NO+ during reduction and suggested that NO+ instead of nitrate is involved in actual electron transfer (24). Ammonia was also identified as the main reduction product in acidic media (24). Duca et al. used an

162

RRDE to detect electroactive intermediates such as NH2OH , OLEMS for volatile product and FTIR for other intermediates. In acidic media NO was identified as an intermediate that is further reduced to N2O. Whereas in basic electrolytes nitrite was observed to reduce to NH2OH followed by a reduction to NH3, however the pH range between 4-13 was not investigated in this study (25). Rima et al. used SEIRAS and determined that adsorbed

NO2 was reduced to NO either via direct conversion or via IR-inactive surface nitrite species on platinum electrode (26).

Even though catalytic performance of many cathodes and the mechanism of

reduction in different electrolytes have been investigated, there are only a few studies on

long term performance of these electrodes. Long term performance of nickel and lead

electrodes were tested for use in low level radioactive waste water treatment at Savannah

River Site. Nickel and lead were previously determined to have high destruction

efficiency of nitrate in batch electrolysis experiments. Both materials showed a decrease

in performance after 50 hours of operation in the flow reactor. In 1000 hr experiments

lead did not show any further decrease in performance compared to 50 hr experiments.

Other components of the electrochemical system such as the cation selective membrane

and anode were also tested. Good stability of nafion was observed but anode materials

such as oxygen evolving dimensionally stable anode (DSA), platinized niobium and

platinum clad niobium anodes showed signs of failure after several hundred hours (27).

Li et al. reported an increase in surface roughness of Cu-Zn electrodes after 5 hr of

electrolysis (28). This change led to passivation in electrolysis experiments performed at

initial pH of 3.0 but for initial pHs of 6.5 and 12.0 no passivation was observed (28).

163

Reyter et al. reported loss of activity on copper electrode after the first few minutes of

electrolysis which could be reversed by using a square wave potential pulse (23).

In chapter 4, we have outlined the criteria for design of an electrochemical reactor for use in biofuel production and studied performance of different cathode materials in growth media for N.europaea (29). We reported that nickel cathodes had approximately

~500 mV less overpotential than glassy carbon electrodes (29). Comparing the performance of fresh glassy carbon and nickel electrodes the latter is potentially a better candidate, however the performance of the cathode materials in long-term operation should be considered as well. In this chapter, we report nitrite reduction at nickel and glassy carbon cathodes in the pH range 6.0-8.0 in phosphate buffer solutions, as well as the long term performance of these cathodes using electrolysis, linear sweep voltammetry and electrochemical impedance spectroscopy in phosphate buffer solution at neutral pH.

Experimental

Chemicals and Reagents

Sodium phosphate dibasic, sodium phosphate monobasic, sodium nitrite and

ammonium sulfate were purchased from Sigma-Aldrich. Glassy carbon rotating disk

electrodes with 3 mm diameter were prepared in house as described (30). Nickel rotating

disk elect odes we e e a ed by lating nickel using Watt’s solution on 4 mm diamete

platinum rotating disk electrode.

164

Linear Sweep Voltammetry

Linear sweep voltammetry experiments were conducted using a µAutolab

Potentiostat (Ecochemie, Netherlands) and a Pine Instruments rotator. A three electrode cell set-up as described in Sahin et al. (29) consisting of Ag/AgCl reference electrode and a platinum counter electrode was used. 50, 100 and 300 mM phosphate buffer containing various concentrations of nitrite at a pH range of 6.0-8.0 were prepared fresh before each experiment and the exact concentrations used are listed in the captions for each figure.

Linear sweeps were performed at a sweep rate of 5 mV/s scanning between 0 and -1.6 V vs. Ag/AgCl. All the experiments were done in duplicate. Fits to the linear sweep data were done using Sigma Plot non-linear equation fitting software.

Electrochemical Impedance Spectroscopy

The same three electrode cell set-up described above was used for electrochemical impedance spectroscopy experiments. The experiments were performed at constant cathodic current density of 2 mA/cm2 , in the frequency range of 100 kHz- 0.1

Hz with 9 measurements every decade. Linearity of the response was verified by applying a range of perturbations and a perturbation of 1x10-5 A was used for subsequent experiments. Data was collected using Nova 1.5 software and fitted using ZSim software. Each experiment was done in duplicate.

Marathon Study

Glassy carbon and nickel rotating disk electrodes were submerged in 50 mM phosphate buffer and 50 mM sodium nitrite solution at pH 7.0. Constant current

165 electrolysis was performed using a two electrode set up consisting of the rotating disk electrode and a platinum mesh. 2 mA/cm2 current was applied. Every 24 hours linear sweep and electrochemical impedance spectroscopy experiments were performed in fresh solution in the three electrode cell set-ups described above. Experiments were continued for 10 days and replicated 3 times.

Results and Discussion

Linear Sweep Voltammetry

In chapter 4, we observed a limiting current plateau for both nickel and glassy carbon electrodes (29). When glassy carbon was used this plateau was shifted toward more negative overpotentials, overlapping with hydrogen evolution making it difficult to observe. On both nickel and glassy carbon electrodes the limiting current increased linearly with the square root of rotation speed but did not depend on nitrite concentration at concentrations higher than 10 mM (29). In this study we further investigated the cause of this mass transfer limited plateau region. Reduction of nitrite to ammonia in neutral solutions is shown in reaction 1.

Rxn1

As shown in reaction 1 nitrite reacts with protons at the electrode to form ammonia and water. Since nitrite was not the limiting reactant we conducted experiments at different concentrations of ammonia and protons which are candidates as limiting product and reactant, respectively. Figure 1 shows linear sweep voltammetry for 50 mM nitrite in 100

166 mM phosphate buffer solution with 5, 10, 15, 100 mM ammonium sulfate. As shown in this figure the addition of ammonium sulfate does not affect the limiting current plateau.

Figure 2 shows polarization curves at 100 mM phosphate buffer at pH varying from 6.0 to 8.0. It is observed that as the pH decreases current density increases. The increase of limiting current density with increasing proton concentration suggests that current is limited by the flux of protons to the electrode surface. Figure 3 shows polarization curves at pH 7.0 at different phosphate buffer concentrations. As the phosphate buffer concentration increases the limiting current also increases. This result suggests that the current is limited by the flux of protons to the electrode which is determined by the flux of phosphate buffer to the surface.

The effect of pH on nitrate reduction was studied previously by others under different conditions. Duca et al. studied the effect of pH on nitrite reduction and observed an increase in current density with decreasing pH in the pH range of 0- 3.0 (25). After analysis of this result the authors concluded homogenous phase reactions that depend on pH must be an integral part of the nitrite reduction mechanism Similarly Rutten et al. observed an increase in current density with increasing proton concentration range of

0.45- 2.7 M and reported both formation of nitrosyl ion from HNO2 and the electrochemical reduction were both contributing to this change (31). Munoz and

Schultze observed the same trend at a higher pH range (3-8) and suggested a 3 step mechanism that involves absorption of nitrite to the electrode to produce N2O as the final product (32). In our previous study we observed that the main product of nitrite reduction at pH 7.0 in phosphate buffer was ammonia. Therefore we suggest the mechanism shown in the schematic in Figure 4 which describes the acid-base equilibrium for the phosphate

167 buffer in the bulk solution, diffusion of phosphate buffer to the electrode and nitrite reduction reaction at the electrode. If we assume Tafel kinetics at the electrode which can be represented by equation (1)

(1)

- where is concentration of H2PO4 at the electrode surface and k is the reaction rate constant. We can substitute the equation for shown below

(2)

where is the concentration of phosphate buffer in bulk solution and is the limiting current density given by the Levich equation below and:

(3)

where s, is the stoichiometric coefficient and δ is the diffusion layer thickness and is given by:

(4)

Combining equation 1 and 2 we get Koutecky Levich equation below:

(5)

where D, the diffusion coefficient for the buffer, is assumed to be 5x10-6 cm2/, ν, the kinematic viscosity, is assumed to be 1x10-2 cm2/s, n, number of electrons, is 6, s =8, F is

168

a aday’s constant, 96485 C/mol and α, transfer coefficient is assumed to be 0.4 based on impedance data presented in the next section.

The fits to the data are shown in figures 2 and 3 with grey lines. Based on these fits we can conclude that the limiting current plateau is due to the diffusion of the buffer species to the electrode which determines the flux of protons required for nitrite reduction to the electrode. The rate constant, k, was determined to be 2.7x10-9 ± 6.9x10-11 cm/s for nickel cathodes based on figures 2 and 3.

Electrochemical Impedance Spectroscopy

Figure 5 shows Nyquist plots for nickel and glassy carbon electrodes at a pH range of 6.0-8.0 in 100 mM phosphate buffer and at pH 7.0 at phosphate buffer concentrations of 50, 100 and 300 mM. For nickel electrodes above pH 7 and for all solutions with glassy carbon electrode two semicircles are observed. Based on mechanism proposed in figure 4 we suggest that these semicircles are due to diffusion of the phosphate buffer species to the surface and nitrite reduction to ammonia at the surface. To test this we build a simple model for this mechanism.

During a transient the total current density is the sum of faradaic, if, and capacitive current, iCDL, as shown below:

(6) where capacitive current is:

(7)

169

Faradaic current density for this mechanism is described by Tafel equation in equation (1). The faradaic current density depends on the potential, (V-φ), and surface concentration of buffer species, . Perturbation of the form:

(8) where is a complex function, is applied to all dependent variables. The first order expansion of the faradaic current density with respect to potential and surface concentration of buffer is given below.

(9)

The partial derivatives are given below in equations:

(10)

(11)

The complex function, is derived by solving the convection, diffusion equation for an

RDE and was presented previously by Scherson and Newman in detail (33). The equation below is obtained for the complex function:

(12)

Substituting equation (12) into equation (11) and equations (10) and (11) back into equation (9) we get an overall equation for the faradaic impedance as shown below:

170

(13)

The overall complex impedance is given by

(14)

When we plug in equation (13) into equation (14) we get the following equation for the overall complex impedance.

(15)

Equation (15) can be rewritten in terms of circuit elements as shown below:

(16)

where Re is electrolyte resistance, ZD is Warburg type diffusion term and Rt is charge transfer resistance. Zsim software was used to fit this model to impedance data in figures

5 and 6. The data is shown with symbols and the fits are presented by lines. Based on this data transfer coefficient for nitrite reduction on nickel was determined to be 0.4±0.18.

Marathon Study

Long-term performance of the glassy carbon and nickel electrodes were investigated using marathon studies. As described in the experimental section these electrodes were used in constant current electrolysis experiments and their performance was measured using linear sweep voltammetry and electrochemical impedance studies over time. Figure

171

6 shows the polarization curves and Nyquist plots for the two cathodes. For the glassy carbon cathode ~500 mV shift in potential is observed after the second day. Additionally formation of a film was observed on glassy carbon after the second day. This film formation also coincides with changes in the Nyquist plot. We observe a significant change in shape of curves after the second day. For the nickel electrodes no trend is observed for the ola ization cu ves. The im edance measu ement doesn’t change with time. However, formation of a black film is observed after the first day. To quantify the change in polarization curves rate constant, k, can be plotted as a function of time as shown in figure 7. It is observed that initially the rate constant for glassy carbon electrode are significantly lower than those for nickel but after 2 days they become very similar.

We previously observed film formation on copper electrodes during bulk electrolysis experiments after 1 hr (34). Other groups also have observed such films and reported passivation due to formation of the films (14, 28). Experiments with X-ray photoelectron spectroscopy (XPS) (see Appendix A) were inconclusive in determining the chemical composition of the film.

Conclusions

In this paper nitrite reduction to ammonia was studied in phosphate buffer between pH 6.0-8.0. Linear sweep voltammetry experiments were performed and a limiting current plateau was observed. The effect of ammonia concentration, pH and buffer concentration on the plateau were studied. Changes in concentration of ammonia did not have any effect on limiting current. However, it was observed that the limiting current increased with increasing proton and buffer concentration. Linear sweep

172 voltammetry and electrochemical impedance spectroscopy were used in marathon studies to monitor the change in electrode performance over time. Formation of a film was observed on both cathodes. The overpotential for nitrite reduction on glassy carbon decreased by 500 mV in 10 days whereas no significant changes were observed for the nickel electrode.

Acknowledgements

This research was supported by the DOE ARPA-E Electrofuels program (DE-0000060).

List of Symbols

CDL double layer capacitance, F

- 3 bulk concentration of H2PO4 , mol/cm

- 3 surface concentration of H2PO4 , mol/cm

D diffusion coefficient, cm2/s

F a aday’s constant 96500, C/mol i current density, A/cm2 j imaginary number, j2= -1 k pre-exponential factor, cm/s n number of electrons r radius of electrode, cm

173

R gas constant, 8.314 J/mol K

2 Re elect olyte esistance, Ω/cm

2 Rt charge transfer resistance, Ω/cm s stoichiometric coefficient t time, s

T temperature, K

Z im edance, Ω

ZD diffusion im edance, Ω

Greek symbols

α charge transfer coefficient

δ diffusion layer thickness, cm

η electrode overpotential, V

κ electrolyte conductivity, 1/ Ω cm

ν kinematic viscosity, cm2/s

ω frequency, s-1

Ω rotation speed, rad/s

Subscripts

174

L limiting ss steady-state

Superscripts

~ perturbation

175

References

1. G. Franceschin, A. Zamboni, F. Bezzo and A. Bertucco, Chemical Engineering Research and Design, 86, 488 (2008).

2. P. Schenk, S. Thomas-Hall, E. Stephens, U. Marx, J. Mussgnug, C. Posten, O. Kruse and B. Hankamer, BioEnergy Research, 1, 20 (2008).

3. D. Pimentel and T. W. Patzek, Natural Resources Research, 14, 65 (2005).

4. D. Walker, Journal of Applied Phycology, 21, 509 (2009).

5. D. R. Bond and D. R. Lovley, Appl. Environ. Microbiol., 69, 1548 (2003).

6. F. Caccavo, Jr, D. J. Lonergan, D. R. Lovley, M. Davis, J. F. Stolz and M. J. McInerney, Appl. Environ. Microbiol., 60, 3752 (1994).

7. H. J. Kim, H. S. Park, M. S. Hyun, I. S. Chang, M. Kim and B. H. Kim, Enzyme and Microbial Technology, 30, 145 (2002).

8. H. D. Peck, Jr., Annu Rev Microbiol, 22, 489 (1968).

9. D. P. Kelly, Archives of Microbiology, 171, 219 (1999).

10. I. Suzuki, Annu Rev Microbiol, 28, 85 (1974).

11. H. Li, P. H. Opgenorth, D. G. Wernick, S. Rogers, T.-Y. Wu, W. Higashide, P. Malati, Y.-X. Huo, K. M. Cho and J. C. Liao, Science, 335, 1596 (2012).

12. W. O. Khunjar, Sahin,A.,West, A.C., Chandran, K., Banta, S. , Biomass production from ammonia generated in a reverse microbial fuel cell, in (2012).

13. L. Durivault, O. Brylev, D. Reyter, M. Sarrazin, D. Bélanger and L. Roué, Journal of Alloys and Compounds, 432, 323 (2007).

14. Horanyi, G., Rizmayer and E. M., Electrocatalytic reduction of NO[2] and NO[3]ions at a platinized platinum electrode in alkaline medium, Elsevier, Amsterdam, PAYS-BAS (1985).

176

15. H.-l. Li, D. H. Robertson, J. Q. Chambers and D. T. Hobbs, Journal of The Electrochemical Society, 135, 1154 (1988).

16. S. Cattarin, Journal of Applied Electrochemistry, 22, 1077 (1992).

17. N. Chebotareva and T. Nyokong, Journal of Applied Electrochemistry, 27, 975 (1997).

18. A. C. A. de Vooys, R. A. van Santen and J. A. R. van Veen, Journal of Molecular Catalysis A: Chemical, 154, 203 (2000).

19. K. Tada and K. Shimazu, Journal of Electroanalytical Chemistry, 577, 303 (2005).

20. Z. Mácová, K. Bouzek and J. Še ák, Jou nal of A lied lect ochemist y, 37, 557 (2007).

21. G. Horányi and E. M. Rizmayer, Journal of Electroanalytical Chemistry and Interfacial Electrochemistry, 140, 347 (1982).

22. K. Bouzek, M. Paidar, A. Sadílková and H. Bergmann, Journal of Applied Electrochemistry, 31, 1185 (2001).

23. D. Reyter, D. Bélanger and L. Roué, Electrochimica Acta, 53, 5977 (2008).

24. M. T. de Groot and M. T. M. Koper, Journal of Electroanalytical Chemistry, 562, 81 (2004).

25. M. Duca, V. Kavvadia, P. Rodriguez, S. C. S. Lai, T. Hoogenboom and M. T. M. Koper, Journal of Electroanalytical Chemistry, 649, 59 (2010).

26. F. R. Rima, K. Nakata, K. Shimazu and M. Osawa, The Journal of Physical Chemistry C, 114, 6011 (2010).

27. J. D. Genders, D. Hartsough and D. T. Hobbs, Journal of Applied Electrochemistry, 26, 1 (1996).

28. M. Li, C. Feng, Z. Zhang, X. Lei, R. Chen, Y. Yang and N. Sugiura, Journal of Hazardous Materials, 171, 724 (2009).

29. A. Sahin, Wan-Ting Lin, W. O. Khunjar, K. Chandran, S. Banta and A. C. West, Submitted to Journal of The Electrochemical Society (2011).

177

30. J. Gallaway, I. Wheeldon, R. Rincon, P. Atanassov, S. Banta and S. C. Barton, Biosensors and Bioelectronics, 23, 1229 (2008).

31. O. W. J. S. Rutten, A. V. Sandwijk and G. V. Weert, Journal of Applied Electrochemistry, 29, 87 (1999).

32. A. G. Muñoz and J. W. Schultze, Electrochimica Acta, 49, 293 (2004).

33. D. A. Scherson and J. Newman, Journal of The Electrochemical Society, 127, 110 (1980).

34. A. Sahin, Lin, W., Khunjar, W.O., Banta,S., West, A.C., Electrochemical Reduction of Nitrite to Ammonia for use in Reverse Microbial Fuel Cells in Journal of The Electrochemical Society (2011).

178

Tables and Figures

)

2 [(NH4)2SO4]=0 -5

[(NH ) SO ]=5 mM -10 4 2 4 [(NH4)2SO4]=15 mM [(NH ) SO ]=100 mM -15 4 2 4

Current Density (mA/cm Density Current

-20

-25 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 Potential (V) vs Ag/AgCl

179

0 pH 8.0 pH 7.5

pH 7.0 -10 pH 6.5

pH 6.0 -20

Current Density (mA/cm2) Density Current Experimental Data Fit

-30 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 Potential (V) vs Ag/AgCl

180

0 50 mM

100 mM -10 300 mM

-20

-30

Current Density (mA/cm2) Density Current Experimental Data -40 Fit

-50 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 Potential(V) vs Ag/AgCl

Figure 3 Experimental polarization curves and fits to data for a nickel rotating disk electrode at 400 rpm in 50 mM sodium nitrite in 50, 100, 300 mM phosphate buffer at pH

7.0.

181

Figure 4 Schematic showing nitrite reduction at the electrode and acid-base equilibrium for phosphate buffer in solution for a rotating disk electrode with a diffusion layer thickness, δ.

182

200 200 A-Glassy Carbon B-Glassy Carbon

150 150 ph6

)

2 pH8 2 100mM pH7 50mM 100 pH 6.5 100 300mM pH 7.5

-Z''(ohm cm

50 50

0 0 0 50 100 150 200 0 50 100 150 200 2 Z'(ohm cm ) Z'(ohm cm2)

100 140 C-Nickel D-Nickel

120 80

) 100 2 60 80

60 40

-Z''(ohm cm2)

-Z''(ohm cm

40 20 20

0 0 0 20 40 60 80 100 120 140 0 20 40 60 80 100 2 Z'(ohm cm2) Z'(ohm cm )

Figure 5 Panel A-B and C-D show Nyquist plots for glassy carbon and nickel rotating disk electrodes, respectively, in 50 mM sodium nitrite at pH 6.0-8.0 (panels A and C) in

100 mM phosphate buffer and at pH 7.0 at phosphate buffer concentrations of 50,100 and

300 mM (panels B and D). Experimental data for impedance is shown with symbols and the fits are shown using continuous lines.

183

160 2 A-Glassy Carbon B-Glassy Carbon 140 time 0 0 time=0

)

day 1 2 120 day 2 day 1 day 5

) -2 2 100 day 6 days 2-10 day 8 80 day 10 -4 time 0 60 -Z''(ohm cm -6 day 1 day 2 40 day 5 Current Density (mA/cm -8 day 6 20 day8 day 10 0 -10 0 20 40 60 80 100 120 140 160 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0

Z'(ohm cm2) Potential (V) vs Ag/AgCl

100 2 C-Nickel D-Nickel

)

80 2 time=0

) -2

2 day 5 60 day 10 -4

-Z''(ohm cm -6

20 Current Density (mA/cm -8

0 -10 0 20 40 60 80 100 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 Z'(ohm cm2) Potential (V) vs Ag/AgCl Figure 6 Panel A-B and C-D show Nyquist plots and polarization curves obtained on glassy carbon and nickel rotating disk electrodes in fresh 50 mM sodium nitrite and 50 mM phosphate buffer at a pH of 7.0 as a function of time during the marathon study, respectively. Both impedance and polarization curves were recorded for a rotation speed of 400rpm. The scan rate for the linear sweeps was 5.0 mV/s. Experimental data for impedance is shown in symbols and the fits are shown using continuous lines.

184

1e-7

1e-8

1e-9

k (cm/s) k 1e-10

Glassy Carbon 1e-11 Nickel

1e-12 0 2 4 6 8 10 12 Time (days)

Figure 7 Rate constant, k, as a function of time for glassy carbon and nickel electrodes.

185

Appendix A

X-ray Photoelectron Spectroscopy (XPS) experiments were performed on glassy carbon and nickel electrodes after marathon studies. As mentioned earlier formation of a film was observed on both cathode materials. To study the characteristics of this film, samples were prepared by applying constant current (2 mA/cm2) to nickel and glassy carbon plates with an electrode area of 0.12cm2. The electrodes were then washed thoroughly and placed in a vacuum oven for 3 hours. For glassy carbon plates the temperature in the vacuum oven was raised to

80oC to avoid off-gassing during XPS experiments. A PHI 5500 ESCA (XPS)/ISS Spectrometer was used for XPS experiments. The settings used in each experiment are listed on the figures.

Figure A1 shows the XPS survey for a glassy carbon electrode with peaks for sodium, oxygen and carbon. No peaks are observed around 400 eV region where the signal for nitrogen is expected to appear.

186

Min: 3 Max: 10317 O 1s

Na 1s

Na-A N(E)

C 1s

Na 2s O 2s

12001080 960 840 720 600 480 360 240 120 0 Binding Energy (eV)

Figure A1 Survey for glassy carbon electrode (EV/step 0.8 eV/s, Time/Step 100 msec, number of sweeps 3, pass energy 187.85 eV)

187

In order to determine the atomic concentration of the species observed in the survey and to see whether nitrogen is present, multiplex studies of each region were conducted. Figure A2 shows the results of a multiplex experiment. In this plot atomic percentages for carbon, nitrogen, sodium and oxygen are shown. Approximately 1% of nitrogen was detected in the sample.

Min: 0 Max: 100 Mux Summary - baseline subtracted: RegionA.C. HeightArea FWHM C 1s 43.18248 446 1.049 N 1s 0.99 5 17 0.161 Na 1s16.02649 946 1.333 O 1s 39.82569 976 1.395

% 43.18 % 39.82 %

16.02 %

0.99 % C 1s N 1s Na 1s O 1s Region

188

Similar experiments were also conducted for nickel electrode figure A3 is an XPS survey and figure A4 is the result of a multiplex experiment. In the survey experiment nickel, carbon, sodium, oxygen and a small amount of nitrogen are detected. In the multiplex experiment 2.97 % nitrogen was detected but these experiments were inconclusive in determining the nitrogen compound present on the surface.

Min: 207Max: 11507 Na 1s

O 1s Ni 2p3

N(E) C 1s Na-A

N 1s

12001080 960 840 720 600 480 360 240 120 0 Binding Energy (eV)

Figure A3 Survey for nickel electrode (EV/step 0.8 eV/s, Time/Step 100msec, number of sweeps 3, pass energy 187.85 eV)

189

Min: 0 Max: 100 Mux Summary - baseline subtracted: RegionA.C. HeightArea FWHM C 1s 45.30222 371 1.170 N 1s 2.97 32 39 1.103 Na 1s9.28 287 430 1.345 Ni 2p0.97 88 97 0.923 O 1s 41.48323 810 2.379 % 45.30 % 41.48 %

9.28 % 2.97 % 0.97 % C 1s N 1s Na 1s Ni 2p O 1s Region

Figure A4 Atomic concentrations of carbon, oxygen, sodium, nickel and nitrogen determined in multiplex experiments for nickel electrode.( EV/step 0.1 eV/s, Time/Step 300 msec, number of sweeps 30, pass energy 11.75 eV)

190

Chapter 7 Conclusions

In this thesis two applications of using electrochemical methods coupled with biological elements are presented: electrochemical biosensors and biofuel production from electricity. In chapter 2 a novel dual enzyme electrochemical biosensor for detection of organophosphates

(OPs) is demonstrated. The dual enzyme biosensor consists of organophosphorus hydrolase

(OPH) which is capable of hydrolyzing a wide class of OPs and horseradish peroxidase which is capable of detecting various phenolic leaving groups that result from the hydrolysis of OPs. A model compound, dichlofenthion which could not be detected with existing electrochemical biosensors is detected. Additionally reverse phase HPLC is used for determining kinetic parameters for hydrolysis of dichlofenthion.

In chapter 3, guidelines for electrochemical biosensor design with an immobilized enzyme layer are outlined. Depending on the application of the biosensor the design goal might be to develop a sensor with target sensitivity, limit of detection and/or linear range.

Dimensionless variables that affect these targets are identified and plots showing the effects are presented. An electrochemical formaldehyde biosensor was designed to demonstrate use of these criteria to design a biosensor.

The second application presented in this thesis is biofuel production using electrical energy. To demonstrate this, a reverse microbial fuel cell consisting of chemolithoautotrophic bacteria, N. europaea and an electrochemical reactor that is used to regenerate the natural redox couple of N. europaea was described. In chapter 4, a proof of principle electrochemical reactor is developed for reduction of nitrite to ammonia with high current efficiency. In chapter 5, this electrochemical reactor was coupled with a chemostat containing wild type N.europaea. The

191 coupled reactors were operated for over 30 days. The ammonia removal efficiency of the bacteria and biomass yield did not show any statistical difference when electrochemically regenerated mediator was used instead of synthetic media. Based on this data it was concluded that the coupling of the reactors did not have any impact on aerobic metabolism of the bacteria.

Once the growth of bacteria on electrochemically reduced mediator was established, the performance of cathodes was studied in more detail using linear sweep voltammetry and electrochemical impedance spectroscopy. In the pH range required by the chemostat a limiting current plateau was identified and determined to be due to flux of protons to the electrode surface. Marathon studies were conducted to study the changes in performance of cathodes with time.