evaluation of unified numerical and experimental · pdf filechapter 3: microbial...
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MASTER 2 - PROFESSIONAL
University of Toulouse III - Paul Sabatier
Processes Engineering - Specialization of Electrochemical Processes
2011-2012
Evaluation of Unified Numerical and
Experimental Methods for Improving
Microbial Electrochemical Technologies
(MXCs)
E. EKİN DALAK
Promotors:
Xochitl Dominguez (VITO)
Karolien Vanbroekhoven (VITO)
()5°(VITO)
Academic Advisors:
Peter Winterton (UPS)
Theo Tzedakis (UPS)
2
TABLE OF CONTENT
TABLE OF CONTENT ........................................................................................................... 2
LIST OF FIGURES .................................................................................................................. 5
LIST OF TABLES .................................................................................................................... 8
GLOSSARY .............................................................................................................................. 9
ACKNOWLEDGEMENTS ................................................................................................... 11
ABSTRACT ............................................................................................................................ 12
OBJECTIVES......................................................................................................................... 13
CHAPTER 1: INTRODUCTION ......................................................................................... 14
CHAPTER 2: DESCRIPTION OF THE INSTITUTE ....................................................... 15
2.1 PROFILE ............................................................................................................................. 15
2.2 ACTIVITIES ........................................................................................................................ 15
2.3 RESEARCH FIELDS ............................................................................................................ 16
2.4 ELECTROCHEMISTRY AT VITO ...................................................................................... 17
CHAPTER 3: MICROBIAL ELECTROCHEMICAL SYSTEMS (MXCS) ..................... 18
3.1 TYPES OF MXCS ............................................................................................................... 19
3.1.1 MICROBIAL FUEL CELL (MFC) .................................................................................. 19
3.1.2 MICROBIAL ELECTROLYSIS CELL (MEC) ................................................................. 20
3.1.3 MICROBIAL ELECTROSYNTHESIS (MES) ................................................................... 21
3.2 ELECTRON TRANSFER MECHANISMS ............................................................................. 21
3.2.1 DIRECT ELECTRON TRANSFER (DET) ........................................................................ 21
3.2.2 MEDIATED ELECTRON TRANSFER (MET) .................................................................. 22
3.3 PERFORMANCE PARAMETERS ......................................................................................... 23
3.3.1 ENERGY GENERATION .................................................................................................. 23
3.3.2 TREATMENT EFFICIENCY ............................................................................................. 25
3.4 MXC DESIGNS .................................................................................................................. 26
3.4.1 REACTOR CONFIGURATIONS ........................................................................................ 26
3
3.4.3 FUEL TYPES ................................................................................................................... 29
3.4.4 MICROBE TYPES ............................................................................................................ 30
3.4.5 OPERATIONAL CONDITIONS ......................................................................................... 30
3.5 ELECTROCHEMICAL CHARACTERIZATION TECHNIQUES ............................................ 31
3.5.1 OPEN CIRCUIT VOLTAGE ............................................................................................. 32
3.5.2 CYCLIC VOLTAMMETRY (CV) ..................................................................................... 32
3.5.3 CHRONOAMPEROMETRY (CA) ..................................................................................... 33
3.5.4 ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY (EIS) .......................................... 33
CHAPTER 4: DESIGN AND OPTIMIZATION OF MFCS VIA MODELING ............... 35
4.1 MODELING CURRENT AND POTENTIAL DISTRIBUTIONS IN MFC .................................... 36
4.1.1 OVERPOTENTIALS ........................................................................................................... 36
4.1.2 TYPES OF CURRENT AND POTENTIAL DISTRIBUTIONS .................................................. 38
4.2 NUMERICAL MODELING OF MFC VIA COMSOL MULTIPHYSICS .................................. 46
CHAPTER 5: MODELING WORK ..................................................................................... 47
5.1. COMSOL MODELING PROCEDURE ................................................................................ 47
5.2. INITIAL MODEL GEOMETRY, DEFINITIONS AND RESPONSE ........................................... 49
5.3 PRIMARY CURRENT DISTRIBUTION MODELS ................................................................... 51
5.3.1 INFLUENCE OF CURRENT COLLECTOR DESIGN ............................................................. 51
5.3.2 INFLUENCE OF LUG DIMENSIONING AND DESIGN ......................................................... 52
5.3.3 HALF CELL CONFIGURATIONS ....................................................................................... 54
5.3.4 INFLUENCE OF LUG POSITIONING ON DIFFERENT CURRENT COLLECTOR DESIGNS ... 55
5.3.5 INFLUENCE OF DIFFERENT PARAMETERS IN SELECTED GEOMETRY ........................... 57
5.3.6 INFLUENCE OF GRID SIZE ............................................................................................... 60
5.4 SECONDARY CURRENT DISTRIBUTION MODELS .............................................................. 62
5.5 SUMMARY AND PERSPECTIVES OF THE MODELING WORK .............................................. 63
CHAPTER 6: EXPERIMENTAL WORK ........................................................................... 66
6.1 MATERIALS AND METHODS ............................................................................................... 66
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6.1.1 MICROBIAL GROWTH ..................................................................................................... 66
6.1.2 ELECTROCHEMICAL CELL COMPONENTS ..................................................................... 67
6.1.3 EXPERIMENTAL SET-UP AND OPERATION ...................................................................... 67
6.1.4 ELECTROCHEMICAL METHODS...................................................................................... 68
6.2 RESULTS AND DISCUSSION ................................................................................................. 69
6.2.1 HALF CELL EXPERIMENTS WITH ACETATE ................................................................... 69
6.2.2 HALF CELL EXPERIMENTS WITH FUMARATE ................................................................ 72
6.2.2.1 GLUCOSE ADDITION AND CHANGE IN POLARIZATION POTENTIAL (50 MV) ............... 72
6.2.2.2 FUMARATE-GLUCOSE COMBINATION AS SUBSTRATE FROM THE INITIAL TIME ......... 74
6.2.2.3 INFLUENCE OF THE BACTERIAL GROWTH IN THE HALF CELL .................................... 75
6.2.2.4 GLUCOSE ADDITION AND CHANGE IN POLARIZATION POTENTIAL (250 MV) ............. 77
6.2.2.5 SEPARATION OF THE MEDIUM CIRCULATION IN COUNTER ELECTRODE AND WORKING
ELECTRODE COMPARTMENTS .................................................................................................. 80
6.2.3 BIOELECTROCHEMICAL KINETICS FOR SECONDARY CURRENT DISTRIBUTION
MODELS ................................................................................................................................... 82
6.4 SUMMARY AND PERSPECTIVES OF THE EXPERIMENTAL WORK ...................................... 83
CHAPTER 7: CONCLUSION AND PERSPECTIVES ...................................................... 85
REFERENCES ....................................................................................................................... 86
APPENDIX 1: ELECTRODE KINETICS ................................................................................... 89
APPENDIX 2: NORMALIZATION ............................................................................................ 91
5
LIST OF FIGURES
Figure 2.1: Organization chart of VITO
Figure 3.1: Schematic diagram of a typical MFC
Figure 3.2: Schematic diagram of a typical MEC
Figure 3.3: Schematic view of DET via (A) membrane-bound cytochromes and (B)
conducting nanowire
Figure 3.4: Schematic view of MET via artificial and self-produced mediators
Figure 3.5: Polarization curve (solid line) and power curve (dashed line) of MXCs
Figure 3.6: Schematics of a two-compartment MFC in rectangular shape (left), miniature
shape (right)
Figure 3.7: One-compartment MFC
Figure 3.8: Schematic of cylindrical shape MFC with open-air cathode
Figure 3.9: Schematic of stacked MFC
Figure 3.10: Schematic representation of potentiostatic regulation for three-electrode
setup
Figure 3.11: Typical CV for an MXC
Figure 3.12: Typical CA response for an MXC
Figure 4.1: Potential losses (overpotentials) over the polarization curve of MFC
Figure 4.2: Two parallel plate electrodes opposite to each other in the walls of an insulating
flow channel (solid curves: current lines, dashed lines: equipotential surfaces)
Figure 4.3: Primary potential distribution over parallel plate electrode (A: ∆V=Eeq and B:
∆V>Eeq)
Figure 4.4: Secondary distribution over parallel plate electrodes
Figure 4.5: Concentration profile at electrode –electrolyte interface
Figure 5.1: Initial model geometry (full cell configuration with porous electrodes and grid
current collector)
Figure 5.2: Primary current distribution profile over the initial model geometry as output
image
Figure 5.3: Primary current distribution profiles with plate (left) and grid (right) current
collector designs
Figure 5.4: Primary current distribution profiles with different lug widths (W_lug)
6
Figure 5.5: Primary current distribution profiles with different lug heights (H_lug)
Figure 5.6: Primary current distribution profiles with plate (left) and grid (right) lug design
Figure 5.7: Primary current distribution profiles for half-cell configurations with plate (left)
and (grid) current collector
Figure 5.8: Four geometry configurations with different lug and cc designs
Figure 5.9: Primary current distribution profiles over the four geometry
Figure 5.10: Normalized value of jmax vs normalized value of parameter
Figure 5.11: Image of a mesh current collector made of welded wires
Figure 5.12: Primary current distribution profiles over the reference model and decreased
W_peld
Figure 5.13: Primary current distribution profiles over the reference model and increased
H_peld
Figure 5.14: Secondary current distribution profiles over the outer boundary (left) and inner
surface (right) of the electrode
Figure 6.1: Schematic view of the MXC half cell in recycled flow batch mode
Figure 6.1: Schematic view of the MXC half cell in recycled flow batch mode
Figure 6.2: Shematic view of the determination of Eanapp
and Ecatapp
for acetate oxidation and
fumarate reduction during CA
Figure 6.3: CA – half cell experiment with acetate
Figure 6.4: CV after inoculation with acetate at initial time (t=0) of CA
Figure 6.5: CV after glucose addition at t=12 d of CA
Figure 6.6: CA – 1st set of half cell experiments with fumarate
Figure 6.7: CV after inoculation with fumarate at initial time (t=0) of CA
Figure 6.8: CV after glucose addition at t=16 d of CA
Figure 6.9: CA - 2nd
set of half cell experiments with fumarate
Figure 6.10: CV after inoculation with fumarate and glucose at t=7 d (left) and t=11 d (right)
of CA
Figure 6.11: CA - 3rd
set of half cell experiments with fumarate
Figure 6.12: CV after inoculation with fumarate and glucose at initial time (t=0) of CA
Figure 6.13: CV after inoculation with fumarate and glucose at t=4 d of CA
Figure 6.14: CA - 4th
set of half cell experiments with fumarate
7
Figure 6.15: CV after fumarate inoculation at initial time (t=0) of CA
Figure 6.16: CV after glucose inoculation at t=16 d of CA
Figure 6.17: CV at lowest scan rate (1 mV/s) after glucose inoculation at t=16 d of CA
Figure 6.18: CA -5th
set of half cell experiments with fumarate
Figure 6.19: CV after glucose inoculation at t=14 d of CA
Figure 6.20: CV at lowest scan rate (1 mV/s) after glucose inoculation at t=14 d of CA
Figure 6.21: Bioelectrochemical kinetics for secondary current distribution model: I vs E
after achieving the jmax=7.32 a/m² at the 5th
set of experiment
8
LIST OF TABLES
Table 4.1: Hypotheses and parameters for each type of current and potential distribution
Table 5.1: Geometry and material parameters of the reference model
Table 5.2: Variation of the parameters and jmax
Table 6.1: Components of 1 L NBAF medium
9
GLOSSARY
*Listed in appearance order
*Abbreviations
BES Bioelectrochemical Systems
SCT Separation and Conversion Technology
MXC Microbial Electrochemical System
MFC Microbial Fuel Cell
MEC Microbial Electrolysis Cell
MES Microbial Electrosynthesis
PEM Proton exchange membrane
DET Direct electron transfer
MET Mediated electron transfer
OCV Open Circuit Voltage
BOD Biologic oxygen demand
COD Chemical oxygen demand
TOD Total oxygen demand
CE Coulombic efficiency
EE Energy efficiency
CA Chronoamperometry
CV Cyclic voltammetry
EIS Electrochemical impedance spectroscopy
WE Working electrode
RE Reference electrode
CE Counter electrode
FRA Frequency response analyzer
cc Current collector
EET Extracellular electron transfer
NBAF Nutrient Broth Acetate Fumarate
SHE Standard Hydrogen Electrode
AC Activated carbon
SS Stainless steel
*Notations and Symbols
S Substrate
E Potential
I Current
R Resistance
Pd Power density
A Area
Pv Volumetric power
V Volume
Yx/s Growth yield
t Time
η Overpotential
Φ potential gradient
j Current density
x Distance from the center of the electrode
L Length of the electrode
K Complete elliptic integral
k Conductivity of the electrolyte
j0 Exchange current density
α Charge transfer coefficient
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n Number of electron exchanged
R Universal gas constant
T Temperature
F Faraday’s constant
y y-Coordinate
N Flux
z Oxidation state
c Concentration
D Diffusion coefficient
A Acetate
M Mediator
G Glucose
F Fumarate
*Indices
cell Electrochemical cell
ext External
max Maximum
int Internal
eq Thermodynamic equilibrium
ohm Ohmic
act Activation
conc Concentration
preced Preceding
n Normal component
avg Average
M Metal
S Surface
an, A Anode
cat, C Cathode
ox Oxidation
red Reduction
i Specie of i
min Minimum
app Applied
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ACKNOWLEDGEMENTS
I would like to express my heartfelt gratitude to my promoter, Xochitl Dominguez-Benetton
for her continuous support and guidance to make possible this work, but especially for her
great kindness and always having her office door open whenever I need. Thank you for
enforcing me to do my best in the most professional way, thus showing me how to do
research. I also would like to acknowledge my co-promoters, Deepak Pant and Karolien
Vanbroekhoven for making me part of their team since the first moment of this internship and
for their insightful comments on my work. I am thankful to all for giving me the opportunity
of working in such a great environment but also continuing to my future studies in their
group.
I am thankful to my academic advisor, Peter Winterton for his motivation, precious help, and
valuable contribution to this study. I also would like to express my deepest gratitude to my
professor Theo Tzedakis for holding his students at high educational level and letting me part
of this program. They are an outstanding example of how to be a successful professional but
most importantly, how to be a kind and caring person.
I thank to my co-workers whom I share the office, laboratory and life in this small town for
their dear friendships throughout my stay in Belgium. I also thank to all my classmates, for
their kindness, especially, for their effort to understand my French.
I would also like to thank to the people who are in Turkey; my family and friends, for their
support and encouragement in every step of this challenging period my life where I change
three countries in two years. My special thanks to Omar, for being such an amazing person
and being in my life.
.
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ABSTRACT
Microbial Electrochemical Systems (MXCs) were evaluated through numerical computational
modeling by using COMSOL Multiphysics. 3-D models based on primary current distribution
displayed that usage of grid current collector in Microbial Fuel Cells (MFCs) increases the
distribution profile at the electrolyte-electrode interface. The key parameters affecting the
system performance were determined lug positioning, grid size in this study. The secondary
current distribution models with bioelectrochemical kinetics obtained from experimental work
showed a more homogenous distribution profile for the same than the primary current
distributions models. The maximal current density solutions of the secondary current
distribution models showed accuracy with the experimental results.
While bioelectrochemical kinetics was achieved, the electrochemical performances of X
strains were monitored. X strains proved their ability of mediated electron transfer (MET) in
a high conductive medium (145 mS/cm) by achieving 7.32 A/m² of maximal current density
in the half-cell MFC containing fumarate and glucose mixture as substrate and AQDS as
mediator.
13
OBJECTIVES
The present document is a product of five months of research work that has been conducting
at VITO (Mol, Belgium) and it was written in order to obtain the degree of Masters in Process
Engineering – Electrochemical Processes of University of Toulouse III – Paul Sabatier.
The overall aim of this study is to investigate the microbial electrochemical systems (MXCs)
in order to gain the knowledge needed for the improvement of these systems, through a
unified approach which combines computational modeling and experimental methods.
This principal target is translated in the following sub-goals;
1. To evaluate current distributions at the electrode-electrolyte interfaces in fuel cells,
through developing numerical computational models in COMSOL
2. To evaluate current distributions at the electrode-electrolyte interfaces in microbial
fuels cells, through experimental electrochemically-active microbial kinetics and
numerical computational models in COMSOL.
3. To construct models for defined electrochemical reactor geometries to solve the
primary distributions at half cell and full cell level.
4. To construct models to solve the secondary distributions at half cell and full cell level,
for the system previously defined in primary current distribution cases.
5. To evaluate different operational parameters in the constructed models in order to
improve electrochemical cell components and assemblies in microbial fuel cells.
6. To analyze the experimental electrochemical response of electrochemically active
biofilms in order to obtain bioelectrochemical kinetics for constructed models.
Furthermore, examination of the electrochemical responses of the chosen bacteria in the
context of microbial electrosynthesis (MES) is considered as a secondary target of this study.
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CHAPTER 1: INTRODUCTION
Recent petroleum shortage and global energy crisis have triggered researches towards
sustainable production. Microbial electrochemical systems (MXCs) have emerged as a
sustainable and innovative way of energy production and chemical synthesis with the ability
of electron transfer between microbes and electrodes, while decreasing the energy needs for the
process. Today, such technologies have gained great world-wide attention due to their high
conversion, selectivity, efficiency and promising potential in wide range of applicability.
Many studies have focused on the field of MXCs and the primary target thereof has become
to increase the performances of MXCs towards scale-up and developing cost-efficient
designs. Despite the great advances, MXCs are still not well understood, since they are rather
complex systems involving many disciplines; such as microbiology, engineering,
electrochemistry.
Therefore, efforts should be given from different aspects and collective approaches should be
considered, such as combining numerical methods with experimental studies for a better
understanding in terms of increasing the performances of such complex systems.
This is why this study approached in a versatile way to MXCs. After a brief description of brief
description of institute where the current study takes place (Chapter 2); Chapter 3 and 4 are
dedicated to provide the state-of-the-art of MXCs and designing and optimization of these
systems through modeling approaches respectively. Chapter 5 introduces the modeling work
performed, whereas Chapter 6 explains the experimental work conducted in parallel. Chapter 7
presents the overall conclusion and the perspectives of the current work.
15
CHAPTER 2: DESCRIPTION OF THE INSTITUTE
2.1 Profile
VITO - Flemish Institute for Technological Research is an autonomous public research
company. This independent and customer-oriented research institute was founded in 1991
with the purpose of providing innovative technological solutions as well as scientifically
based advice and support in order to stimulate the sustainable development of Flanders
society.
VITO is located in Mol, Antwerp and today, it is one of the largest Belgian research institutes
with approximately 700 researchers of 15 different nationalities plus supporting staff.
2.2 Activities
VITO works with companies, governments, universities and other research institutions, both
in Belgium and abroad. Therefore, VITO activities can be summarized as being based on 3
main objectives:
i. Innovation for industry
Companies tend to combine environmental and company profits to find their way to eco-
efficiency by re-evaluating their product design, optimizing their processes, and re-using their
waste products. VITO supports them in all these domains by developing important
innovations, and follows up on all international innovations with customer-driven basic
projects.
ii. Technological pillar for governments
As a research partner for local, Flemish, and European governments; VITO delivers the
necessary scientific input for government decision takers to build new policies in an efficient
and goal-oriented way. VITO also develops barometers that measure the results of a policy
and that quickly reveal where adjustments are necessary.
iii. International scientific research
VITO works with universities and other research institutions that lead to various common
international research programs, publications and communications at international
conferences and symposia. It also plays an active part in Europe such as in the Framework
16
Programs of the European Union. Besides all this, it carries out its own strategic research in
different technological fields. Presently VITO is working strategically in China, India and
Vietnam.
2.3 Research Fields
Research at VITO is centered on three main societal challenges: transition towards a society
less dependent on fossil fuels, transition towards a more sustainable industry in Flanders, and
improved quality of life by better use of the environment.
With the aim of answering the needs of the development of these challenges, different
research topics that are currently investigated at VITO can be listed as;
o Earth observation
o Environmental modeling
o Transition energy and environment
o Energy technology
o Materials technology
o Environmental risk and health
o Separation and conversion technology
In Figure 2.1, the departments dedicated to conducting these research fields can be seen
together with the main organizational structure of VITO.
Figure 2.1: Organization chart of VITO
17
2.4 Electrochemistry at VITO
Electrochemical engineering and electrochemistry studies have a very important role at VITO
and they are currently carried out for various types of material developments (separators,
electrodes, and membrane-electrode assemblies), electrochemical cell designs as well as their
diverse applications (wastewater treatment in microbial fuel cells, bio-electrosynthesis, bio-
electrolysis processes).
The current report is a product of a performed work in a research group which investigates
Bioelectrochemical Sytems (BES) in a versatile way, from different educational backgrounds
and scientific experiences such as electrochemistry, materials science and engineering,
biotechnology, microbiology and process engineering.
This BES-research group is located under the ‘Separation and Conversion Technology (SCT)
Unit’ that belongs to the ‘Industrial Innovation Department’ as can be seen from organization
chart of VITO above.
The following chapters (Chapter 3 and 4) will help to provide a better understanding of some
of the studies of this BES-research group by presenting the state of the art.
18
CHAPTER 3: MICROBIAL ELECTROCHEMICAL
SYSTEMs (MXCs)
Bioelectrochemical Systems (BESs) consist of devices in which electrochemically active
biological components are used as catalysts for electrochemical reactions occurring at
electrodes (Pant et al., 2012). BESs are generally classified depending on the type of
biocatalyst which can be enzymes or microbes; however, focus here is on Microbial
Electrochemical Systems (MXCs) since enzymatic systems are out of the scope of the
present work.
The MXC concept was first proposed by Potter in 1910 based on the idea that microbial
catalytic activities and conversions could generate electrical current (Potter, 1910). This
is the first description of the Microbial Fuel Cell (MFC); however, MFCs did not gain
much attention until the 1980s when it was found that energy generation can be enhanced
by the addition of electrochemically active mediators that accelerate electron transfer in
the system. Although mediated electron transfer highly increased the attractiveness of
MFCs, toxicity and instability of most of the mediator species limited their applications
and the real breakthrough was made at the beginning of 2000 when certain particular
bacteria were found to be capable of direct (mediator-less) electron transfer which opens
new pathways for MFC technologies (Du et al., 2007). Since then, MFCs have received
world-wide attention for alternative energy production, wastewater treatment and fuel
recovery from organic waste (Pant et al., 2012).
In 2005, a new technology was developed for hydrogen generation; named the Microbial
Electrolysis Cell (MEC). The same potential benefits of using MFCs to generate
electricity from wastewater treatment were applied for electricity-driven hydrogen
generation within MECs (Liu et al., 2005b). More recently, in 2010, Microbial
Electrosynthesis (MES) was described for bioproduction of valuable chemicals, a
principle that was proven when electricity-driven acetate production was achieved from
CO2 reduction (Nevin et al., 2010).
These three systems now are considered as presenting highly sustainable and innovative
approaches for energy production, fuel recovery and chemical synthesis (Dominguez et al.,
19
2012). Nowadays, numerous studies are performed on MXC technologies and innovative
designs have emerged along with newer concepts in MXC applications (Pant et al., 2012);
(Watanabe, 2008). In this way, Chapter 3 introduces the fundamentals of Microbial
Electrochemical Systems (MXCs) in order to provide a broad perspective on these novel
technologies.
3.1 Types of MXCs
3.1.1 Microbial Fuel Cell (MFC)
Microbial Fuel Cells (MFCs) are devices that use bacteria as catalysts to oxidize organic
substrates and generate electrical current (Logan et al., 2006). For instance, bacteria in
the anodic chamber oxidize the added substrates (S) and release electrons (e-) towards an
anode, as well as protons (H+). Carbon dioxide (CO2) is produced as an oxidation
product. The electrons released are transported from the anode (A) to the cathode (C)
through an external circuit which generates electricity. After passing the proton exchange
membrane (PEM), the protons enter the cathodic chamber where oxygen (O2) reduction
takes place and water (H2O) is formed (See Figure 3.1).
Figure 3.1: Schematic diagram of a typical MFC
Typical electrode reactions of MFC are shown below using acetate (CH3COO-) as a
model substrate:
20
Anodic reaction: Acetate oxidation
CH3COO- + 2H2O ⟶ 2CO2 + 7H
+ + 8e
-
Cathodic reaction: Oxygen reduction
O2 + 4e- + 4H
+ ⟶ 2H2O
MFC operates as a galvanic cell in which the anodic potential (Ean) is lower than the
cathodic potential (Ecat). Cell reactions occur spontaneously and as a result; electrical
current is generated.
3.1.2 Microbial Electrolysis Cell (MEC)
Microbial Electrolysis Cells (MECs) function with almost the same mechanism as MFCs;
however, instead of gaining energy from the system, additional energy needs to be
applied in order to drive the electrochemical reactions. When separate reactions
occurring at the anode (A) and the cathode (C) are analyzed in a MXC, it is observed that
the anodic potential (Ean) is higher than the cathodic potential (Ecat) implying that the
MFC operates as an electrolytic cell (Dominguez et al., 2012).
Figure 3.2: Schematic diagram of a typical MEC
In case of hydrogen (H2) production in a MEC, an organic substrate (S), e.g. acetate, is
oxidized by electrochemically-active (EA) bacteria in the anodic chamber and carbon
dioxide (CO2) is produced as an oxidation product. The electrons released (e-) are
transferred to the cathodic chamber. The protons enter the cathodic chamber after passing
21
the proton exchange membrane (PEM). On the cathode, proton (H+) reduction takes place
and hydrogen (H2) is produced (See Figure 3.2).
3.1.3 Microbial Electrosynthesis (MES)
Microbial Electrosynthesis (MES) has evolved from the concept of MEC. In MES, bacteria
still oxidize the added substrates at the anode and released electrons are transported from
the anode to the cathode where oxidized species are reduced into value-added products.
For such production, MESs can follow at least three different approaches:
reduction of CO2 to an organic product (e.g. acetate)
reduction of an organic substrate to a desired product (e.g. fumarate to succinate or
glucose to butanol)
oxidation of an organic substrate to a desired product (e.g. glycerol to ethanol)
(Dominguez et al., 2012)
Today, the idea of producing fuels and chemicals from CO2 or waste organics is one of the
strongest driving forces for MXC studies. With these empirical studies as well as the
increased knowledge about electron transfer mechanism gained over the past few years MES
has a great potential to become a key process in future bioproduction (Rabaey and Rozendal,
2010).
3.2 Electron Transfer Mechanisms
Electron transfer from microbes to electrodes (or vice versa) is the process that links
microbiology and electrochemistry in MXCs; this is why determination of the
mechanisms of electron transfer is regarded as the key issue for elucidating overall
system behavior. So far, two main possible mechanisms have been described: direct
electron transfer (DET) and mediated electron transfer (MET).
3.2.1 Direct Electron Transfer (DET)
Direct electron transfer mechanism (DET) occurs via physical contact between the
electrode and bacterial cell components or membranes without involvement of dissolved
redox species. DET requires the bacteria to possess organelle- or membrane-bound redox
proteins; e.g. cytochromes to carry out the electron transport between the bacterial cell
and the electrode.
22
Metal reducing bacteria often need solid terminal electron acceptors like Fe(III) oxides in
their natural environment and in the case of MXCs, the anode plays the role of the solid
electron acceptor. Most research concerning direct electron transfer has focused on these
metal reducing bacteria, specifically, Geobacter and Shewanella (Schröder, 2007).
Recently, it has been reported that Geobacter sulfurreducens and Shewanella oneidensis
can evolve electrochemically conducting molecular pili (nanowires) which maintain a
conductive path between the cytochromes present in their outer cell membrane and the
electrode. This allows microorganisms to reach and utilize more distant solid electron
acceptors (Figure 3.3). The formation of such nanowires may allow the development of
thick biofilms and thus higher anode performances (Schröder, 2007).
Figure 3.3: Schematic view of DET via (A) membrane-bound cytochromes and (B)
conducting nanowire (Schröder, 2007)
3.2.2 Mediated Electron Transfer (MET)
Microbes can use redox active molecules that ‘shuttle’ electrons from bacteria to the
electrode surfaces. These conductive molecules, so called mediators, can be artificial or
self–produced by the bacteria (Figure 3.4). The most common artificial mediators are
neutral red, thionin, methyl viologen or anthraquinones (Rabaey and Rozendal, 2010).
The addition of the mediators increases the performance in terms of current generation
since they facilitate electron transfer, but when it comes to selectivity, toxicity and
limited stability DET can be considered as a great advantage over MET (Rabaey and
Rozendal, 2010); Schröder, 2007).
23
Figure 3.4: Schematic view of MET via artificial and self-produced mediators (Schröder,
2007)
3.3 Performance Parameters
MXC performance can be evaluated in many ways, but principally through energy
generation and treatment efficiency. Although the newest application areas have emerged
for MXCs, the analysis of the parameters associated with these two current purposes are
regarded to be well-established approaches in terms of evaluating the system
performance.
3.3.1 Energy Generation
Theoretical and Actual Cell Potential
The theoretical cell potential or electromotive force (emf) of the overall reactions occurring in
an MXC is defined as the potential difference between the cathode and the anode. The emf
refers to the best possible cell potential which is the maximum cell potential that can be
attained in a MFC and the minimum potential required to drive it. However, the actual cell
potential (Ecell) is lower than this theoretical value because of irreversible energy losses, the so
called overpotential (η).
Ecell = emf – η
Efficient MXC designs therefore need to focus on reducing overpotentials as much as
possible in order to optimize system performance. Different types of overpotentials occurring
in a MXC due to different phenomena are largely described in Chapter 4.
24
Power Output
Power can be regarded as the most significant output to be evaluated in terms of electricity
generation. Direct electrical power measurement is done through measuring the cell potential
(Ecell; V) and the current (I; A) at a fixed external resistance (Rext; Ω). Thus, power (P; W) is
calculated as;
P =IEcell
Power output can be attained in different forms. For an objective evaluation, direct power
measurement is often normalized to the electrode surface area (A; m2) which makes possible
comparison between different systems. This normalized value is called the power density (Pd;
W.m–2
) and can be calculated as:
Pd = Ecell2 /ARext
When power output is needed to be normalized to the electrode volume (V; m3), generally
with the purpose of facilitating the calculations of reactor size or costing, it is called
volumetric power (Pv; W.m–3
) and it is calculated as:
Pv = Ecell2 / VRext
Polarization Curve
Polarization and power curves are functional methods to calculate the maximum power (Pmax)
that can be attained in MXCs as well as Rint (Ω) and OCV (V) magnitudes. Rint (Ω) is the
internal resistance of the system and OCV (V) is the open circuit voltage. OCV is defined as
the measured cell potential after some time in the absence of current.
A polarization curve illustrates the cell potential (Ecell) as a function of current (I); it is plotted
by measuring currents at different potentials. Polarization curves provide power curves, which
plot power (P) versus current (I) (Figure 3.5).
25
Figure 3.5: Polarization curve (solid line) and power curve (dashed line) of MXCs
(Watanabe, 2008)
As seen in the figure above, the relationship between Ecell and I is expressed as:
Ecell = OCV – IRint
3.3.2 Treatment Efficiency
When MXCs are applied for waste treatment; they are evaluated in terms of treatment
efficiency (%) which can be calculated through removal of the biological oxygen demand
(BOD; kg), chemical oxygen demand (COD; kg) or total organic carbon (TOC; kg).
COD is the most common measurement for treatment efficiency which is referred to as
COD-removal efficiency, in most cases. It is an indicator of fuel conversion either into
current or biomass, by showing the ratio between the removed and influent organics.
In addition to COD-removal efficiency, treatment efficiency can be evaluated trough
other parameters which can be listed as:
Coulombic efficiency (CE, %) is the ratio of electrons recovered as current to the
maximum number of electrons contained in the fuel.
Loading rate (kg.m–3
.d–1
) indicates the rate at which COD that is loaded into a
MXC. It is measured by normalizing the amount of COD loaded into the electrode
volume (m3) and time (d).
Growth yield (Yx/s) is an index that shows substrate utilization as the electrons are
converted into biomass. It is found by normalizing the amount of COD produced
to time (d) (Logan et al., 2006).
26
Energy efficiency (EE %) is calculated as the ratio of power produced to the heat
energy obtained by combustion of the substrates added. It is the most significant
parameter to evaluate the MXCs in terms of energy recovery processes
(Watanabe, 2008).
All together, these parameters provide an accurate characterization of the waste or wastewater
treatment that can be correlated to the electrochemically active biomass at the electrode
surface, as well as to the active biomass with no electrochemical contributions to the process
(Dominguez et al., 2012).
Nevertheless, comparisons of these performance data in terms of both energy generation and
treatment efficiency are difficult since every study refers to a specific combination of reactor
volume, membrane, organic load, and bacteria.
In addition to energy generation and treatment efficiency, the relative amount of product
formation from a substrate with the specific microbial community is started to be regarded as
a key performance parameter along with the concept of electricity-driven biosynthesis,
particularly for MES studies.
3.4 MXC Designs
In order to increase the performance parameters described above, efficient MXC designs are
required. In recent years, various studies have been carried out in this direction by using
different reactor, material, microbe and fuel configurations under different operating
conditions. Even though the studies reported offer valuable knowledge, it is important to note
that the microbiological or electrochemical optimizations for one type of MXC are not always
optimal for another type. This is why MXCs should be considered as complex systems, and
when evaluating a single part, the effect of all other parts of the system should also be taken
into account (Dominguez et al., 2012); (Watanabe, 2008).
3.4.1 Reactor Configurations
Reactors have been constructed with various configurations in order to obtain better MXC
performances, in other words, to minimize the potential losses. This effort involves a number
of modifications, mostly concerning the system geometry. For example, placing the electrodes
within a shorter distance from each other is one of key factors that minimize the ohmic drop
27
in electrochemical systems and consequently MXCs, since the resistance is proportional to
distance (Logan et al., 2006).
A typical double-chamber (two-compartment) reactor consists of an anodic and a cathodic
chamber separated by a proton exchange membrane (PEM) as they are illustrated in the
Figure 3.1 and 3.2 before. They typically run in batch mode, mostly, in laboratories. They can
be in various shapes such as rectangular or miniature as it can be seen in Figure 3.6.
Figure 3.6: Schematics of a two-compartment MFC in rectangular shape (left), miniature
shape (right)(Du et al., 2007)
Due to their complex designs, double-chamber MFCs are difficult to scale-up even though
they can be operated in either batch or continuous mode. Single-chamber (one-compartment)
reactors (Figure 3.4) offer simpler designs and cost savings. They typically possess only an
anodic chamber without the requirement of aeration in a cathodic chamber (Du et al., 2007).
Figure 3.7: One-compartment MFC (Watanabe, 2008)
Park and Zeikus designed a one-compartment MFC consisting of an anode in a rectangular
anode chamber coupled with a porous air-cathode that is exposed directly to the air as shown
in Figure 3.7 (Park and Zeikus, 2003).
28
Liu and Logan also designed an MFC consisting of an anode placed inside a plastic
cylindrical chamber and a cathode placed outside which can be seen in Figure 3.8 (Liu and
Logan, 2004).
Figure 3.8: Schematic of cylindrical shape MFC with open-air cathode (Du et al., 2007)
A stacked MFC is shown in Figure 3.9 for the investigation of performances of several MFCs
connected in series and in parallel. Enhanced voltage or current output can be achieved by
connecting several MFCs in series or in parallel (Aelterman et al., 2006).
Figure 3.9: Schematic of stacked MFC (Du et al., 2007)
3.4.2 Materials
The material of the electrodes has a direct influence on cell performance since different
activation polarization losses are observed with different types of materials due to their
intrinsic characteristics. Undesired high activation polarization losses can be avoided by
increasing the electrode quality.
Carbon-based materials (e.g., activated charcoal, carbon cloth or graphite felt) are generally
used to construct electrodes owing to their large surface areas. Platinum (Pt) and Pt-black
29
electrodes generally perform better than carbon-based electrodes and good catalysts when it
comes to oxygen reduction. However, their high costs make practical applications prohibitive.
Several studies have proved that modifications of carbon-based substrates result in better
performance. Schröder et al. showed that higher currents can be gained with platinized-carbon
cloth, compared to unmodified carbon cloth under the same operating conditions (Schröder et
al., 2003). Park and Zeikus reported that the incorporation of the metal ions (Mn4+
and Fe3+
)
and neutral red as mediators at the anode level enhances electron transfer and results in
greater power generation (Park and Zeikus, 2000).
An efficient ion transfer system, and more particularly proton-exchanger, also increases the
performance in MXCs by reducing the internal resistance and concentration polarization
losses. Proton-transfer efficiency depends on the type of the proton exchange membrane
(PEM). The most common PEM is Nafion with its high proton selectivity. However, owing to
the transport of other cation species (Na+, K
+, NH4
+, Ca
2+, Mg
2+) within Nafion the need
arises for a PEM which has better selectivity for the protons and not for the other cations
(Rozendal et al., 2006). Besides, Nafion membranes are costly; Ultrex membranes and Zirfon
have been provided as useful alternatives for achieving suitable ion-exchange at more
affordable prices (Dominguez et al., 2012).
Oh and Logan showed that the ratio of PEM surface area to system volume is another limiting
factor, as well as type of the material for power generation. They reported that internal
resistance decreases with increases of PEM surface area in MFCs (Oh and Logan, 2006).
In another study, Liu and Logan replaced the PEM with an air-cathode membrane as a
separator, in order to boost gas transfer between the compartments of the system. They
showed this approach increases power generation; however, one drawback of this method is
that it can lead to reduced electron recovery (Liu and Logan, 2004); (Rozendal et al., 2008);
(Watanabe, 2008).
3.4.3 Fuel Types
Fuel type and concentration influence MXC performance by changing the cell power density.
The power density differs widely with the different type of fuels with a specific microbial
consortium. Besides a higher fuel concentration gives a higher power density output (Du et
al., 2007).
30
A great variety of substrates can be used in MFCs for electricity production, ranging from
pure compounds to complex mixtures of organic matter present in wastewater. It is difficult,
from the literature; to compare MFC performances due to different operating conditions,
surface areas and types of electrodes and different microorganisms used (Pant et al., 2010b).
3.4.4 Microbe Types
In MXCs electrons are transferred from the organic substrate to the anode through the
microbial respiratory chain that depends on the type of microbes involved. A microbial
community consists of a mixed culture usually shows better performance than a pure culture,
due to their broader substrate specificity which allows wider substrate utilization (Rabaey and
Verstraete, 2005).
The selection of a suitable microbial consortium for a given MXC performance is extremely
important but difficult to achieve, since microbial structure and activity depend on the
operating conditions selected for a particular situation. This is another reason for preferring
mixed cultures over pure cultures, due to the different adaptation behaviors of microbes.
However, microbial succession in MXCs is still under early studies and its effects on the
performance of these systems is still unclear, especially for non-short-term batch experiments
(Watanabe, 2008).
3.4.5 Operational Conditions
As mentioned above, operational conditions modify microbial activities in MXCs. In addition
to that, they also play a significant role on the electrochemical kinetics and transport
phenomena in the cell. Numerous operating conditions such as pH, oxidation/reduction
potential, ionic strength, and temperature can be counted as factors strongly affecting
performance (Liu et al., 2005a).
pH differences between the anodic and cathodic chambers have critical impacts on the driving
force of proton exchange through diffusion. A different example concerns ionic strength. Liu
et al. found that addition of NaCl to MFC improved the power generation by increasing the
conductivity. The use of buffers or weak acids has also shown to improve MXC performance
under particular operational conditions (Dominguez et al., 2012; (Liu et al., 2005a).
31
3.5 Electrochemical Characterization Techniques
Characterization is a very important issue to elucidate the performance and properties of
complex systems like MXC; thereby, the system efficiency can improve for existing and
developing designs. Various types of characterization techniques can be applied from
different disciplines for this purpose. For example, microbial characterization can identify the
microbial composition at the electrode and the ones suspended in the electrolyte while
microscopic characterization can be applied for morphological determination of electrode
surfaces, and more importantly biofilm structures.
Electron transfer between microbes and electrodes allows characterizing MXCs by
potentiostatic techniques which perturb the system with potential and measure the current as
output; such as open circuit voltage (OCV) measurements, chronoamperometry (CA) , and
cyclic voltammetry (CV) ; more recently electrochemical impedance spectroscopy (EIS).
In order to apply these techniques a potentiostat is required which operates typically in three-
electrode-setups, consisting of a working electrode (WE, anode or cathode), a reference
electrode (RE), and a counter electrode (CE) (Figure 3.10).
Figure 3.10: Schematic representation of potentiostatic regulation for
three-electrode setup
More advanced measurements can be done when the potentiostat is equipped with a
frequency response analyzer (FRA), allowing electrochemical impedance spectroscopy
measurements (EIS).
32
3.5.1 Open Circuit Voltage
Open circuit voltage (OCV) is measured by means of the potential difference between the
working electrode and a reference electrode in the absence of current. The application of
this technique is simple. The disadvantage of OCV measurements is the possible over-
interpretation of results. The technique is recommended to be used along with other
electrochemical techniques to determine the cathodic and anodic influences of the
electrochemical processes since with OCV the separate contributions are monitored
together.
3.5.2 Cyclic Voltammetry (CV)
Cyclic voltammetry (CV) is an electrochemical technique in which current (I) is recorded
through a working electrode, while the applied potential (E) to the electrode is controlled
as a linear function of time (t). In CV experiments, the potential is applied reversibly at a
certain scan rate (Figure 3.11).
Figure 3.11: Typical CV for an MXC
This is a widely applied technique, particularly in the systems where complex electrode
reactions take place (Bard and Faulkner, 2001). In MXCs, the ability to determine the
standard redox potential of electrochemically active elements of the system gives a broad
understanding of the microbial activities and electrode performances (Logan et al., 2006).
33
3.5.3 Chronoamperometry (CA)
Chronoamperometry (CA) is an electrochemical technique in which the potential of the
working electrode is stepped and the resulting current occurring at the electrode is
monitored as a function of time (Figure 3.12).
Figure 3.12: Typical CA response for an MXC
CA generates high charging currents which decay exponentially with time. Since the
current is integrated over a relatively long time, CA gives a better signal in comparison
to other techniques for MXCs. CA also allows studying the microbial capabilities for
electron transfer as well as determination of the optimal conditions for the system
(Dominguez et al., in press).
3.5.4 Electrochemical Impedance Spectroscopy (EIS)
In addition to the conventional techniques mentioned above, electrochemical impedance
spectroscopy (EIS) has started to gain attention for profound MXC analyses (Strik et al.,
2008).
In EIS, the frequency response of an electrochemical system to an alternate signal is
analyzed in a transfer function, between an input signal (e.g. voltage) and an output one
(e.g. current) through a frequency-response analyzer (FRA).
EIS can be used to measure the ohmic and internal resistance of MXCs as well as to
provide additional insight into the operation of an MFC. The interpretation of EIS data
can be rather complex. This rather sophisticated EIS method can provide superior and
additional information about the system (Logan et al., 2006).
34
The four aforementioned methods are also applied during this research and their analyses
are largely presented in Chapter 6. In addition to electrochemical techniques, modeling is
considered as a powerful method for MXC characterizations. Chapter 4 and 5 are
dedicated to introduce the state-of-the-art and practical applications of MXCs
characterizations by modeling respectively.
35
CHAPTER 4: DESIGN AND OPTIMIZATION OF
MFCs VIA MODELING
The interest in MFC development towards industrial scale has risen sharply in recent years
due to their possibility to yield electricity from organic waste or biomass decomposition;
however, many challenges in terms of increasing system performance still exist. This may be
understood better considering the fact that multiple processes (physical, electrochemical,
chemical, or biological) and phenomena (diffusion, adsorption, or ion migration) are
simultaneously involved; thereby accurate optimizations for systems like MFCs are rather
complex.
Most of the time, experimental studies are useful but not satisfactory for explaining all the
pertinent system parameters; the reason for this is that they focus on either microbiological or
engineering aspects separately. Modeling, and more particularly multi-physics modeling, can
be considered as an appropriate method in order to gather information from several
disciplines for increasing the overall system performance through a multidisciplinary
approach.
The research in this direction for MFCs, coupling microbial with electrochemical dynamics
and kinetics was successfully addressed by Picioreanu et al. in 2007, when they presented a
computational model for biofilm-based MFCs. In their studies, they showed a heterogeneous
current distribution over the electrode surface for young biofilms, but a uniform distribution
in older and more homogeneous biofilms by two-dimensional (2D) and three-dimensional
(3D) model simulations (Picioreanu et al., 2007);(Picioreanu et al.,2008).
Furthermore, a newer modeling approach was developed by Picioreanu et al. to observe the
influence of 2D and 3D biofilm and electrode geometry on the MFC performances by using
very efficient combination of Matlab, Java, and COMSOL Multiphysics (Picioreanu
(Picioreanu et al., 2010). However, such models have appeared from the perspective of
understanding the fundamentals of electrochemically-active biofilm behavior over the
electrodes, rather than from the engineering approach with the purpose of optimizing reactor
geometries, membrane-electrode assemblies, construction and operational conditions.
36
Regarding the fact that the geometry greatly affects system performance, current and potential
distribution is possibly the most crucial point when modeling an electrochemical cell since
there is direct association between them (Yoon et al., 2003). Hence, modeling approaches
based on current and potential distribution can guide to a better industrial prospection for
MFCs.
Although few studies have introduced that approach, electrochemical engineering models of
MFC systems (single-cell and stacks) concerning current and potential distributions are
expected to be one of the significantly developing fields in the very near future, in parallel, of
course, to the progress of MFC studies from the practical engineering point of view.
4.1 Modeling Current and Potential Distributions in MFC
4.1.1 Overpotentials
Overpotentials occurring in an electrochemical system are key parameters to be understood
for modeling based on current and potential distributions since they play a major part in
defining the type of the distribution.
As briefly mentioned in section 3.3.1, overpotential (η) is defined as the potential difference
between the half reaction reduction potential (Eeq) and the potential at which the redox event
is experimentally observed (E) (Bard and Faulkner, 2001). It is directly linked to the
efficiency of any electrochemical system and in case of MFCs overpotential signifies
recovery of less energy than the thermodynamics would predict; in other words, energy
losses.
η = E – Eeq
For MFCs, four major overpotentials are described:
i. Ohmic overpotential (ηohm)
The ohmic overpotential in an MFC includes both the resistance to the flow of electrons
through the electrodes and interconnections, and the resistance to the flow of ions through the
membrane (if present) due to the geometry of the system. Ohmic overpotential can be reduced
by minimizing electrode spacing, using a membrane with a low resistivity, (if practical)
increasing solution conductivity to the maximum tolerated by the bacteria.
37
ii. Activation overpotential (ηact)
Due to the activation energy needed for a redox reaction, activation overpotential occurs
during the transfer of electrons by electrochemical reactions at the electrode surface. It can be
reduced by increasing the electrode surface area, improving electrode catalysis, increasing the
operating temperature, and through the establishment of an enriched biofilm on the electrode.
iii. Concentration overpotential (ηconc)
The concentration overpotential occurs when the rate of mass transport of species to or from
the electrode limits current production. It mainly occur at high current densities due to
diffusion. It also considers bubble formation due to the evolution of gas at the electrode; it
comprises all phenomena that stimulate concentration differences of the charge-carriers
between the bulk solution and the electrode surface (Bard and Faulkner, 2001; Picioreanu et
al., 2007)
iv. Overpotential associated to preceding chemical or biochemical reactions (ηpreced)
Although important, the overpotential associated with preceding chemical or biochemical
reactions is frequently ignored from overpotential considerations. However, this should not be
neglected in the case of microbially-mediated systems, since the involvement of sensitively
regulated metabolic chains will always and inevitably precede or succeed the purely-
electrochemically mediated phenomena. Such overpotentials may be masked by the ohmic
and concentration losses, since the metabolic influence of bacteria can occur at both the bulk
electrolyte or at the electrochemical interface adjacent to the electrode when a microbial
biofilm is formed (Dominguez et al. in press).
Figure 4.1 displays the overpotentials (or potential losses) occurring in an MFC over a
polarization curve.
38
Figure 4.1: Potential losses (overpotentials) over the polarization curve of MFC
A polarization curve analysis of a MXC can indicate to what extent the various losses
listed contribute to the overall potential drop. This can point to possible measures to
minimize them in order to approach the ideal potential.
Thus, for an MFC total overpotential can be expressed as;
η = ηact + ηconc + ηohm + ηpreced
Since both the electrolyte and the electrodes obey Ohm's law, ηohm can be expressed as IRint,
in which I is the current flowing through the MFC and Rint is the total cell internal resistance
of the MFC.
η = ηact + ηconc + IRint + ηpreced
4.1.2 Types of Current and Potential Distributions
The distribution of current and potential is highly important in electrochemical systems since
the output and the performance of the system can be strongly affected by them (Orazem and
Tribollet, 2008).
Current and ions flow through the paths that are subject to less resistance, which leads to a
certain distribution in the electrochemical systems. This distribution can be due to many
factors such as geometry, conductivity of the materials, and the different contributions to
overpotential.
39
Typically, a classification is made based on some general rules and assumptions in order to
determine such distribution for a macroprofile:
i. Primary current and potential distributions
In the case of primary distribution, the passage of the current through the system is controlled
by the ohmic resistance. Therefore, primary distribution applies when the ohmic resistance
dominates and surface overpotentials can be neglected (Newman and Thomas-Alyea, 2004;
Orazem and Tribollet, 2008).
Primary current distribution is independent of flow rate, since it is considered that convection
is great enough to eliminate concentration variations, and consequently the distribution is
considered symmetric. The electrolyte that is adjacent to the electrode is taken to be an
equipotential surface, under the assumption that the concentrations are uniform within the
electrolyte. The current density is infinite at the end of the electrodes since the current can
flow through the solution beyond the ends of the electrodes (Newman and Thomas-Alyea,
2004).
The potential distribution at the electrode surface (ΦS) is a solution of the Laplace’s equation.
An example on this solution in case of two parallel plate electrode configurations can be seen
in Figure 4.2.
Figure 4.2: Two parallel plate electrodes opposite to each other in the walls of an insulating
flow channel (solid curves: current lines, dashed lines: equipotential surfaces) (Newman and
Thomas-Alyea, 2004)
Generally, the primary distribution shows that the more inaccessible parts of an electrode
receive a lower current density. When the electrode and the insulator lie in the same plane, the
primary current density is inversely proportional to the square root of the distance from the
edge for positions sufficiently close to the edge which can be expressed as:
40
)/2(sinhsinh
)(tanh/cosh
22
2
Lx
K
j
j
avg
n
where jn is the normal component of current density on the electrode (A/m2), javg is the
average current density, K is the complete elliptic integral of the first kind, x is the distance
measured from the centre of the electrode, L the length of the electrode and ε = ПL/2h
(Newman and Thomas-Alyea, 2004).
Based on the previous explanations, the following hypothesis can be defined for a primary
potential distribution model:
Activation and concentration overpotentials are neglected.
The electrodes are considered as perfect conductors; therefore, the electrode potential
(ΦM) is constant.
The electrolyte potential over electrodes (ΦS) is constant.
The outer surface of the electrodes is considered to be insulating:
The conductivity of the electrolyte (k) is constant.
Limit conditions:
The electrodes are at equilibrium conditions: Ean = Ean,eq Ecat = Ecat,eq
The electrolyte at the electrode surface obeys Ohm’s Law:
Figure 4.3 displays the primary potential distribution model in an electrochemical cell
consisting of two parallel-plate electrodes. At equilibrium conditions (∆V = Eeq), no potential
gradient within the electrolyte exists (Φelectrolyte= ΦS,A=ΦS,C) and ηohm is therefore neglected
(Figure 4.3-A). As a result, current density is equal to zero (j = 0 A/m2).
When the potential difference is larger than the equilibrium potential (∆V > Eeq), a potential
gradient is established within the electrolyte (ΦS,A ≠ ΦS,C). As a result, current density is no
longer equal to zero (j ≠ 0 A/m2). For this case, the potential difference over the equilibrium
potential is distributed within the electrolyte (Figure 4.3-B).
0S
Skj
41
A B
Figure 4.3: Primary potential distribution over parallel plate electrodes
(A: ∆V=Eeq and B: ∆V>Eeq) (modified from Viaplana, 2010)
ii. Secondary current and potential distributions
The secondary distribution is considered when the reaction kinetics cannot be neglected.
Activation overpotential that is associated to the electrochemical reactions at the electrode
becomes relevant while concentration variations at the electrolyte are neglected. Therefore,
the electrolyte that is adjacent to the electrode can no longer be considered as an equipotential
surface (Newman and Thomas-Alyea, 2004; Orazem and Tribollet, 2008).
The potential distribution at the electrode surface (ΦS) is a solution of the Laplace’s equation
with a more complex boundary condition resulting from the polarization of the electrodes.
The electrode kinetics is expressed by the following equation which describes how the
electrical current on an electrode depends on the electrode potential, so called Butler-Volmer
equation:
d
eq
dOx
eq
Ox EERT
nFEE
RT
nFjj ReRe
0
1expexp
At small overpotentials the Butler-Volmer equation can be linearized as:
0
0
0 y
s
s
nsn
yRT
nFj
d
djj
s
This provides a linear boundary condition for the Laplace’s equation. y is the coordinate
normal to the electrode surface. At sufficiently small overpotentials, the equation can be
linearized as:
42
s
y
Jy
0
J (in/iavg) is a dimensionless parameter and equals to nj0nF/ RT. For J ∞ primary current
distribution is obtained where the ohmic resistance dominates over the kinetics resistance at
the interface. For any finite value of J, secondary distribution is obtained which is more
uniform and finite at the edge of the electrode (Newman and Thomas-Alyea, 2004).
The following hypothesis can be defined for a secondary potential distribution model based
on the previous explanations:
Activation overpotential exists: the overpotential is distributed as ohmic drop in the
electrolyte and surface overpotential at the the electrode.
Concentration overpotential is neglected.
The electrodes are considered as perfect conductors; therefore, the electrode potential
(ΦM) is constant.
The electrolyte potential over electrodes (ΦS) is not constant. And it depends on the
local current density.
The outer surface of the electrodes is considered to be insulating: 0S
The conductivity of the electrolyte (k) is constant.
Limit conditions:
The electrode are not at equilibrium conditions:
d
eq
dOx
eq
Ox EERT
nFEE
RT
nFjj ReRe
0
1expexp
The electrolyte at the electrode surface obeys Ohm’s Law: Skj
Figure 4.4 displays the secondary potential distribution model in an electrochemical cell
consisting of two parallel-plate electrodes. The potential gradient within the electrolyte is
established (ΦS,A≠ΦS,C) and therefore j ≠ 0 A/m2.
43
Figure 4.4: Secondary distribution over parallel-plate electrodes (modified from Viaplana,
2010)
Far from equilibrium conditions, following equations are used :
Anode: eqanananan EERT
nFjj ,0 exp
Cathode: eqcatcatcatcat EE
RT
nFjj ,0
)1(exp
When a potential difference close to the equilibrium potential j can be described as:
eqSMeq ERT
nFjEE
RT
nFjj 00
Therefore, for each electrode:
Anode: anASAManan ERT
nFjj ,,0
Cathode: catCSCMcatcat ERT
nFjj ,,0
Detailed information about the electrode kinetic equations and their simplified forms based on
different limiting conditions is provided in Appendix 1.
iii. Tertiary current and potential distributions
The tertiary distribution takes into account the concentration changes mostly due to diffusion;
therefore, mass transfer phenomena (reflected as concentration overpotential) play an
important role, as well as Ohmic resistance and kinetic limitations (Newman and Thomas-
Alyea, 2004; Orazem and Tribollet, 2008).
44
Tertiary current and potential distributions apply when Laplace’s equation is replaced by a
series of n equations of the form:
iii RNt
c
Where ic is the concentration of species i, iN is the net flux of species i, and iR is the rate of
generation of species, coupled with electroneutrality:
i
iicz 0
Where n represents the number of ionic species in the system. Thus, tertiary distributions
implicate the assumption that concentrations are uniform. Ohmic, kinetic and mass-transfer
resistances all play a role in the distribution (Figure 4.5). The distribution of local current
density results of the resolution of a complex problem that takes into account the Laplace’s
equation and Ohm’s Law, as well as the convective diffusion equation that controls the
transport of species to the electrode (Newman and Thomas-Alyea, 2004).
Figure 4.5: Concentration profile at the electrode –electrolyte interface
The previous described Butler–Volmer equation is valid when the electrode reaction is
controlled by electrical charge transfer at the electrode (and not by the mass transfer to or
from the electrode surface from or to the bulk electrolyte). In the region of the limiting
current, when the electrode process is mass-transfer controlled, the value of the current
density becomes concentration-dependent:
eq
Ox
Electrode
Oxeq
d
Electrode
d EERT
nF
C
CEE
RT
nF
C
Cjj
)1(expexp
Re
Re0
45
The following hypothesis can be defined for the tertiary potential distribution model:
Concentration overpotential exits.
The electrodes are considered as perfect conductors; therefore, the electrode potential
(ΦM) is constant.
The electrolyte potential over electrodes (ΦS) is not constant.
The outer surface of the electrodes is considered to be insulating: 0S
The conductivity of the electrolyte is constant.
Limit conditions:
The electrode are not at equilibrium conditions:
eq
Ox
Electrode
Oxeq
d
Electrode
d EERT
nF
C
CEE
RT
nF
C
Cjj
)1(expexp
Re
Re0
Mass balance : nF
jCD
Electrodeii
The electrolyte at the electrode surface: i
iii CDzFkj S
Table 4.1 summarizes the hypotheses and system parameters associated with each type of
distribution.
Table 4.1: Hypotheses and parameters for each type of current and potential distribution
Distribution Hypothesis Parameters
Primary Ohmic resistance Geometry, material conductivity
Secondary Ohmic resistance
Kinetic resistance
Geometry, material conductivity,
activation overpotential
Tertiary Ohmic resistance
Kinetic resistance
Mass-transport resistance
Geometry, material conductivity,
activation overpotential,
concentration overpotential
For MFCs, uniform current distribution over the electrodes is desirable for efficient
operation. However; even for a simple-cell configuration, the calculation of the current
distribution is a very complex problem. Furthermore, difficulties intensify when increasing
the complexity of the cell geometry, which is the main reason to prefer numerical solutions
rather than analytical solutions for such calculations.
46
4.2 Numerical Modeling of MFC via COMSOL Multiphysics
For the numerical modeling of current and potential distributions, the most appealing tool to
deal with complex environments like MFCs is nowadays ‘COMSOL Multiphysics’.
Even though no published work exists in this direction so far, since modeling MFCs is a new
approach itself, some researchers (Picioreanu C., Delft University of Technology, Netherlands
and Bergel A., ENSIACET, France) made efforts to stimulate progress from the fundamental
perspective. Engineering-oriented efforts are still highly required.
COMSOL Multiphysics is an engineering simulation software that facilitates all steps of a
computational modeling process; such as defining the geometry, surface meshing, specifying
the physics, solving, and then visualizing the results.
COMSOL versions above 4.0 have an application, ‘The Batteries & Fuel Cells Module’, that
provides easy-to-use tools for simulation of fundamental processes of fuel cells. With it, the
impact on performance of different materials, geometric configurations, and operating
conditions can be quickly and accurately investigated.
More importantly, the Module features have options to study primary, secondary, and tertiary
potentials and current density distributions in electrochemical systems. The electrode
reactions, which are coupled to the transport phenomena, provide full descriptions of the
electrode kinetics including activation and concentration overpotentials. The cell can contain
solid or porous electrodes with dilute or concentrated electrolytes included in the COMSOL
Multiphysics library.
At this point, in order to have a deeper perception of COMSOL and its function over
modeling MFCs based on current and potential distributions; the following chapter (Chapter
5) will be more specific by presenting the practical modeling applications using this efficient
tool.
47
CHAPTER 5: MODELING WORK
The present modeling study is conducted with the aim of developing new cost-efficient MFC
designs but also improving the system performances of the existing MFCs that are currently
under experimental evaluation at VITO. Towards that aim, three dimensional (3D) models
based on current and potential distributions are constructed for single and stacked MFCs
using COMSOL Multiphysics 4.2 as modeling tool.
The current distribution profiles over the electrodes are investigated in order to obtain high
system efficiencies and determine the electrochemically active sites. In addition, high local
current density magnitudes are aimed at the same time they are homogeneously distributed
over the electrode surface. The importance of having homogenous current and potential
distribution for MFCs and the advantages of using COMSOL Multiphysics were largely
explained in the previous chapter (Chapter 4).
Typically, these studies are initiated with the primary current distribution and continued with
secondary and tertiary current distribution models respectively, due to the fact that the non-
uniformity is reduced from primary to tertiary distribution, in other words, primary current
distribution displays the worst case scenario. This is why if the primary current distribution is
as uniform as possible, the secondary and tertiary will more likely be uniform as well. The
present investigation only covers up to the secondary current distribution model, since the
resolution of a tertiary distribution would take deeper understanding and time than those
appointed to the present work. Future investigations on this direction are nonetheless
suggested to follow-up such work.
5.1. COMSOL Modeling Procedure
There are general simulation instructions that should be followed when modeling COMSOL
Multiphysics 4.2 for any application, yet these instructions can vary according to the aim of
the study. This section briefly introduces the procedure used for building MFC models based
on potential and current distributions by explaining the following modeling steps:
I. Model Wizard
When COMSOL is opened the Model Wizard opens by default in order to select the basic
elements of the models such as space dimension and physics interfaces. After selecting the 3D
48
as space dimension, the physics interface is chosen as primary (or secondary, tertiary) current
density distributions physic interface.
II. Parameter Definitions
After the Model Wizard, parameters used throughout the modeling should be defined on the
parameter table that is under the Global Definitions section. Parameters are scalar numbers
that can be used for geometric dimensions (e.g. width, length or depth), mesh sizes, physics
characteristics (e.g. current or potential), and etc…
III. Geometry
This section is where the model geometry, which is a collection of bounded geometric
entities, is built. The geometric entities are dimensioned and positioned based on previously
defined parameters and connected to each other with several operations to form a model
geometry. Various geometric entities can be used at different shapes and phases; here, solid
blocks are preferred as geometric entities to construct the desired MFC models (See Figure
5.1).
IV. Physics
This section demonstrates the features of the previously selected physic interface; primary (or
secondary, tertiary) current distribution physic interface. It provides tools for building detailed
models of the configuration of the electrodes and electrolyte in electrochemical cells. It also
includes descriptions of the electrochemical reactions and the transport properties that
influence the performance of batteries, fuel cells, and other electrochemical cells. After
building the geometric entities, each of them is attributed to different electrochemical cell
components. Material properties, boundary and interface conditions, equations and initial
conditions are set in this section.
V. Mesh
This section enables the discretization of the model geometry into small units of simple
shapes, referred to as mesh elements. Free Tetrahedral is chosen as mesh technique
generating an unstructured mesh with tetrahedral elements for 3D models. The size and
sequencing of the mesh elements are introduced.
VI. Study
Finally, the model is run in this section by using the previously created meshes.
49
VII. Results
After the simulation is completed, final image of the model is seen here by selecting the
desired output data (See Figure 5.2).
It should be noted that the provided general COMSOL modeling procedure is detailed and
further improved towards the needs of each constructed model throughout the modeling work.
5.2. Initial Model Geometry, Definitions and Response
A defined geometry was constructed according to an existing prototype of single-cell MFC,
with all components fully developed at VITO. Stack-MFC models could be in the future
developed using this approach, as well taking into account other than already-existing
geometrical designs. In this work, different MFC-component configurations were proposed as
deviations from this original geometry in order to improve the individual components,
assemblies and full-prototype.
For the full cell configuration, the initial model was considered to consist of a rectangular
electrolyte domain separating two parallel arrays of porous electrodes (cathode and anode)
supported by metallic grid current collectors (without a separator or membrane between the
electrodes) and lugs, which are placed on top of each current collector (Figure 5.1).
Figure 5.1: Initial model geometry (full cell configuration with porous electrodes and grid
current collector)
50
The metallic current collector provides electronic conductivity to the electrode and increases
electron recovery. The lug was considered to be made of the same material as the current
collector and it is used for maintaining the external electrical connection between the cell and
the potentiostat, power source or external load (resistance), accordingly to the conditions in
use.
Once the geometry is constructed, it is simulated with the procedure explained above, in order
to investigate the primary current distribution at the interface. The electrolyte current density
(A/m2), referring to the current density (j) over the porous electrode-electrolyte interface was
selected as the most relevant output to be analyzed. As a result, the local current density
magnitudes are displayed in different colors over the electrode surface, which provide
practical visualization of the current distribution profile. A color scale also is shown next to
the geometry which attributes the color range to the numerical solutions for the local current
density magnitudes (Figure 5.2).
The visualization of the color profile along the geometry and the color scale magnitudes are
both highly important for proper interpretations of the output data. The desired output is to
have the highest possible maximum current density magnitude (jmax) homogenously or well-
distributed at the electrode-electrolyte interface.
Figure 5.2: Primary current distribution profile over the initial model geometry as
output image
51
In the figure above, it is seen (on the color scale) that the maximum local current density
(jmax) obtained is 12.401 A/m² while minimum (jmin) is equal to 0.0174 A/m². On the other
hand, the current distribution profile increases from blue to red on the porous electrode (in
this case simulating a cathode), starting at the edge opposed to the lug. This can be interpreted
in terms of a highly heterogeneous current distribution at the electrochemical interface
between the porous electrodes and the electrolyte, highly dependent on the location of the lug
at the current collector.
This example is provided with the purpose to introduce the reader to the models that were
developed within the context of this research. However, details on the parameters used and
the cases of study are described in the following sections.
5.3 Primary Current Distribution Models
This section describes the investigations performed on the primary current distribution in a
fuel cell configuration during 0.02 A of discharge, at open circuit conditions. At this point, no
parameter related to the microbial dynamics or kinetics is considered, as they are not relevant
for this type of distribution. In primary current distribution, the potential losses due to
electrode kinetics and mass transport are assumed to be negligible, and ohmic losses govern
the current distribution in the cell; thus, primary current distribution study focused on the
optimum geometry investigations in order to find the most homogenous distribution profile.
5.3.1 Influence of Current Collector Design
The design of the current collector is perhaps the most significant issue for MFC designs,
since this highly electrically conductive material (e.g. stainless steel) directly affects the
system performance. In addition, current collectors are the most expensive components of an
electrochemical cell in terms of material and fabrication costs, in the case of non-precious
metal-based electrodes. This is why, it is essential to find a cost-efficient and performance-
effective current collector design.
For this purpose, the options of using plate or grid current collector were investigated. For an
accurate comparison, all the geometry parameters are kept constant for the models except the
current collector design.
52
Figure 5.3: Primary current distribution profiles with plate (left) and grid (right) current
collector designs
As can be observed in Figure 5.3, the primary current distributions of the two types of current
collectors studied include a heterogeneous profile. It is immediately perceptible that the range
of current densities rather broad at the porous electrode-electrolyte interface; however, the use
of a grid current collector enlarges the region of the porous electrodes that is active at higher
current densities. Besides the distribution, higher jmax (12.401 A/m²) is obtained with grid
current collector than with plate current collector (3.8764 A/m²). Therefore, it can be safely
assumed that utilizing a grid current collector for further models concerning the geometry of
study would lead to better performing MFC configurations.
5.3.2 Influence of Lug Dimensioning and Design
Lug maintains the external connection between electrodes, in other words, electrons will be
transported away from or towards a particular electrode through the respective lugs. For this
reason, the contact phase between the lug and the cell is considered to have an important
influence on the current distribution profile.
53
Figure 5.4: Primary current distribution profiles with different lug widths (W_lug)
When the width of the lug is increased (keeping constant all other system parameters) even
though jmax doesn’t differ significantly from one case to the other, it is observed that the
distribution over the electrode is highly influenced by this parameter. In Figure 5.4, it is
observed that the high current density region is distributed to a larger area in case of study on
the right when compared to the one on the left.
On the other hand, it can be observed that changing the lug height has no influence on the
current distribution over the electrodes as expected (Figure 5.5). This is considered to be due
to the high conductivity of the lug material that prevents current to encounter any
considerable resistance over the lug.
Figure 5.5: Primary current distribution profiles with different lug heights (H_lug)
54
Finally, the options of using plate or grid lug were also investigated. There was no significant
difference in between the two designs (Figure 5.6), neither in jmax nor in current distribution.
Although the usage of the grid lug design has no considerable benefit over the output, this
configuration was considered in further models for practical concerns since the lug is usually
fabricated in one piece with the current collector, and usage of grid current collector design
was previously proven advantageous.
Figure 5.6: Primary current distribution profiles with plate (left) and grid (right) lug designs
Although it is proven that increasing the W_lug gives better distribution, the usage of lug
which has the same width as the grid current collector was not considered due to the possible
practical difficulties of maintaining the external connections between the cell and the
electrochemical apparatus with a lug that covers the cell entrance, especially, in case of stack-
cell.
5.3.3 Half Cell Configurations
Half cell configurations were also constructed, in order to examine the current distribution
profiles at the single-electrode level. For that configuration, one of the electrodes is simply
eliminated from the initial geometry and electrolyte domain is considered to be adjacent to an
insulating material.
Figure 5.7 confirms that the half cell configuration with both current collector designs (plate
and grid) have nearly the same distribution profile as in full-cell array, as well as close jmax
values over the cathode (See Figure 5.3 and 5.7).
55
Figure 5.7: Primary current distribution profiles for half-cell configurations with plate (left)
and (grid) current collector
5.3.4 Influence of Lug Positioning on Different Current Collector Designs
In this section, for fuel cell models based on primary current distribution, four different
geometries were constructed with the guidance of the output obtained from the previous
models. The geometries considering different current collector (cc) and lug configurations
are:
1. Plate cc with one lug on each cc
2. Plate cc with two lug on each cc
3. Grid cc with two lug on each cc
4. Grid cc with one lug on each cc
It was previously proven advantageous to increase the width of the lug. Here it is indented to
observe the effect of using two lugs from cross sides of the cc instead of only one wide lug in
order to better distribute the current over the electrode surface. Although, it was proven that
the grid cc is more efficient than the plate cc, the study was conducted for both plate and grid
cc designs in order to see of the if the influence of lug positioning overcomes the influence of
the cc design. The mentioned geometries can be examined in Figure 5.8.
The aim of this geometry and configuration study is to select one of the four configurations
and perform the further progress and optimizations of the variables directly associated to the
physics (current distribution).
56
Figure 5.8: Four geometry configurations with different lug and cc designs
After building the geometries, they were simulated once again with the primary current
distribution interface. Apart from the cc and lug designs, all other system parameters were
kept constant.
The results can be examined in Figure 5.9. It is seen that placing two lugs from cross corner
on the cc gives a better current distribution (2nd
and 3rd
configurations); this beneficial effect
of two-lug usage is more apparent for the 3rd
configuration since it is combined with grid cc
design.
Although jmax (55.251 A/m2) value obtained from the 4
th configuration is nearly as twice as
the jmax obtained from 3rd
configuration (29.443 A/m2), the preferable case is to obtain the
better distribution instead of observing a higher jmax in one corner.
Ultimately, the 3rd
geometry is regarded as the ideal option as it provides a more homogenous
current distribution over the surface. Further optimizations and progress studies were decided
to be performed over that geometry.
57
1
4
2
3
Figure 5.9: Primary current distribution profiles over the four geometry
5.3.5 Influence of Different Parameters in Selected Geometry
After the selection of the model geometry in the previous section, the subsequent studies were
initiated over this model (Figure 5.9-3), hereby called reference model. The geometry and the
material parameters of the reference model are listed in the Table 5.1.
Table 5.1: Geometry and material parameters of the reference model
Parameter Symbol Value
Width of the current collector W_cc 11 cm
Height of the current collector H_cc 11 cm
Depth of the current collector D_cc 0.05 cm
Width of the lug W_lug 2.788 cm
Height of the lug H_lug 1.564 cm
Depth of the lug D_lug 0.05 cm
Width of the electrolyte W_e 11 cm
Height of the electrolyte H_e 11cm
Depth of the electrolyte D_e 1 cm
Width of each porous electrode frame W_peld 1.319 cm
Height of each porous electrode frame H_peld 0.4714 cm
Depth of each porous electrode D_peld 0.05 cm
Number of the porous electrodes in x direction N_x 8
Number of the porous electrodes in y direction N_z 21
Space between the porous electrodes s_grid 0.05 cm
58
Conductivity of the current collector σ_cc 4.8E6 S/m
Conductivity of the porous electrode σ_peld 9500 S/m
Conductivity of the electrolyte σ_e 1 S/m
The objective here was to investigate the effects of different model parameters and determine
the more influencing factors on the output data. This is why; seven key parameters were
individually varied, while the rest of the parameters remained constant at characteristic ranges
of the actual physical reference model. jmax values obtained from that ranges were recorded
(Table 5.2).
Table 5.2: Variation of the parameters and jmax
The variation ranges for each parameter were chosen in respect to the possible practical or
physical laboratory implementations. For example, the conductivity of the current collector
(σ_cc) was varied from 4.8E6 S/m to 9500 S/m in order to investigate the primary current
distribution models in case of using less conductive current collector materials. The minimum
value of this variation range was selected as 9500 S/m which is equal to the practical
conductivity of the porous electrode (σ_peld); in this way, the case of not using conductive
current collector was also examined.
In order to compare accurately the effect of each parameter, both the variation ranges and jmax
ranges were normalized between 0 and 1, since the parameters vary in different ranges. Figure
5.10 displays the change of the jmax with alteration in different parameters. The normalization
calculations are explained in Appendix 2.
59
Figure 5.10: Normalized value of jmax vs normalized value of parameter
The geometry parameters such as electrode spacing (the distance between the cathode and
anode, in other words, depth of electrolyte, D_e), lug size (the width of the lug, W_lug) and
wire thickness (space between the porous electrodes, s_grid) have significant linear influence
on the jmax; with the increase of these parameters jmax linearly decreases. The lug size is the
most influencing factor among others since it establishes the region where current starts to be
distributed as it was explained before. The electrode spacing has relatively less effect on the
performance.
Wire thickness is an important characteristic of the grid current collector since, in practice; the
grid current collectors are typically made by welding the metallic wires to form a mesh
(Figure 5.11). Therefore; when current collector is used together with porous electrode, the
mesh openings are filled with the electrode material. Thus, the thickness of the wires that
composes the mesh is considered as the space between the porous electrodes and has more
influence comparing with other geometry parameters. When wires thickness decreases jmax
increases.
60
Figure 5.11: Image of a mesh current collector made of welded wires
Among the material conductivity properties, the less influencing one is determined as the
conductivity of the (σ_cc) as it seen from the graph above that jmax changes slightly with the
large alternation in σ_cc. However, σ_cc becomes more relevant when it approaches to the
conductivity of porous electrode (σ_peld). This also points out the importance of the σ_peld
comparing to σ_cc. Here, the conductivity of electrolyte (σ_e) doesn’t have a remarkable
effect since it has relatively low value than electrode and cc material.
This is study is important to decide the effective parameters that should be paid attention for a
cost-efficient design. It is discovered that the high conductive cc material usage does
significantly not increase the performance, so the cc material can be shifted from stainless
steel to a slightly less conductive but also less expensive material. It is also found that
decreased wire thickness augments the performance as well as reduces the cost of the cc since
less metallic wire is used to fabricate it.
It should be noted that in this section, jmax values are recorded in order to compare; however,
for individual evaluation of each parameter both jmax and the output image of distribution
profile should be taken into account for a global and a more accurate conclusion.
5.3.6 Influence of Grid Size
The grid cc was determined as more performing than the plate cc. However, further
improvements on design of the grid cc were foreseen. With this objective, smaller and larger
frames that are filled with porous electrodes are considered. This means that first W_peld is
decreased, and then H_peld is increased starting from the reference model.
61
Figure 5.12: Primary current distribution profiles over the reference model and decreased
W_peld
Figure 5.12 displays that when the W_peld is decreased from 1.319 to 0.5 cm the current
distribution profile does hardly differ. However it is observed in the Figure 5.13 that the
increase of H_peld from 0.4714 to 1 cm improves the current distribution profile. Higher jmax
magnitudes are obtained at the regions that the current could not reach on previous cases.
Figure 5.13: Primary current distribution profiles of over the reference model and increased
H_peld
Increased H_peld values can practically be maintained by reducing the cc material in order to
extend the mesh openings when fabricating the grid cc. This may result in cost benefits as
well because the less material is needed for the construction of a same sized-electrode. With
the same approach, since it is seen that W_peld has no noteworthy effect on the performance,
62
larger openings can be considered to the horizontal direction which reduces the cc material
usage which is beneficial for the cost without altering the performance. Nonetheless,
diminishing the majority of the material would directly impact on the mechanical properties
of the electrode. Therefore, an optimization between a higher and homogeneously distributed
current as well as a mechanically solid electrode and costs must be addressed.
5.4 Secondary Current Distribution Models
This section describes the investigations performed on the secondary current distribution in a
microbial fuel cell during at closed circuit conditions. The parameters related to the microbial
kinetics that are obtained from the experiments are inserted to the model. The mass transport
phenomena is assumed to be negligible, ohmic losses and activation polarization losses
govern the current distribution in the cell.
The cell geometry that is used for the secondary current distribution models was determined
with respect to the primary current distribution modeling results. This is why; the gird current
collector and two lug placed on the current collector from cross corners were used. In addition
the increased H_peld value was used (H_peld=1 cm) as it was determined to be more
performing in terms of distribution profile in the Section 5.3.6.
The half-cell configuration was chosen for the secondary distribution profiles as in the
experimental case. The other system parameters were also tried to be determined from the
experimental set-up (active surface area of the electrodes, porosity of the porous electrodes,
etc…) in order to imitate the real cases as much as can be.
The bioelectrochemical kinetics obtained from the Section 6.2.2.5 was inserted as it was
explained in the Section 6.2.3.
The polynomial equation govern from current-potential curve (I vs E) (See Figure 6.21):
y = -0.043x4 – 0.001x
3 + 0.0107 x² + 0.0048x + 0.001
was inserted to the COMSOL and ffter building the geometry, it was simulated with the
secondary current distribution interface. Figure 5.14 displays the secondary current
distribution at the outer and inner boundary of the electrode. This current density values of the
inner and the outer surfaces are different from each other since the closed circuit condition
was used for the secondary distribution models. The result of interest is the inner surface of
63
the electrode since it is facing to the electrolyte; thus it represents the electrode-electrolyte
interface in this case.
Figure 5.14: Secondary current distribution profiles over the outer boundary (left) and inner
surface (right) of the electrode
As can be seen, the secondary current distribution is found to be much more homogenous for
the same geometry than the primary current distribution as the kinetics overcomes the
geometry influence. This is why, it is highly important to study the primary distribution
profiles in order to find the optimal geometry and material configurations before studying the
secondary current distribution models in order to see the kinetics effects.
The jmax was obtained as 13.25 A/m² (5.14), however; it should be noted that the current
density range over the inner surface of the electrode is 6-10 A/m² as can be read from the
color scale and visual observation. This is a value is in the same range with the experimental
result that was obtained in the Section 6.2.2.5 (7.32 A/m²).
5.5 Summary and Perspectives of the Modeling Work
The primary current distribution profiles proved the importance of the cell geometry and the
material properties in a fuel cell or microbial fuel cell. This is why the first focus of this study
was to the model the primary current distribution in MFCs since it displays the worst-case
scenario for the current distribution at the electrode electrolyte interface.
It is seen that with numerous variations over these parameters, many different distribution
profiles can be obtained. With respect to the existing VITO prototypes, the optimal geometry
64
was investigated for experimental usages. As a result, the grid current collector usage woth
increased vertical mesh openings (H=1 cm) was found to be more performing. It was also
proven that increased the contact phase between the lug and the cell increases the
performance; thus , two lug placed on the current collector from the cross corners can be a
better option for MFC geometries.
In addition to geometry, material conductivity was found to be an important factor alhtoug its
influence is less comparing with geometry parameters.
The bioelectrochemical kinetics, which was obtained from the most performing experimental
case, was introduced to this optimal geometry and secondary current distribution profile was
found for the microbial fuel cell. The selected experimental case was the fumarate-glucose
oxidation on a half-cell.
Secondary current distribution models showed more homogenous profiles since the
electrochemical kinetics is involved over the electrode surface. With the bioelectrochemical
kinetics obtained from experimental work, the maximal current density was found as the same
range than the experimental value. jmax obtained as 13.25 A/m² and the current density values
at the interface was observed in between 6-10 A/m² which are close to the experimental value
(7.32 A/m²).
It is highly important to remember that the target is for the secondary current distribution
modeling is not to obtain the same result with the experimental case. Sometimes simulating
an experimental case with computational modeling can be misguided since the environmental
factors are not considered in the simulations. However computational modeling and 3-D
simulations are highly significant in order to gain an insight opinion and conceptual
knowledge. Therefore; the accurate evaluations of the output images and the comparisons
between the models constructed with different parameters are more important than the value
itself.
The future studies will be continued with the tertiary current distribution profiles. Tertiary
distribution profiles should be modeled, especially when the gas-diffusion electrode or
membranes are involved since mass-transfer plays a massive role when these components are
introduced to the models.
The introduction of the additional components mentioned above are inevitable for the stack-
MFCs. Another important future target is to build the stack-MFCs in COMSOL and
65
investigate the optimal configuration for cost-efficient operations so that the path towards
scale-up of MFCs can be opened together with stack-cell development.
One of the most important characteristics of COMSOL is to combine the different physics
interfaces; thus, when electrochemistry is combined with fluid dynamics and mathematics it
can be much more effective for modeling tertiary distribution profiles. With the addition of
Optimization Module, the obtained models can be optimized.
66
CHAPTER 6: EXPERIMENTAL WORK
The experimental work is performed with the main goal of providing electrochemical kinetics
for the modeling work described in the previous chapter (Chapter 5). The data obtained from
the experimental kinetics are introduced for the secondary current distribution models as
input. Furthermore, the electrochemical activities of the selected bacteria are investigated as
well in the context of microbial electrosynthesis (MES) while the experimental kinetics is
monitored.
MES targets to have value-added product formation in a microbial electrochemical system
(MXC) either in electrolysis (MEC) or in fuel cell mode (MFC). The selected bacteria, hereby
called X strain due to the confidentiality concerns, are renowned for their capacity for high-
value product synthesis; nonetheless, their ability of electron (e-) transfer between substrate
and electrode has not been a subject of deep investigations. In addition, X strain is a type of
gram-positive bacteria, which have not particularly shown strong performance for
extracellular electron transfer (EET) but have been found to reduce or oxidize redox
mediators such as AQDS. Therefore, this chapter examines the electrochemical performance
in MXCs of the X strain—which has not been investigated up till now, while supplying
information for the modeling study that has been conducted in parallel.
6.1 Materials and Methods
6.1.1 Microbial Growth
The X strain was routinely cultured with NBAF medium (Nutrient Broth Acetate Fumarate)
containing 35 mM of fumarate as e- acceptor and 14.7 mM of acetate as e
- donor. The e
-
acceptor and donor were added from the previously prepared stock solutions to the medium at
the appropriate volumes. 1 L of medium that comprises the ingredient listed in Table 6.1,
including vitamin, mineral and salt mixtures, was also supplemented with 100 g NaCl and
adjusted to pH 9 in order to maintain optimum conditions particularly for X strain to grow.
67
Table 6.1: Components of 1 L of NBAF medium
Ingredient Amount
100X NB Salts 10 ml
NB Mineral Elixir 10 ml
DL Vitamins 0.75 ml
CaCl2.2H2O 0.04 g
MgSO4 .7H2O 0.1 g
NaHCO3 1.8 g
Na2CO3.H2O 0.5 g
Na2SeO4 1.0 ml
The serum bottles containing the medium were flushed with N2 to remove any trace of O2,
sealed, and autoclaved. The cultures were incubated (10% inoculum) in triplicate at 30 °C for
electrochemical experiments. 1 L of NBAF medium is prepared based on the procedure which
is modified from the protocol developed by Derek Lovely for the Geobacter sulfurreducens
(Coppi et al., 2001).
6.1.2 Electrochemical Cell Components
For the experimentation, half cell configuration which was previously designed at VITO, was
used where the as anode or cathode was the working electrode (WE) whereas Ag/AgCl – 3 M
KCl (+199 mV vs. SHE) was reference electrode (RE) and a Pt plate served as counter
electrode (CE). Activated carbon (AC) (30% porosity) was used as electrode material and
supported with stainless steel (SS) grid current collector. The current collectors were chosen
to be grid as their significance was previously proven in Chapter 5. Zirfon, an ion permeable
separator, was placed in between the WE and CE in order to prevent the interference of gases
evolutions (O2 or H2) which can occur at the CE during the electrochemical measurements
mode (Pant et al., 2010a).
6.1.3 Experimental Set-up and Operation
The half cells were single-chamber cylinder-shaped reactors that were assembled from the
components described above. They were operated in a recycled flow batch mode (See Figure
6.1).
68
Figure 6.1: Schematic view of the MXC half cell in recycled flow batch mode (modified from
Pant et al., 2010)
1 L of NBAF medium containing X strains (%10 v/v inoculation, 100 ml), that were
previously grown as explained in Section 6.1.1, was prepared and circulated continuously
from the feed bottle to cell during the process. When preparing NBAF feed solution, fumarate
or acetate was not provided to the medium since they were replaced by an electrode as e-
acceptor (anode) or e- donor (cathode) respectively for X strains to maintain the MET between
substrate and electrode. Anthraquinone-2,6-disulphonate (AQDS) was also added to NBAF
feed solution to serve as redox mediator.
During the operation, medium was fed with substrates (acetate, fumarate or glucose) at the
critical moments determined by electrochemical measurement for microbes to continue to
carry out the electrochemical activity.
The samples were taken from the feed bottle for the pH, conductivity and optical density
measurements in order to control the desired operational conditions (pH 9, σ= 145 mS/cm, λ >
0.3) as well as for further analytical measurements in order to determine the product
formation.
6.1.4 Electrochemical Methods
The electrochemical performance of X strain as biocatalyst oxidation of acetate and reduction
of fumarate were investigated. For this reason current evolution in the MXCs was monitored
by cronoamperometry (CA) technique. CA measurements were done at constant applied
69
anodic (Eanapp
) and cathodic (Ecatapp
) potential values that are favorable for the achievement of
the oxidation or reduction reactions in the half cells. The standard reduction potentials (Eo) of
substrates and mediator but also polarization losses (η) were taken into account for
determining applied potentials (See Figure 6.2). CA measurements were initiated at -200 mV
(vs Ag/AgCl) for acetate oxidation and -600 mV (vs Ag/AgCl) for fumarate reduction to be
achieved.
Figure 6.2: Shematic view of the determination of Eanapp
and Ecatapp
for acetate oxidation and
fumarate reduction during CA
Cyclic voltammetry (CV) technique was applied at the specific moments of the operation
based which were determined based on CA screening but also whenever the system was
intervened, e.g. substrate addition, change in potential. This way, the system was
characterized at that particular moment of process and microbial electrochemical kinetics
were obtained. CV was done at 3 scan rates for each time (1 mV/s, 10 mV/s, 100 mV/s). For
every scan rate, 3 cycles were run in between -700mV and 400 mV vs Ag/AgCl.
6.2 Results and Discussion
6.2.1 Half Cell Experiments with Acetate
Anodic activities of X strain were monitored trough CA measurement during 13 days in a cell
initially inoculated with acetate (A) and AQDS mediator (M). Until the 5th
day of CA,
reduction current density values were observed (j<0). After the 5th
day, half cell was
inoculated with more acetate. Nevertheless; no further current evolution was observed;
70
therefore, it was decided to feed the exhausted medium with new energy source and glucose
(G) was added at the 9th
day (See Figure 6.3).
Figure 6.3: CA – half cell experiment with acetate
At the initial time (t=0) and different moments of CA, CV measurements were taken;
however, no significant change was observed from initial moment until glucose addition
(t=9). 3 days after the glucose addition (t=12), another CV was run. As a result, the
polarization potential was decided to be switched from -200 mV to 50 mV since the CV
showed that (possibly) oxidation current could be obtained at that potential, according to the
slowest scan rate response (See Figure 6.5).
Indeed, the change in the potential gave a tendency of increase in the j value to the oxidation
direction (j>0); however; no significant oxidation current was obtained from X strain neither
with acetate nor with acetate and glucose combination as substrates. The maximal current
density (jmax) was achieved as 0.03 A/m² in 13 days of experimentation.
In a conducted with pure X strain (reference cannot be cited due to the confidentiality), the
maximal current density obtained was reported as 0.06 A/m2
using AQDS as mediator and
only glucose as substrate in a medium containing 5 g/L NaCl. Therefore, the oxidation
current obtained in this study with acetate is lower than the value of interest but not negligible
when compared to the literature studies.
71
Figure 6.4: CV after inoculation with acetate at initial time (t=0) of CA
The CV run at initial time after inoculation with acetate and AQDS (Figure 6.4) shows that
the reduction and oxidation peaks are not symmetric which is typical for microbial systems
since they are not reversible systems .
When Figure 6.4 and 6.5 are compared, it can be distinguished that one of the peaks, that is
observed at t=0, later disappears. This can be explained as electrochemical reduction of one
electrochemically active species -probably AQDS- since the CA also demonstrates reduction
current as well in the beginning.
.
Figure 6.5: CV after glucose addition at t=12 d of CA
72
6.2.2 Half Cell Experiments with Fumarate
6.2.2.1 Glucose addition and change in polarization potential (50 mV)
Cathodic activities of X strain were desired to be monitored through CA measurement over 30
days in a cell initially inoculated with fumarate (F) and AQDS mediator (M). Until the 5th
day
of CA, reduction current density values were observed (j<0). Then, the half cell was
inoculated with more fumarate. Nonetheless, no further current evolution was observed;
therefore, it was decided to feed the exhausted medium with new energy source such as
glucose (G) at the 9th
day (See Figure 6.6).
Figure 6.6: CA – 1st set of half cell experiments with fumarate
At the initial time (t=0) and different moments of CA, CV measurements were taken;
however, no evolution was observed from the initial moment until the glucose addition (t=9
d). On the other hand, after running a CV at the 16th
day (t=16 d), polarization potential was
decided to be switched from -600 mV to 50 mV since the CV showed that oxidation current
can be obtained at 50 mV for the slowest scan rate response (See Figure 6.8).
After switching the potential, oxidation current was attained immediately and jmax was
achieved as 1.8 A/m². In the study of Read et al. (2010), where they compare several gram-
positive and negative bacteria, maximal current density was reported as 0.2 A/m2
with gram-
positive Enterococcus faecium . For pure X strains, jmax is reported as 0.06 A/m2
as it was
mentioned before. Therefore, 1.8 A/m2 is a high magnitude as maximal oxidation current
73
density, since such high value has never been reported for gram-positive bacteria and
specifically for X strains. It is also highly significant to note that the experiment result was
obtained with a medium containing 100 g/L NaCl which is highly conductive environment
(145 mS/cm) unlike the literature cases.
After achieving the jmax, in 18 days, the medium was continued to be fed with more substrate
(F and G) however jmax did not further evolved.
Figure 6.7: CV after inoculation with fumarate at initial time (t=0) of CA
CVs (Figure 6.7 and 6.8) illustrate that jmax amplitudes are greatly changing at different scan
rates which means that mass-transfer is an important phenomena for the system. The
amplitude of the jmax is decreasing from 100 to 1 mV/s, since diffusion rate overcomes the
scan rate at sufficiently low scan rates, and as a result, smaller jmax values are obtained. This is
why, when modeling this type of system based on current and potential distributions, tertiary
current distribution should be taken into account (See Section 4.1.2).
After obtaining a significant jmax in the 1st set of experiments for fumarate, the following
experiments were conducted with the aim of reproducing or increasing this value for the X
strains.
74
Figure 6.8: CV after glucose addition at t=16 d of CA
6.2.2.2 Fumarate-glucose combination as substrate from the initial time
The following experiment (2nd
set of experiment) was decided to be initiated by providing
fumarate and glucose combination as substrate, AQDS as mediator and by polarizing at 50
mV from the beginning of the CA monitoring in order to simulate the previous experimental
conditions (1st set of experiments) where 1.8 A/m
2 of
oxidation current density was achieved.
Figure 6.9: CA - 2nd
set of half cell experiments with fumarate
It is seen from the graph above (Figure 6.9) that there was no reduction or oxidation current
evolution during the first 7 days under 50 mV polarization potential; thus, the potential
switched to -600 mV like it was initially determined. In between 7th
and 11th
days, reduction
75
current was observed as it was in the previous experiments with both acetate and fumarate
which is thought to be belonging to electrochemical -not bioelectrochemical- reduction of
AQDS. After the reduction current was stabilized, a CV was run and oxidation current was
observed after at 50 mV as it was observed in the previous measurements. Due to that fact,
the polarization potential was switched back to 50 mV; conversely, no further current
production was obtained during the next 6 days.
Figure 6.10 illustrates the CVs taken at each time of potential change during CA (t=7 d and
t=11 d) and as can be seen, there is no dissimilarity between in both CVs which proves that
there is no electrochemical transition due to microbial activity.
Figure 6.10: CV after inoculation with fumarate and glucose at t=7 d (left) and t=11 d (right)
of CA
Consequently, the fact that no reduction or oxidation of substrate was obtained neither under -
600 mV nor 50 mV polarization potential, even though it was previously achieved, suggests
that providing glucose as energy from the beginning leads X strains to fermentation instead of
respiration. Therefore, e- transfer between substrate and electrode cannot occur as a part of
respiratory chain of bacteria.
6.2.2.3 Influence of the bacterial growth in the half cell
The following experiments (3rd
set of experiments) were started by providing only fumarate
as substrate to eliminate the possible fermentation path as mentioned. In addition, the cell was
initially polarized at -600 mV considering the mediator reduction that was observed in CAs
before.
76
After starting with fumarate and later supplementing the medium with glucose, a CV was run
at the 4th
day and oxidation current was observed at 50 mV as it was in the previous
experiments. As a result, the polarization potential was switched to 50 mV (See Figure 6.13);
conversely, no current production was obtained during the next 10 days (See Figure 6.11). At
the first period, under -600 mV potential, the typical reduction current which is considered to
be belonging to AQDS was again observed in the Figure 6.11.
Figure 6.11: CA - 3rd
set of half cell experiments with fumarate
The cell medium was not found turbid during the operation based on the visual observation
which means microbial growth in the cell could not be retained. The optical density
measurements of the samples that were regularly taken from the half cell during the operation
verified that the X strains could not succeed to grow and as a result, current could not be
harvested from the system. The measured optical density was 0.1 which is under the lower
than the accepted minimum value (0.3) for system to operate
Figure 6.12 and 6.13 display the CVs taken at t=0 and t=4 d and as can be seen, the shape and
the amplitude of the peaks were changed after 4 days. This change can better be observed in
the fastest scan rate response since the hysteresis between forward and reverse scan are larger.
77
Figure 6.12: CV after inoculation with fumarate and glucose at initial time (t=0) of CA
Figure 6.13: CV after inoculation with fumarate and glucose at t=4 d of CA
6.2.2.4 Glucose addition and change in polarization potential (250 mV)
The following experiments (4th
set of experiments) were conducted during 30 days by
following the same procedure as the 1st set of experiments. The cell initially was inoculated
with fumarate and AQDS mediator, later with more fumarate. At the 9th
day glucose was
added as new energy source. Until the 15th
day of CA, reduction current density values were
observed (See Figure 6.14).
78
Figure 6.14: CA - 4th
set of half cell experiments with fumarate
At the initial time (t=0) and different moments of CA, CV measurements were taken;
however, no evolution was observed from initial moment until glucose addition (t=9) (Figure
6.15 and 6.16).
Figure 6.15: CV after fumarate inoculation at initial time (t=0) of CA
79
Figure 6.16: CV after glucose inoculation at t=16 d of CA
After running a CV at the 16th
day (t=16 d), polarization potential was decided to be switched
from -600 mV to 250 mV since the CV showed that oxidation current can be obtained at that
potential for the slowest scan rate (See Figure 6.16 and 6.17).
Figure 6.17: CV at lowest scan rate (1 mV/s) after glucose inoculation at t=16 d of CA
After switching the potential, oxidation current was attained immediately and jmax was
achieved as 0.12 A/m² which is an approximate magnitude comparing with the jmax values
reported in the previously mentioned literature values for other gram positive bacteria. This
indicates that the X strain is able to achieve mediated e- transfer between the suitable substrate
and electrode under suitable operation conditions.
80
However, with the same type of substrate and methodology; maximal current density was
achieved as 1.8 A/m² in the 1st set of experiments; therefore the value obtained here was
considered relatively low when compared with previous study.
The medium was continued to be fed with more substrate (F and G) after the jmax achieved
however the jmax did not further evolved.
6.2.2.5 Separation of the medium circulation in counter electrode and working electrode
compartments
For the next experiments (5th
set of experiments), increasing the jmax, which was previously
obtained, was set as primary target with appropriate provisions. This is why, the NBAF
medium in the counter electrode (CE) compartment was replaced with NaCl solution (100
g/L) which does not contain any bacteria or substrate inoculation. This was maintained by
separating the circulation system, which was described in Figure 6.1, into two compartments
(counter and working electrode) by using two feed bottles. With this new experimental set-up,
the interference of the electrochemical reactions occurring at the CE can eliminated for a
better observation over the WE.
The cell initially was inoculated with fumarate and AQDS mediator, later with more
fumarate. At the 10th
day glucose was added as new energy source. Until the 14th
day of CA,
reduction current density values were observed. After running a CV at the 14th
day (t=14 d),
polarization potential was decided to be switched from -600 mV to 50 mV; however, no
significant oxidation current was observed. On the other hand at the 17th
day the noteworthy
jmax was immediately reached when the polarization potential was switch to 150 mV; jmax was
attained as 7.32 A/m² in 18 days of experimentation (See Figure 6.20).
81
Figure 6.18: CA -5th
set of half cell experiments with fumarate
The CV that was run at the 14th
day can be observed in Figure 6.19 and 6.20; polarization
potential was decided to be switched from -600 mV, first to 50 mV, and then to 150 mV since
the CV showed that oxidation current can be obtained at that potential range for the slowest
scan rate.
Figure 6.19: CV after glucose inoculation at t=14 d of CA
82
Figure 6.20: CV at lowest scan rate (1 mV/s) after glucose inoculation at t=14 d of CA
Besides being above the reported literature value, 7.32 A/m² is higher jmax value that was
obtained in previous experiments (1.8 A/m²). This indicates that eliminating the possible
counter electrode reactions by circulation a medium without any bacteria or substrate
inoculation is an effective way of improving the system performance.
6.2.3 Bioelectrochemical Kinetics for Secondary Current Distribution Models
For the secondary distribution models, bioelectrochemical kinetics, that were obtained from
the 5th
set of experiments, was used. In this experiment 7.32 A/m² of maximal current density
was achieved without interference of possible electrochemical reactions occurring at the
counter electrode in this experiment. This is why, the 5th
set of experiments was considered as
the most suitable case in order to model the secondary current distribution for the microbial
fuel cells with bioelectochemical–not electrochemical- kinetics.
After the jmax was retained, a CV was run at slowest scan rate (1 mV/s) in order to obtain the
electrochemical kinetics based on the microbial activities of X strains. Figure 6.21 displays
the current-potential graph (I=f(E)) for the oxidation of substrate. The curve was fit to a
polynomial model and the equation govern from this kinetics model was inserted for the
secondary current distribution model. The scondary current distribution modeling result is
largely described in the Section 5.4.
83
Figure 6.21: Bioelectrochemical kinetics for secondary current distribution model:
I vs E after achieving the jmax=7.32 a/m² at the 5th
set of experiment
6.4 Summary and Perspectives of the Experimental Work
All in all, no significant oxidation current was attained with X strains neither with acetate nor
glucose as substrates at -200 mV and 50 mV polarization potentials during 13 days of
experimentation.
Significant reduction current was neither obtained with X strains with fumarate or glucose as
substrates at -600 mV, during 15 days of experimentation. On the other hand, important
oxidation current was obtained as 1.8 A/m² with X strains using a mixture of fumarate and
glucose as substrates at 50 mV polarization potential. In another trial, when the results were
tried to be reproduced, no significant reduction current was obtained with X strains either
with fumarate and glucose as substrates at -600 mV during 15 days of experimentation but
considerable oxidation current was obtained as 0.12 A/m² with X strains using a mixture of
fumarate and glucose as substrates at 250 mV polarization potential. The major oxidation
current was achieved as 7.32 A/m² when the possible counter electrode reactions eliminated
with X strains using a mixture of fumarate and glucose as substrates at 150 mV polarization
potential.
When comparing with reported values, the experimental results achieved for maximal current
density for X strains are important. After several experiments maximal current density was
84
improved from 1.8 A/m² to 7.32 A/m² which is a very substantial magnitude compared with
for that type of bacteria and specifically for X strains.
These high oxidation current density values proves that X strains are able to achieve mediated
e-
transfer (MET) by using AQDS as mediator, fumarate and glucose as substrates. The
addition of the mediators increases the performance in terms of current generation since
they facilitate electron transfer but they regarded disadvantageous in terms of the
toxicity. On the contrary, in this case AQDS is a non-toxic mediator.
The more importantly, X strains proved its electrochemical capacity in a very high conductive
medium. The capacity of this microbe to resist high salt concentrations, unlike most bacteria
makes it already desirable for any electrochemical system. This is because of the high
conductive environments improve the performance of the electrochemical system since the
ohmic drop is reduced in the cell.
Although, X strains are known for the microbial synthesis for high-value products, they have
never been investigated for the microbial electrochemical synthesis (MES).The fact that they
accomplish MET indicates the potential possibility of high-value product formation while
lowering the energy need for such production in a MES operation. The samples, which were
regularly taken from the electrochemical half cells during the operation will be analyzed for
quantifying the product formation with a suitable method.
In addition, operational parameters will be evaluated to keep the system at high current
density values for longer durations. The experiments will be repeated with fumarate and
glucose alone as substrate –not the mixture of them- in order to determine the contribution og
the each energy source separately. EIS measurements will also be examined in order to have
additional opinion about the system.
85
CHAPTER 7: CONCLUSION AND PERSPECTIVES
Towards the main objective of this work, MXCs were investigated from a unified approach of
computational modeling and experimental. The current distributions at the electrode-
electrolyte interfaces in microbial fuel cells at half cell and full cell level were investigated.
Smart designs of electrochemical cell components, focused on current collector, were
displayed. The importance of the geometry and material properties but also electrochemically
active sites at the interface were determined through primary current distribution models. The
secondary current distribution models were constructed by applying bioelectrochemical
kinetics obtained from experimental work.
Moreover, the electrochemical responses of the X strains were analyzed in order to
understand its electrochemical performance. X strains were found to be performing in terms
of MET in high conductive medium when compared with the literature studies. Microbial
growth, cell assemblies and effective operations were conducted towards that secondary goal
throughout the working period.
The perspectives of this work for the remaining internship period but also for the future
studies is to develop stack-MFCs models through COMSOL simulations, since stack-MFCs
are the key towards scale-up of these systems. Tertiary current distributions will also be
evaluated since it is expected for MXCs to fit to the tertiary current distributions models
especially for the stack-MFCs where mass-transfer phenomena is involved with the usage of
components such membrane, gas-diffusion cathode, etc…
86
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89
APPENDIX 1: Electrode Kinetics
Among variety of expressions, Butler-Volmer equation is one of the most fundamental
relationships in electrochemistry that defines the electrode kinetics. It describes how the
electrical current on an electrode depends on the electrode potential, considering that both a
cathodic and anodic reactions occur on the same electrode in the following form:
d
eq
dOx
eq
Ox EERT
nFEE
RT
nFjj ReRe
0
1expexp
where;
• j = electrode current density , A/m²
• jo = exchange current density, A/m2
• E = electrode potential, V
• Eeq = equilibrium potential, V
• T = absolute temperature, K
• n = number of electrons involved in the electrode reaction
• F = Faraday constant
• R = universal gas constant
• α = symmetry factor or charge transfer coefficient
i. At the equilibrium condition (E = Eeq, η = 0)
01
exp0exp0RT
nF
RT
nFjj
0j
ii. Close to equilibrium condition (E ≈ Eeq)
0)1(
exp0expRT
nF
RT
nFjj ox
xxfxxf x 1)()exp()( 0
d
eq
dOx
eq
Ox EERT
nFαEE
RT
nFαjj ReRe
0
111
90
eqEERT
nFjj 0
iii. Far from equilibrium condition
While the Butler-Volmer equation is valid over the full potential range, simpler approximate
solutions can be obtained over more restricted ranges of potential. In practice, where an
electrochemical reaction occurs in two half reactions on separate electrodes, it is considered
that the reverse reaction rate is negligible compared to the forward reaction rate so the Butler-
Volmer equation is simplified to a linear or logarithmic Tafel equation between the surface
overpotential and the potential derivative at the electrode, which is applicable to each
electrode where the overpotential is high. As overpotentials, either positive or negative,
become larger the second or the first term of equation becomes negligible, respectively.
Hence, simple exponential relationships between current and overpotential are obtained, or
the overpotential can be considered as logarithmically dependent on the current density
obtained in the following form:
Anode : E >> Eeq
d
eq
dOx
eq
Ox EERT
nFEE
RT
nFjj ReRe
0
1expexp
eqanananan EERT
nFjj ,0 exp
Cathode : E << Eeq
d
eq
dOx
eq
Ox EERT
nFEE
RT
nFjj ReRe
0
1expexp
eqcatcatcatcat EERT
nFjj ,0
)1(exp
91
APPENDIX 2: Normalization
Normalization is the process of reducing measurements to a standard scale in order to make
variables comparable to each other. For example, when measuring temperature, Fahrenheit
and Centigrade degrees are both valid but they produce different numbers. In order to know
the temperature when comparing the two scales, a calculation is necessary to turn one of the
numbers into the other scale of temperature; it is needed to reduce the measurements to the
same scale, and then compare. In the treatment of normalized scales, different paths are
followed depending upon the requirements of the normalization. One possible way of
normalization is rescaling the numerical variables in the range of [0,1].
In the present work, this approximation is used within the following the procedure:
1. Selecting the minimum and maximum values from the data set and assigning the letter
“A” the minimum value to and the letter “B” to the maximum value.
2. Selecting the new scale range from 0 to 1 and assigning the letter ‘a’ to the minimum
and letter ‘b’ to the maximum value.
3. Calculating the normalized value by:
Normalized value = a + [(X-A).(a-b)/(B-A)]
e.g. normalization of sigma_eld = 5000 (S/m)from sigma_eld data set (Section 5.3.5):
x = 5000
A = 1000, B = 15000
a = 1, b = 1
Normalized value = 0 + [(5000-1000).(1-0)/(15000-1000)] = 0.28
This calculation is done for each value from the data set and the following table is obtained:
Date set Normalized value
1000 0
2500 0.10
5000 0.28
7500 0.46
9500 0.60
15000 1