Mathematical model for microbial fuel cells with anodic biofilms and anaerobic digestion

Kinetic modeling for miniaturize single-chambered microbial fuel cell: effects of biochemical reaction on its performance

In this study, Nernst growth model equations are used to explain the anodic biofilm (ABF) modeling, linear sweep voltammetry (LSV) at various growth stages of biofilm, and polarization curve modeling for its electron generation behavior in a miniaturized single-chambered microbial fuel cell (SMFC). Kinetic constants of various growth model equations were determined using non-linear regression analysis. Maximum specific growth rate (μmax) at anodic surface is observed 0.016 h-1 at a glucose concentration of 12 g L-1, whereas retardation in μmax is observed 14 g L-1 or more in SMFC. LSV results showed maximum current density of 6720.56 mA m-2. Anode performance in SMFC is examined through polarization curve resulting maximum open-circuit voltage (OCV), minimum charge transfer loss, and ohmic loss for NWG (NiWO4 impregnated on rGO), NiWO4, rGO, and plain CC (carbon cloth) anode. These results demonstrate significant enhancement in performance of MFC to lead towards model-based process controlling for significant scale-up in future.

Methodology for In Situ Microsensor Profiling of Hydrogen, pH, Oxidation-Reduction Potential, and Electric Potential throughout Three-Dimensional Porous Cathodes of (Bio)Electrochemical Systems

We developed a technique based on the use of microsensors to measure pH and H2 gradients during microbial electrosynthesis. The use of 3D electrodes in (bio)electrochemical systems likely results in the occurrence of gradients from the bulk conditions into the electrode. Since these gradients, e.g., with respect to pH and reactant/product concentrations determine the performance of the electrode, it is essential to be able to accurately measure them. Apart from these parameters, also local oxidation-reduction potential and electric field potential were determined in the electrolyte and throughout the 3D porous electrodes. Key was the realization that the presence of an electric field disturbed the measurements obtained by the potentiometric type of microsensor. To overcome the interference on the pH measure, a method was validated where the signal was corrected for the local electric field measured with the electric potential microsensor. The developed method provides a useful tool for studies about electrode design, reactor engineering, measuring gradients in electroactive biofilms, and flow dynamics in and around 3D porous electrodes of (bio)electrochemical systems.

Model development of bioelectrochemical systems: A critical review from the perspective of physiochemical principles and mathematical methods

Bioelectrochemical systems (BESs) are promising devices for wastewater treatment and bio-energy production. Since various processes are interacted and affect the overall performance of the device, the development of theoretical modeling is an efficient approach to understand the fundamental mechanisms that govern the performance of the BES. This review aims to summarize the physiochemical principle and mathematical method in BES models, which is of great importance for the establishment of an accurate model while has received little attention in previous reviews. In this review, we begin with a classification of existing models including bioelectrochemical models, electronic models, and machine learning models. Subsequently, physiochemical principles and mathematical methods in models are discussed from two aspects: one is the description of methodology how to build a framework for models, and the other is to further review additional methods that can enrich model functions. Finally, the advantages/disadvantages, extended applications, and perspectives of models are discussed. It is expected that this review can provide a viewpoint from methodologies to understand BES models.

Contribution of configurations, electrode and membrane materials, electron transfer mechanisms, and cost of components on the current and future development of microbial fuel cells

Microbial fuel cells (MFCs) are a technology that can be applied to both the wastewater treatment and bioenergy generation. This work discusses the contribution of improvements regarding the configurations, electrode materials, membrane materials, electron transfer mechanisms, and materials cost on the current and future development of MFCs. Analysis of the most recent scientific publications on the field denotes that dual-chamber MFCs configuration offers the greatest potential due to the excellent ability to be adapted to different operating environments. Carbon-based materials show the best performance, biocompatibility of carbon-brush anode favors the formation of the biofilm in a mixed consortium and in wastewater as a substrate resembles the conditions of real scenarios. Carbon-cloth cathode modified with nanotechnology favors the conductive properties of the electrode. Ceramic clay membranes emerge as an interesting low-cost membrane with a proton conductivity of 0.0817 S cm^-1, close to that obtained with the Nafion membrane. The use of nanotechnology in the electrodes also enhances electron transfer in MFCs. It increases the active sites at the anode and improves the interface with microorganisms. At the cathode, it favors its catalytic properties and the oxygen reduction reaction. These features together favor MFCs performance through energy production and substrate degradation with values above 2.0 W m^-2 and 90% respectively. All the recent advances in MFCs are gradually contributing to enable technological alternatives that, in addition to wastewater treatment, generate energy in a sustainable manner. It is important to continue the research efforts worldwide to make MFCs an available and affordable technology for industry and society.

Effects of bioelectricity generation processes on methane emission and bacterial community in wetland and carbon fate analysis

Wetlands are an important carbon sink for greenhouse gases (GHGs), and embedding microbial fuel cell (MFC) into constructed wetland (CW) has become a new technology to control methane (CH4) emission. Rhizosphere anode CW-MFC was constructed by selecting rhizome-type wetland plants with strong hypoxia tolerance, which could provide photosynthetic organics as alternative fuel. Compared with non-planted system, CH4 emission flux and power output from the planted CW-MFC increased by approximately 0.48 ± 0.02 mg/(m2·h) and 1.07 W/m3, respectively. The CH4 emission flux of the CW-MFC operated under open-circuit condition was approximately 0.46 ± 0.02 mg/(m2·h) higher than that under closed-circuit condition. The results indicated that plants contributed to the CH4 emission from the CW-MFC, especially under open-circuit mode conditions. The CH4 emission from the CW-MFC was proportional to external resistance, and it increased by 0.67 ± 0.01 mg/(m2·h) when the external resistance was adjusted from 100 to 1000 Ω. High throughput sequencing further showed that there was a competitive relationship between electrogenic bacteria and methanogens. The flora abundance of electrogenic bacteria was high, while methanogens mainly consisted of Methanothrix, Methanobacterium and Methanolinea. The form and content of element C were analysed from solid phase, liquid phase and gas phase. It was found that a large amount of carbon source (TC = 254.70 mg/L) was consumed mostly through microbial migration and conversion, and carbon storage and GHGs emission accounted for 60.38% and 35.80%, respectively. In conclusion, carbon transformation in the CW-MFC can be properly regulated via competition of microorganisms driven by environmental factors, which provides a new direction and idea for the control of CH4 emission from wetlands.

Data-driven and validated dimensional analysis for rational scale-up of a dual-chamber microbial fuel cell system for water-energy nexus exploitation

Mathematical modelling of microbial fuel cells (MFC) facilitates their scale-up by maintaining dimensionless parameters across reactor volumes for consistent performance. This study developed data-driven correlations to predict areal power density for a batch-fed dual-chamber MFC using hybridised first-principle mechanistic model and Buckingham's Pi theorem. The established correlations were validated using experimentally-derived data for pre-enriched electroactive biofilm from mixed cultures. The biochemical model parameters are infilled with stoichiometric and thermodynamics estimations. Results showed that the correlations using logistic kinetics (Nash-Sutcliffe Efficiency, NSE = 0.59) outperformed Monod kinetics (NSE = 0.52) as the latter was not suitable for representing the first-order biochemical kinetics under limited substrate conditions. Sensitivity analysis on varying pH and bicarbonate concentration improved model predictions by ± 50%, though relative absolute error was ± 20% due to inherent error of estimated biochemical parameters. The application of hybridised approach for modelling MFC provides renewed perspectives for their rational design and scale-up applications.

Characterization of spatiotemporal electroactive anodic biofilm activity distribution using 1D simulations

Activity distribution limitation in electroactive biofilm remains an unclear phenomenon. Some observations using confocal microscopy have shown notable difference between activity close to the anode and activity at the liquid interface. A numerical model is developed in this work to describe biofilm growth and local biomass segregation in electroactive biofilm. Under our model hypothesis, metabolic activity distribution in the biofilm results from the competition between two limiting factors: acetate diffusion and electronic conduction in the biofilm. Influence of inactive biomass fraction (i.e. non-growing biomass fraction) properties (such as conductivity and density) is simulated to show variation in local biomass distribution. Introducing a dependence of effective diffusion to local density leads to a drastic biomass fraction segregation. Increasing density of inactive fraction reduces significantly acetate diffusion in biofilm, enhances biomass activity on the outer layer (liquid/biofilm interface) and maintains inner core largely inactive. High inactive fraction conductivity enhances biomass activity in the outer layer and enhances current production. Hence, investment in extracellular polymer substance (EPS), anchoring redox components, is benefit for biofilm electroactivity. However, under our model hypothesis it means that conductivity should be two order lower than biofilm conductivity reported in order to observe inner core active biomass segregation.

Review on microbial fuel cells applications, developments and costs

The microbial fuel cell (MFC) technology has attracted significant attention in the last years due to its potential to recover energy in a wastewater treatment. The idea of using an MFC in industry is very attractive as the organic wastes can be converted into energy, reducing the waste disposal costs and the energy needs while increasing the company profit. However, taking aside these promising prospects, the attempts to apply MFCs in large-scale have not been succeeded so far since their lower performance and high costs remains challenging. This review intends to present the main applications of the MFC systems and its developments, particularly the advances on configuration and operating conditions. The diagnostic techniques used to evaluate the MFC performance as well as the different modeling approaches are described. Towards the introduction of the MFC in the market, a cost analysis is also included. The development of low-cost materials and more efficient systems, with high higher power outputs and durability, are crucial towards the application of MFCs in industrial/large scale. This work is a helpful tool for discovering new operation and design regimes.

Reaction kinetics of anodic biofilms under changing substrate concentrations: Uncovering shifts in Nernst-Monod curves via substrate pulses

In the present study, it is shown that the concentration dependency of undefined mixed culture anodic biofilms does not follow a single kinetic curve, such as the Nernst-Monod curve. The biofilms adapt to concentration changes, which inevitably have to be applied to record kinetic curves, resulting in strong shifts of the kinetic parameters. The substrate concentration in a continuously operated bioelectrochemical system was changed rapidly via acetate pulses to record Nernst-Monod curves which are not influenced by biofilm adaptation processes. The values of the maximum current density j max and apparent half-saturation rate constant K s increased from 0.5 to 1 mA cm-2 and from 0.5 to 1.6 mmol L-1, respectively, within approximately 5 h. Double pulse experiments with a starvation phase between the two acetate pulses showed that j max and K s decrease reversibly through an adaptation process when no acetate is available. Pseudo-capacitive charge values estimated from non-turnover cyclic voltammograms (CV) led to the hypothesis that biofilm adaptation and the observed shift of the Nernst-Monod curves occurred due to changes in the concentration of active redox proteins in the biofilm. It is argued that concentration-related parameters of kinetic models for electroactive biofilms are only valid for the operating points where they have been determined and should always be reported with those conditions.

Modelling of self-sustainable microbial fuel cell type oil sensors based on restricted oxygen transfer and two-population competition

Oil leaks during oil industrial chain pose threats to the ecosystem. The microbial fuel cell-type oil sensor has been developed for early warning of such issues. Oil contacting with the sensor restricts oxygen availability and triggers correlative signal anomaly which serves as indicative of the oil presence. To extend its application for the real world, modelling of the sensor is required to pre-describe the signal behavior under unknown conditions. Therefore, by integrating Butler-Volmer, restricted oxygen transfer (ROT) and Monod equations, a dynamic ROT-MFC model with sufficient substrate precondition was developed. The ROT-MFC model was trained on the experimental single-oil-shock test (R 2 = 0.996) and validated by the experimental sequential-shocktest (R 2 = 0.998). Numerical analysis of the trained ROT-MFC model indicates that the single-shock detection has higher sensitivity (≥40.6 mV/detection) and the sequential-shocks detection spends a shorter response time (≤2.2 h). Besides, the sequential-shocks detection with proper strategy is more applicable due to flexible options on detection limit and working range. The model was further evolved into the TPC-ROT-MFC model by introducing a two-population competition (TPC) theory to describe performance under limited substrate conditions. Results indicate a critical substrate concentration range (42.1 to 62.8 mg-COD/L) for dividing baseline steadiness, and that the impact of substrate concentration on anodic charge transfer coefficient soars when the substrate concentration lessens furtherly. This sensor model is relatively easy to implement and may enhance practical use for design and operation.

Simulation of cathode for synthesizing organic acids by MES reduction of CO2

Microbial electrochemical system (MES) is a favorable tool for CO2 emission reduction. Microbial cathode is one of the core components of the system, and its surface energy transfer characteristics can greatly affect the yield of organic matter from MES. In order to solve the problem that the energy transfer characteristics of microbial cathode are not clear, the mathematical model of MES was constructed on the basis of the preliminary experiment with an electrode made of copper foam modified with the reduced graphene oxide, analyzing the current and substrate concentration in the biofilm with conductivity, cathode potential and porosity of the biofilm. The results show that when the cathode potential is higher than -0.8 V (VS SHE), the substrate concentration and current density in the biofilm are related to the cathode potential. However, when the cathode potential decreased to -0.8 V (vs SHE), the ability of biofilm to reduce CO2 basically reached saturation. Low conductivity (<10-3S/m) will lead to the formation of significant potential difference in the biofilm, which will reduce the substrate utilization rate and seriously affect the performance of microbial cathode. The current density is highest, when the porosity of the biofilm is about 0.35.

Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.

Microbial fuel cell soft sensor for real-time toxicity detection and monitoring

Real-time toxicity detection and monitoring using a microbial fuel cell (MFC) is often based on observing current or voltage changes. Other methods of obtaining more information on the internal state of the MFC, such as electrochemical impedance spectroscopy (EIS), are invasive, disruptive, time consuming, and may affect long-term MFC performance. This study proposes a soft sensor approach as a non-invasive real-time method for evaluating the internal state of an MFC biosensor during toxicity monitoring. The proposed soft sensor approach is based on estimating the equivalent circuit model (ECM) parameters in real time. A flow-through MFC biosensor was operated at several combinations of carbon source (acetate) and toxicant (copper) concentrations. The ECM parameters, such as internal resistance, capacitance, and open-circuit voltage, were estimated in real time using a numerical parameter estimation procedure. The soft sensor approach proved to be an adequate replacement for EIS measurements in quantifying changes in the biosensor internal parameters. The approach also provided additional information, which could lead to earlier detection of the toxicity onset.

The Influence of External Load on the Performance of Microbial Fuel Cells

In this work, the effect of the external load on the current and power generation, as well as on the pollutant removal by microbial fuel cells (MFCs), has been studied by step-wise modifying the external load. The load changes included a direct scan, in which the external resistance was increased from 120 Ω to 3300 Ω, and a subsequent reverse scan, in which the external resistance was decreased back to 120 Ω. The reduction in the current, experienced when increasing the external resistance, was maintained even in the reverse scan when the external resistance was step-wise decreased. Regarding the power exerted, when the external resistance was increased below the value of the internal resistance, an enhancement in the power exerted was observed. However, when operating near the value of the internal resistance, a stable power exerted of about 1.6 µW was reached. These current and power responses can be explained by the change in population distribution, which shifts to a more fermentative than electrogenic culture, as was confirmed by the population analyses. Regarding the pollutant removal, the effluent chemical oxygen demand (COD) decreased when the external resistance increased up to the internal resistance value. However, the effluent COD increased when the external resistance was higher than the internal resistance. This behavior was maintained in the reverse scan, which confirmed the modification in the microbial population of the MFC.

Monitoring stratification of anode biofilms in bioelectrochemical laminar flow reactors using flow cytometry

A laminar flow bioelectrochemical systems (BES) was designed and benchmarked using microbial anodes dominated with Geobacter spp. The reactor architecture was based on modeled flow fields, the resulting structure was 3D printed and used for BES manufacturing. Stratification of the substrate availability within the reactor channels led to heterogeneous biomass distribution, with the maximum biomass found mainly in the initial/middle channels. The anode performance was assessed for different hydraulic retention times while coulombic efficiencies of up to 100% (including also hydrogen recycling from the cathode) and current densities of up to 75 μA cm-2 at an anode surface to volume ratio of 1770 cm2 L-1 after 35 days were achieved. This low current density can be clearly attributed to the heterogeneous distributions of biomass and the stratification of the microbial community structure. Further, it was shown that time and space resolved analysis of the reactor microbiomes per channel is feasible using flow cytometry.

A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells

A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters.

Individual Based Model Links Thermodynamics, Chemical Speciation and Environmental Conditions to Microbial Growth

Individual based Models (IbM) must transition from research tools to engineering tools. To make the transition we must aspire to develop large, three dimensional and physically and biologically credible models. Biological credibility can be promoted by grounding, as far as possible, the biology in thermodynamics. Thermodynamic principles are known to have predictive power in microbial ecology. However, this in turn requires a model that incorporates pH and chemical speciation. Physical credibility implies plausible mechanics and a connection with the wider environment. Here, we propose a step toward that ideal by presenting an individual based model connecting thermodynamics, pH and chemical speciation and environmental conditions to microbial growth for 5·105 individuals. We have showcased the model in two scenarios: a two functional group nitrification model and a three functional group anaerobic community. In the former, pH and connection to the environment had an important effect on the outcomes simulated. Whilst in the latter pH was less important but the spatial arrangements and community productivity (that is, methane production) were highly dependent on thermodynamic and reactor coupling. We conclude that if IbM are to attain their potential as tools to evaluate the emergent properties of engineered biological systems it will be necessary to combine the chemical, physical, mechanical and biological along the lines we have proposed. We have still fallen short of our ideals because we cannot (yet) calculate specific uptake rates and must develop the capacity for longer runs in larger models. However, we believe such advances are attainable. Ideally in a common, fast and modular platform. For future innovations in IbM will only be of use if they can be coupled with all the previous advances.

A self-charging cyanobacterial supercapacitor

Microliter-scale photosynthetic microbial fuel cells (micro-PMFC) can be the most suitable power source for unattended environmental sensors because the technique can continuously generate electricity from microbial photosynthesis and respiration through day-night cycles, offering a clean and renewable power source with self-sustaining potential. However, the promise of this technology has not been translated into practical applications because of its relatively low performance. By creating an innovative supercapacitive micro-PMFC device with maximized bacterial photoelectrochemical activities in a well-controlled, tightly enclosed micro-chamber, this work established innovative strategies to revolutionize micro-PMFC performance to attain stable high power and current density (38 μW/cm² and 120 μA/cm²) that then potentially provides a practical and sustainable power supply for the environmental sensing applications. The proposed technique is based on a 3-D double-functional bio-anode concurrently exhibiting bio-electrocatalytic energy harvesting and charge storing. It offers the high-energy harvesting functionality of micro-PMFCs with the high-power operation of an internal supercapacitor for charging and discharging. The performance of the supercapacitive micro-PMFC improved significantly through miniaturizing innovative device architectures and connecting multiple miniature devices in series.

Microbial Electrochemical Technologies for Wastewater Treatment: Principles and Evolution from Microbial Fuel Cells to Bioelectrochemical-Based Constructed Wetlands

Microbial electrochemical technologies (MET) rely on the presence of the metabolic activity of electroactive bacteria for the use of solid-state electrodes for oxidizing different kinds of compound that can lead to the synthesis of chemicals, bioremediation of polluted matrices, the treatment of contaminants of interest, as well as the recovery of energy. Keeping these possibilities in mind, there has been growing interest in the use of electrochemical technologies for wastewater treatment, if possible with simultaneous power generation, since the beginning of the present century. In the last few years, there has been growing interest in exploring the possibility of merging MET with constructed wetlands offering a new option of an intensified wetland system that could maintain a high performance with a lower footprint. Based on that interest, this paper explains the general principles of MET, and the different known extracellular electron transfer mechanisms ruling the interaction between electroactive bacteria and potential solid-state electron acceptors. It also looks at the adoption of those principles for the development of MET set-ups for simultaneous wastewater treatment and power generation, and the challenges that the technology faces. Ultimately, the most recent developments in setups that merge MET with constructed wetlands are presented and discussed.

Continuum and discrete approach in modeling biofilm development and structure: a review

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions.

A grey box model of glucose fermentation and syntrophic oxidation in microbial fuel cells

In this work, the fermentative and oxidative processes taking place in a microbial fuel cell (MFC) fed with glucose were studied and modeled. The model accounting for the bioelectrochemical processes was based on ordinary, Monod-type differential equations. The model parameters were estimated using experimental results obtained from three H-type MFCs operated at open or closed circuits and fed with glucose or ethanol. The experimental results demonstrate that similar fermentation processes were carried out under open and closed circuit operation, with the most important fermentation products being ethanol (with a yield of 1.81molmol(-1) glucose) and lactic acid (with a yield of 1.36molmol(-1) glucose). A peak in the electricity generation was obtained when glucose and fermentation products coexisted in the liquid bulk. However, almost 90% of the electricity produced came from the oxidation of ethanol.

Metabolic modeling of spatial heterogeneity of biofilms in microbial fuel cells reveals substrate limitations in electrical current generation

Microbial fuel cells (MFCs) have been proposed as an alternative energy resource for the conversion of organic compounds to electricity. In an MFC, microorganisms such as Geobacter sulfurreducens form an anode-associated biofilm that can completely oxidize organic matter (electron donor) to carbon dioxide with direct electron transfer to the anode (electron acceptor). Mathematical models are useful in analyzing biofilm processes; however, existing models rely on Nernst-Monod type expressions, and evaluate extracellular processes separated from the intracellular metabolism of the microorganism. Thus, models that combine both extracellular and intracellular components, while addressing spatial heterogeneity, are essential for improved representation of biofilm processes. The goal of this work is to develop a model that integrates genome-scale metabolic models with the model of biofilm environment. This integrated model shows the variations of electrical current production and biofilm thickness under the presence/absence of NH4 in the bulk solution, and under varying maintenance energy demands. Further, sensitivity analysis suggested that conductivity is not limiting electrical current generation and that increasing cell density can lead to enhanced current generation. In addition, the modeling results also highlight instances such as the transformation into respiring cells, where the mechanism of electrical current generation during biofilm development is not yet clearly understood.

Arsenite oxidation and removal driven by a bio-electro-Fenton process under neutral pH conditions

The iron-catalyzed oxidation of arsenite (As(III)) associated with Fenton or Fenton-like reactions is one of the most efficient arsenic removal methods. However, the conventional chemical or electro-Fenton systems for the oxidation of As(III) are only efficient under acid conditions. In the present study, a cost-effective and efficient bio-electro-Fenton process was performed for As(III) oxidation in a dual-chamber microbial fuel cell (MFC) under neutral pH conditions. In such a system, the Fenton reagents, including H2O2 and Fe(II), were generated in situ by microbial-driven electro-reduction of O2 and γ-FeOOH, respectively, without an electricity supply. The results indicated that the process was capable of inducing As(III) oxidation with an apparent As(III) depletion first-order rate constant of 0.208 h(-1). The apparent oxidation current efficiency was calculated to be as high as 73.1%. The γ-FeOOH dosage in the cathode was an important factor in determining the system performance. Fourier-transform infrared spectroscopy (FT-IR) analysis indicated that As(V) was bound to the solid surface as a surface complex but not as a precipitated solid phase. The mechanism of bio-E-Fenton reaction for As(III) oxidation was also proposed. The bio-electro-Fenton system makes it potentially attractive method for the detoxification of As(III) from aqueous solution.

Application of the finite difference method to model pH and substrate concentration in a double-chamber microbial fuel cell

The purpose of this study was to develop a mathematical model that can describe glucose degradation in a microbial fuel cell (MFC) with the use of finite difference approach. The dynamic model can describe both substrate and pH changes in the anode chamber of a double-chamber MFC. It was developed using finite differences and incorporates basic mass transfer concepts. Model simulation results could fit the experimental data for substrate consumption well, while there was a moderate discrepancy (maximum 0.11 pH unit) between the simulated pH and the experimental data. A parametric sensitivity analysis showed that increases in acetate and propionate consumption rates can cause great decrease in chemical oxygen demand (COD) in the anode chamber, while an increase in glucose consumption rate does not result in significant changes of COD reduction. Therefore, the rate limitation steps of glucose degradation are the oxidations of secondary degradation products of glucose (acetate and propionate). Due to the buffering effect of the nutrient solution, the increases in glucose, acetate and propionate consumption rates did not result in much change on pH of the anode chamber.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance.