A Generalized Model for Complex Wastewater Treatment with Simultaneous Bioenergy Production Using the Microbial Electrochemical Cell

Microbial community analysis of the biofilms of both working and counter electrodes in single-chamber microbial electrolysis cells

This study was conducted to delineate microbial community development and composition on both working and counter electrodes in single-chamber microbial electrolysis cells (MECs) using synthetic wastewater. Two separate bioelectrochemical reactors were inoculated with anaerobic sludge. The first was operated at an anodic potential poised at + 0.4 V and the second one at a cathodic potential poised at -0.7 V, both vs. an Ag/AgCl reference electrode. The performance of the MECs, including current generation, bioelectrochemical activity of the biofilms on both the working and counter electrodes, and chemical oxygen demand (COD) depletion were monitored over the last 45 days of operation. Scanning electron microscopy (SEM) and 16S rRNA gene sequencing were performed to delineate the development and morphology of the microbial communities on both the working and the counter electrodes. The current generated at the anodic working electrode provided evidence of the growth of anode-respiring exoelectrogens (Clostridium sensu stricto). Similarly, the Faradaic current data at the cathodic working electrode confirmed the formation of an electroactive biofilm dominated by acetoclastic and hydrogenotrophic methanogens (Methanothrix and Methanobacterium). Microbial communities on the counter electrodes were found to be richer but less diverse compared to those on the working electrodes. These communities were likely influenced by the fluctuating potentials at the counter electrodes. SEM observations were consistent with the microbial analysis. These findings demonstrate the ability of a mixed inoculum to shift towards anode-reducing and cathode methanogenic communities using a complex substrate on a constant working electrode and varying counter electrode potentials.

Nickel silicide nanowire anodes for microbial fuel cells to advance power production and charge transfer efficiency in 3D configurations

The growing energy demands of the industrial world have driven advancements in green energy technologies. Microbial fuel cells (MFCs), which harness power from microorganisms, show promise for energy extraction from wastewater and sludge. However, challenges remain in improving power output and sustaining performance under high-charge conditions. Incorporating nanomaterials into 3D structures offers potential solutions, including miniaturized designs. This study introduces nickel silicide nanowires as anode materials for MFCs. Synthesized on nickel foam, these nanowires form a 3D nickel-based structure with semi-metal nanostructures. Tested in a microfluidic MFC system with E. coli, this configuration achieved significant improvements, including a peak power density of 323 mW m-2 and a current density of 2.24 A m-2, representing a 2.5-fold increase in power and a 4-fold boost in current compared to bare nickel foam. Nutrient broth proved the most effective charge transfer medium, surpassing glucose and urea by 3 and 5 times, respectively. These results, supported by EIS and SEM analyses, highlight the role of nanowires in enhancing charge transfer and sustaining high-current performance. The presented 3D nickel-based configuration anode offers advancements in microbial fuel cell technology, providing a foundation for further enhancements and applications in energy harvesting systems.

Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field.

Boosting microfluidic microbial fuel cells performance via investigating electron transfer mechanisms, metal-based electrodes, and magnetic field effect

The presented paper fundamentally investigates the influence of different electron transfer mechanisms, various metal-based electrodes, and a static magnetic field on the overall performance of microfluidic microbial fuel cells (MFCs) for the first time to improve the generated bioelectricity. To do so, as the anode of microfluidic MFCs, zinc, aluminum, tin, copper, and nickel were thoroughly investigated. Two types of bacteria, Escherichia coli and Shewanella oneidensis MR-1, were used as biocatalysts to compare the different electron transfer mechanisms. Interaction between the anode and microorganisms was assessed. Finally, the potential of applying a static magnetic field to maximize the generated power was evaluated. For zinc anode, the maximum open circuit potential, current density, and power density of 1.39 V, 138,181 mA m^-2 and 35,294 mW m^-2 were obtained, respectively. The produced current density is at least 445% better than the values obtained in previously published studies so far. The microfluidic MFCs were successfully used to power ultraviolet light-emitting diodes (UV-LEDs) for medical and clinical applications to elucidate their application as micro-sized power generators for implantable medical devices.

Modelling the cathodic reduction of 2,4-dichlorophenol in a microbial fuel cell

This work presents a simplified mathematical model able to predict the performance of a microbial fuel cell (MFC) for the cathodic dechlorination of 2,4-dichlorophenol (2,4-DCP) operating at different cathode pH values (7.0 and 5.0). Experimental data from previous work were utilized for the fitting of the model. The MFC modelled consisted of two chambers (bioanode and abiotic cathode), wherein the catholyte contained 300 mg L⁻¹ of 2,4-DCP and the anolyte 1000 mg L⁻¹ of sodium acetate. The model considered two mixed microbial populations in the anode compartment using sodium acetate as the carbon source for growth and maintenance: electrogenic and non-electrogenic biomass. 2,4-DCP, its intermediates of the reductive process (2-chlorophenol, 2-CP and 4-chlorophenol, 4-CP) and protons were considered in the model as electron acceptors in the electrogenic mechanism. The global process rate was assumed to be controlled by the biological mechanisms and modelled using multiplicative Monod-type equations. The formulation of a set of differential equations allowed to describe the simultaneous evolution of every component: concentration of sodium acetate in the anodic compartment; and concentration of 2,4-DCP, 2-CP, 4-CP, phenol and chloride in the cathode chamber. Current production and coulombic efficiencies were also estimated from the fitting. It was observed that most of the organic substrate was used by non-electrogenic mechanism. The influence of the Monod parameters was more important than the influence of the biomass yield coefficients. Finally, the model was employed to simulate different scenarios under distinct experimental conditions.

A robust correlation based on dimensional analysis to characterize microbial fuel cells

We present a correlation for determining the power density of microbial fuel cells based on dimensional analysis. Important operational, design and biological parameters are non-dimensionalized using a selection of scaling variables. Experimental data from various microbial fuel cell studies operating over a wide range of system parameters are analyzed to attest accuracy of the model in predicting power output. The correlation predicts nonlinear dependencies between power density, substrate concentration, solution conductivity, external resistance, and electrode spacing. The straightforward applicability without the need for any significant computational resources, while preserving good level of accuracy; makes this correlation useful in focusing the experimental effort for the design and optimization of microbial fuel cells.

Life cycle, techno-economic and dynamic simulation assessment of bioelectrochemical systems: A case of formic acid synthesis

A novel framework, integrating dynamic simulation (DS), life cycle assessment (LCA) and techno-economic assessment (TEA) of a bioelectrochemical system (BES), has been developed to study for the first time wastewater treatment by removal of chemical oxygen demand (COD) by oxidation in anode and thereby harvesting electron and proton for carbon dioxide reduction reaction or reuse to produce products in cathode. Increases in initial COD and applied potential increase COD removal and production (in this case formic acid) rates. DS correlations are used in LCA and TEA for holistic performance analyses. The cost of production of HCOOH is €0.015-0.005 g^-1 for its production rate of 0.094-0.26 kg yr^-1 and a COD removal rate of 0.038-0.106 kg yr^-1. The life cycle (LC) benefits by avoiding fossil-based formic acid production (93%) and electricity for wastewater treatment (12%) outweigh LC costs of operation and assemblage of BES (-5%), giving a net 61MJkg^-1 HCOOH saving.

A novel model for predicting bioelectrochemical performance of microsized-MFCs by incorporating bacterial chemotaxis parameters and simulation of biofilm formation

Bacterial transport parameters play a fundamental role in microbial population dynamics, biofilm formation and bacteria dispersion. In this study, the novel model was extended based on the capability of microsized microbial fuel cells (MFCs) as amperometric biosensors to predict the cells' chemotactic and bioelectrochemical properties. The model prediction results coincide with the experimental data of Shewanella oneidensis and chemotaxis mutant of P. aeruginosa bdlA and pilT strains, indicating the complementary role of numerical predictions for bioscreening applications of microsized MFCs. Considering the general mechanisms for electron transfer, substrate biodegradation, microbial growth and bacterial dispersion are the main features of the presented model. In addition, the genetic algorithm method was implemented by minimizing the objective function to estimate chemotaxis properties of the different strains. Microsized MFC performance was assessed by analyzing the microbial activity in the biofilm and the anolyte.

Microbial fuel cell-photoelectrocatalytic cell combined system for the removal of azo dye wastewater

In this study, a novel parallel circuit microbial fuel cell-photoelectrocatalytic cell (MFC-PEC) combined system was established to enhance azo dye removal. Results showed that this system had synergistic effects compared with the MFC alone. In the MFC part, a 56% decrease in chemical oxygen demand (COD) and 85% decolorization were achieved, and further reduced by 25% and 12% in the PEC part where titania nanotube functioned as the photoelectrode. For one thing, the PEC raised the maximum current of the MFC by 14.2%, which facilitated COD removal and decolorization in the MFC and promoted adenosine triphosphate (ATP) level of anode microorganisms, for another, this system significantly increased the dye removal in the PEC. Besides, cyclic voltammograms illustrated intermediate products degradation in this system. Hence, the system achieved marked deep decolorization and rapid toxic intermediate products degradation of high concentration azo dyes.

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.

Characterization of a microfluidic microbial fuel cell as a power generator based on a nickel electrode

This study reports the fabrication of a microfluidic microbial fuel cell (MFC) using nickel as a novel alternative for conventional electrodes and a non-phatogenic strain of Escherichia coli as the biocatalyst. The feasibility of a microfluidic MFC as an efficient power generator for production of bioelectricity from glucose and urea as organic substrates in human blood and urine for implantable medical devices (IMDs) was investigated. A maximum open circuit potential of 459 mV was achieved for the batch-fed microfluidic MFC. During continuous mode operation, a maximum power density of 104 Wm(-3) was obtained with nutrient broth. For the glucose-fed microfluidic MFC, the maximum power density of 5.2 μW cm(-2) obtained in this study is significantly greater than the power densities reported previously for microsized MFCs and glucose fuel cells. The maximum power density of 14 Wm(-3) obtained using urea indicates the successful performance of a microfluidic MFC using human excreta. It features high power density, self-regeneration, waste management and a low production cost (<$1), which suggest it as a promising alternative to conventional power supplies for IMDs. The performance of the microfluidic MFC as a power supply was characterized based on polarization behavior and cell potential in different substrates, operational modes, and concentrations.