Microbial Community Predicts Functional Stability of Microbial Fuel Cells

Exploring the function of key species in different composting stages for effective waste biotransformation

Composting is a microbial-driven process that plays a vital role in recycling waste and promoting sustainable production. To develop more effective bioaugmentation strategies, this study examined three successive stages in an aerobic composting system, focusing on microbial community adaptation to high-temperature stress (mode_2) and nutrient-poor conditions (mode_3). The results revealed a shift from an r-strategy (rapid growth) to a K-strategy (thriving under resource-limited conditions). Community succession was predominantly driven by deterministic processes (>90 %) and exhibited strong cooperative interactions. Using multiple statistical approaches, key species were identified for each condition. These species enhanced microbial network connectivity under environmental stresses, increasing network edges by 29 %-35 %. Under high-temperature stress, Bacillus and Ureibacillus maintained core functions, while Chelativorans and Aeribacillus contributed to key metabolic pathways, including amino acid metabolism. In nutrient-poor conditions, Saccharomonospora and Pseudoxanthomonas enhanced overall system functionality, and Novibacillus played a key role in carbon and nitrogen cycling, particularly nitrogen fixation. Predictive models for microbial community stability (R2 = 0.68-0.97) were developed based on these key species to enable rapid assessment of system stability. Overall, this study identifies essential microbes involved in composting across different environmental conditions and clarifies their functional roles, providing valuable insights for optimizing aerobic composting efficiency and advancing waste resource management.

Evolution of interspecific interactions underlying the nonlinear relationship between active biomass and pollutant degradation capacity in bioelectrochemical systems

This study proposes a switching operating mode that alternates between microbial fuel cell (MFC) and microbial electrolysis cell (MEC) to restore the biofilm activity and organic pollutant degradation capacity in bioelectrochemical systems (BESs) during prolonged operation. After the model switching, the toluene degradation kinetics in BESs equipped with graphite sheet (GS) and polyaniline@carbon nanotubes (PANI@CNTs) bioanodes were elevated by 2.10 and 3.14 times, respectively. Nevertheless, the amount of active biomass in the GS and PANI@CNTs bioanodes only increased by 1.04 and 1.05 times, with the PANI@CNTs bioanode consistently outperforming in hierarchical biofilm activity and redox properties. Additionally, the distribution of functional genes across the dominant genera revealed their roles in extracellular electron transfer and the four steps of toluene degradation (primary oxidation, ring-opening, intermediate oxidation, and tricarboxylic acid cycle). Furthermore, the cooperation of substrate exchange among Pseudomonas, Alicycliphilus, and Acidovorax in the MFC mode evolved to interactions among Acidovorax, Alicycliphilus, and Geobacter in the MEC mode, which attributed to the nonlinear relationship between active biomass and pollutant degradation capacity. These results provide insights into the operating mode and interspecific interactions of BESs, with implications for practical applications.

The Role of Anode Potential in Electromicrobiology

Electroactive microorganisms are capable of exchanging electrons with electrodes and thus have potential applications in many fields, including bioenergy production, microbial electrochemical synthesis of chemicals, environmental protection, and microbial electrochemical sensors. Due to the limitations of low electron transfer efficiency and poor stability, the application of electroactive microorganisms in industry is still confronted with significant challenges. In recent years, many studies have demonstrated that modulating anode potential is one of the effective strategies to enhance electron transfer efficiency. In this review, we have summarized approximately 100 relevant studies sourced from PubMed and Web of Science over the past two decades. We present the classification of electroactive microorganisms and their electron transfer mechanisms and elucidate the impact of anode potential on the bioelectricity behavior and physiology of electroactive microorganisms. Our review provides a scientific basis for researchers, especially those who are new to this field, to choose suitable anode potential conditions for practical applications to optimize the electron transfer efficiency of electroactive microorganisms, thus contributing to the application of electroactive microorganisms in industry.

Leveraging microbial synergy: Predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN

Microbial Fuel Cells (MFCs) are a sophisticated and advanced system that uses exoelectrogenic microorganisms to generate bioenergy. Predicting performance outcomes under experimental settings is challenging due to the intricate interactions that occur in mixed-species bioelectrochemical reactors like MFCs. One of the key factors that limit the MFC's performance is the presence of a microbial consortium. Traditionally, multiple microbial consortia are implemented in MFCs to determine the best consortium. This approach is laborious, inefficient, and wasteful of time and resources. The increase in the availability of soft computational techniques has allowed for the development of alternative strategies like artificial intelligence (AI) despite the fact that a direct correlation between microbial strain, microbial consortium, and MFC performance has yet to be established. In this work, a novel generic AI model based on subspace k-Nearest Neighbour (SS-kNN) is developed to identify and forecast the best microbial consortium from the constituent microbes. The SS-kNN model is trained with thirty-five different microbial consortia sharing different effluent properties. Chemical oxygen demand (COD) reduction, voltage generation, exopolysaccharide (EPS) production, and standard deviation (SD) of voltage generation are used as input features to train the SS-kNN model. The proposed SS-kNN model offers an accuracy of 100% during training period and 85.71% when it is tested with the data obtained from existing literature. The implementation of selected consortium (as predicted by SS-kNN model) improves the COD reduction capability of MFC by 15.67% than that of its constituent microbes which is experimentally verified. In addition, to prevent the effects of climate change and mitigate water pollution, the implementation of MFC technology ensures clean and green electricity. Consequently, achieving sustainable development goals (SDG) 6, 7, and 13.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Application of biochar in microbial fuel cells: Characteristic performances, electron-transfer mechanism, and environmental and economic assessments

Biochar is a by-product of thermochemical conversion of biomass or other carbonaceous materials. Recently, it has garnered extensive attention for its high application potential in microbial fuel cell (MFC) systems owing to its high conductivity and low cost. However, the effects of biochar on MFC system performance have not been comprehensively reviewed, thereby necessitating the evaluation of the efficacy of biochar application in MFCs. In this review, biochar characteristics were outlined based on recent publications. Subsequently, various applications of biochar in the MFC systems and their probable processes were summarized. Finally, proposals for future applications of biochar in MFCs were explored along with its perspectives and an environmental evaluation in the context of a circular economy. The purpose of this review is to gain comprehensive insights into the application of biochar in the MFC systems, offering important viewpoints on the effective and steady utilization of biochar in MFCs for practical application.

Biofilm metagenomic characteristics behind high coulombic efficiency for propanethiol deodorization in two-phase partitioning microbial fuel cell

Hydrophobic volatile organic sulfur compounds (VOSCs) are frequently found during sewage treatment, and their effective management is crucial for reducing malodorous complaints. Microbial fuel cells (MFC) are effective for both VOSCs abatement and energy recovery. However, the performance of MFC on VOSCs remains limited by the mass transfer efficiency of MFC in aqueous media. Inspired by two-phase partitioning biotechnology, silicone oil was introduced for the first time into MFC as a non-aqueous phase (NAP) medium to construct two-phase partitioning microbial fuel cell (TPPMFC) and augment the mass transfer of target VOSCs of propanethiol (PT) in the liquid phase. The PT removal efficiency within 32 h increased by 11-20% compared with that of single-phase MFC, and the coulombic efficiency of TPPMFC (11.01%) was 4.32-2.68 times that of single-phase MFC owing to the fact that highly active desulfurization and thiol-degrading bacteria (e.g., Pseudomonas, Achromobacter) were attached to the silicone oil surface, whereas sulfur-oxidizing bacteria (e.g., Thiobacillus, Commonas, Ottowia) were dominant on the anodic biofilm. The outer membrane cytochrome-c content and NADH dehydrogenase activity improved by 4.15 and 3.36 times in the TPPMFC, respectively. The results of metagenomics by KEGG and COG confirmed that the metabolism of PT in TPPMFC was comprehensive, and that the addition of a NAP upregulates the expression of genes related to sulfur metabolism, energy generation, and amino acid synthesis. This finding indicates that the NAP assisted bioelectrochemical systems would be promising to solve mass-transfer restrictions in low solubility contaminates removal.

Energy generation from bioelectrochemical techniques: Concepts, reactor configurations and modeling approaches

The process industries play a significant role in boosting the economy of any nation. However, poor management in several industries has been posing worrisome threats to an environment that was previously immaculate. As a result, the untreated waste and wastewater discarded by many industries contain abundant organic matter and other toxic chemicals. It is more likely that they disrupt the proper functioning of the water bodies by perturbing the sustenance of many species of flora and fauna occupying the different trophic levels. The simultaneous threats to human health and the environment, as well as the global energy problem, have encouraged a number of nations to work on the development of renewable energy sources. Hence, bioelectrochemical systems (BESs) have attracted the attention of several stakeholders throughout the world on many counts. The bioelectricity generated from BESs has been recognized as a clean fuel. Besides, this technology has advantages such as the direct conversion of substrate to electricity, and efficient operation at ambient and even low temperatures. An overview of the BESs, its important operating parameters, bioremediation of industrial waste and wastewaters, biodegradation kinetics, and artificial neural network (ANN) modeling to describe substrate removal/elimination and energy production of the BESs are discussed. When considering the potential for use in the industrial sector, certain technical issues of BES design and the principal microorganisms/biocatalysts involved in the degradation of waste are also highlighted in this review.

Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system

Composting, reliant on microorganisms, effectively treats kitchen waste. However, it is difficult to precisely understand the specific role of key microorganisms in the composting process by relying solely on experimental research. This study aims to employ machine learning models to explore key microbial genera and to optimize composting systems. After introducing a novel microbiome preprocessing approach, Stacking models were constructed (R2 is about 0.8). The SHAP method (SHapley Additive exPlanations) identified Bacillus, Acinetobacter, Thermobacillus, Pseudomonas, Psychrobacter, and Thermobifida as prominent microbial genera (Shapley values ranging from 3.84 to 1.24). Additionally, microbial agents were prepared to target the identified key genera, and experiments demonstrated that the composting quality score was 76.06 for the treatment and 70.96 for the control. The exogenous agents enhanced decomposition and improved compost quality in later stages. In summary, this study opens up a new avenue to identifying key microorganisms and optimizing the biological treatment process.

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.

Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production

Hydrogen peroxide (H₂O₂) production in microbial electrochemical systems (MESs) is an attractive option for enabling a circular economy in the water/wastewater sector. Here, a machine learning algorithm was developed, using a meta-learning approach, to predict the H₂O₂ production rates in MES based on the seven input variables, including various design and operating parameters. The developed models were trained and cross-validated using the experimental data collected from 25 published reports. The final ensemble meta-learner model (combining 60 models) demonstrated a high prediction accuracy with very high R² (0.983) and low root-mean-square error (RMSE) (0.647 kg H₂O₂ m^-3 d^-1) values. The model identified the carbon felt anode, GDE cathode, and cathode-to-anode volume ratio as the top three most important input features. Further scale-up analysis for small-scale wastewater treatment plants indicated that proper design and operating conditions could increase the H₂O₂ production rate to as high as 9 kg m^-3 d^-1.

Universal Dynamics of Microbial Communities in Full-Scale Textile Wastewater Treatment Plants and System Prediction by Machine Learning

The performance of full-scale biological wastewater treatment plants (WWTPs) depends on the operational and environmental conditions of treatment systems. However, we do not know how much these conditions affect microbial community structures and dynamics across systems over time and predictability of the treatment performance. For over a year, the microbial communities of four full-scale WWTPs processing textile wastewater were monitored. During temporal succession, the environmental conditions and system treatment performance were the main drivers, which explained up to 51% of community variations within and between all plants based on the multiple regression models. We identified the universality of community dynamics in all systems using the dissimilarity-overlap curve method, with the significant negative slopes suggesting that the communities containing the same taxa from different plants over time exhibited a similar composition dynamic. The Hubbell neutral theory and the covariance neutrality test indicated that all systems had a dominant niche-based assembly mechanism, supporting that the communities had a similar composition dynamic. Phylogenetically diverse biomarkers for the system conditions and treatment performance were identified by machine learning. Most of the biomarkers (83%) were classified as generalist taxa, and the phylogenetically related biomarkers responded similarly to the system conditions. Many biomarkers for treatment performance perform functions that are crucial for wastewater treatment processes (e.g., carbon and nutrient removal). This study clarifies the relationships between community composition and environmental conditions in full-scale WWTPs over time.

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Prediction and optimization of medium-chain carboxylic acids production from food waste using machine learning models

Machine learning models were developed in this study to predict and optimize the medium-chain carbolic acids (MCCAs) production from food waste. All three selected prediction algorithms achieved decent performance (accuracy > 0.85, R² > 0.707). Three optimization algorithms were applied for MCCA production optimization based on the prediction algorithms. The maximum MCCA production rate (0.68 g chemical oxygen demand per liter per day) was achieved by simulated annealing coupled with random forest under the optimal conditions of pH 8.3, temperature 50 °C, retention time 4 days, loading rate 15.8 g volatile solid per liter per day, and inoculum to food waste ratio 70:30 with semi-continuous mode. Further experiments validated (18 % error) that the MCCA production rate was 113 % higher than the highest production rate of current lab experiments and 60 % higher than the statistical optimization using response surface methodology. This study demonstrates the potential of using machine learning for MCCA production prediction and optimization.

The diagnostic and prognostic potential of gut bacteria in inflammatory bowel disease

The gut microbiome serves as a signaling hub that integrates environmental inputs with genetic and immune signals to influence the host's metabolism and immunity. Gut bacteria are intricately connected with human health and disease state, with specific bacteria species driving the characteristic dysbiosis found in gastrointestinal conditions such as inflammatory bowel disease (IBD); thus, gut bacteria changes could be harnessed to improve IBD diagnosis, prognosis, and treatment. The advancement in next-generation sequencing techniques such as 16S rRNA and whole-genome shotgun sequencing has allowed the exploration of the complexity of the gut microbial ecosystem with high resolution. Current microbiome data is promising and appears to perform better in some studies than the currently used fecal inflammation biomarker, calprotectin, in predicting IBD from healthy controls and irritable bowel syndrome (IBS). This study reviews current data on the differential potential of gut bacteria within IBD cohorts, and between IBD and other gastrointestinal diseases.

Anaerobic Digestion of Chicken Manure in the Presence of Magnetite, Granular Activated Carbon, and Biochar: Operation of Anaerobic Reactors and Microbial Community Structure

The influence of magnetite nanoparticles, granular activated carbon (GAC), and biochar, as well as their combinations on the anaerobic digestion of chicken manure and the structure of microbial communities was studied. The addition of magnetite, GAC, and biochar increased the rate of methane production and the total methane yield. It has been observed that these additives stimulated anaerobic microorganisms to reduce the concentration of accumulated volatile organic acids. Various bacterial species within the classes Bacteroidia and Clostridia were found at higher levels in the anaerobic reactors but in different proportions depending on the experiment. Members of the genera Methanosarcina, Methanobacterium, Methanothrix, and Methanoculleus were mainly detected within the archaeal communities in the anaerobic reactors. Compared to the 16S rRNA gene-based study, the mcrA gene approach allowed a higher level of Methanosarcina in the system with GAC + magnetite to be detected. Based on our findings, the combined use of granular activated carbon and magnetite at appropriate dosages will improve biomethane production.

Co, N co-doped hierarchical porous carbon as efficient cathode electrocatalyst and its impact on microbial community of anode biofilm in microbial fuel cell

The exploration of low-cost, long-term stable, and highly electrochemically active cathode catalysts is important for the practical application of microbial fuel cell (MFC). In this work, a series of the 3D hierarchical porous Co-N-C (3DHP Co-N-C) materials are designed and synthesized by a metal-organic framework ZIF-67 as a precursor and SiO₂ sphere of different sizes as the hard template. The 3DHP Co-N-C-2 with 129 nm macropore exhibits excellent ORR performance in 0.1 M KOH solution with a half-wave potential of 0.80 V vs. RHE and superior durability than Pt/C (20%) due to the specific macropore-mesopore-micropore structure that exposes a large number of active sites and accelerates the electrolyte transport and oxygen diffusion. The MFC with 3DHP Co-N-C-2 as the cathode catalysts shows excellent performance with a maximum power density of 426.9±7.87 mW m^-2 and favorable durability after 50 d of operation. In addition, 16s rDNA results reveal the presence of different dominant electrogenic bacteria and different abundance of important non-electrogenic bacteria in the anode biofilm in MFCs using cathode catalysts with different ORR activity. And 3DHP Co-N-C-2 was found to be beneficial to the synergistic effect of electrogenic and non-electrogenic bacteria. This study explores electrocatalysts in terms of both electrocatalytic activity and anode microorganisms, providing new and comprehensive insights into the power generation of MFC.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Leveraging artificial intelligence in bioelectrochemical systems

Bioelectrochemical systems (BESs) are highly evolved and sophisticated systems that produce bioenergy via exoelectrogenic microbes. Artificial intelligence (AI) helps to understand, relate, model, and predict both process parameters and microbial diversity, resulting in higher performance. This approach has revolutionized BESs through highly advanced computational algorithms that best suit the systems' architecture.

Linking population dynamics to microbial kinetics for hybrid modeling of bioelectrochemical systems

Mechanistic and data-driven models have been developed to provide predictive insights into the design and optimization of engineered bioprocesses. These two modeling strategies can be combined to form hybrid models to address the issues of parameter identifiability and prediction interpretability. Herein, we developed a novel and robust hybrid modeling strategy by incorporating microbial population dynamics into model construction. The hybrid model was constructed using bioelectrochemical systems (BES) as a platform system. We collected 77 samples from 13 publications, in which the BES were operated under diverse conditions, and performed holistic processing of the 16S rRNA amplicon sequencing data. Community analysis revealed core populations composed of putative electroactive taxa Geobacter, Desulfovibrio, Pseudomonas, and Acinetobacter. Primary Bayesian networks were trained with the core populations and environmental parameters, and directed Bayesian networks were trained by defining the operating parameters to improve the prediction interpretability. Both networks were validated with Bray-Curtis similarly, relative root-mean-square error (RMSE), and a null model. A hybrid model was developed by first building a three-population mechanistic component and subsequently feeding the estimated microbial kinetic parameters into network training. The hybrid model generated a simulated community that shared a Bray-Curtis similarity of 72% with the actual microbial community at the genus level and an average relative RMSE of 7% for individual taxa. When examined with additional samples that were not included in network training, the hybrid model achieved accurate prediction of current production with a relative error-based RMSE of 0.8 and outperformed the data-driven models. The genomics-enabled hybrid modeling strategy represents a significant step toward robust simulation of a variety of engineered bioprocesses.

Enhancement of microbiome management by machine learning for biological wastewater treatment

Here, we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.

Efficacy of electrode position in microbial fuel cell for simultaneous Cr(VI) reduction and bioelectricity production

Microbial fuel cells (MFCs) that are bio-energy transducers capture bioelectricity produced from the oxidation of organic matter by using the electro-active bacteria grown on the biofilm attached on anode. Previous studies explored the effect of several limiting factors, such as electrode material, catalyst type, membrane structure, and electrolyte, on the electrochemical performance of MFCs. However, the effects of electrode position on Cr(VI) reduction and bioelectricity production remain unknown. In this study, MFCs with different electrode positions (i.e., 4 cm (MFC-4), 3 cm (MFC-3), 2 cm (MFC-2), and 1 cm (MFC-1)) were designed and fabricated to evaluate the overall performance of MFCs. The results of electrochemical analysis confirmed that MFC-2 exhibited low exchange transfer resistance (4.9 Ω) and strong conductivity, resulting in optimal electrochemical performance. In addition, Cr(VI) was completely removed within 11 h in MFC-2 with a large reduction rate of 0.91 g/m³·h. and COD removal efficiency of 78.25%. The overall performance of MFC-2 was comparatively higher than those of MFC-1 (0.80 g/m³·h and 68.82%), MFC-3 (0.64 g/m³·h and 61.67%), and MFC-4 (0.52 g/m³·h and 39.85%). Meanwhile, MFC-2 generated high open voltage (1.02 V) and power density (535.4 mW/m²), which are 1.4- and 3.1-fold larger than those of MFC-4 (0.72 V and 171.3 mW/m²). High COD removal and power density indicated the strong electrochemical activity of electroactive bacteria in the anode chamber of the MFCs, which was due to the low resistance in the MFCs could accelerate electron transfer and boost electrochemical reaction. Consequently, the optimal electrode spacing in MFCs was 2 cm. Further studies confirmed that Cr(VI) was removed and deposited in the form of Cr(III) on the electrode surface. High-throughput analysis suggested Pseudomonas species are the key electroactive bacteria for electricity generation.