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Currently, global annual CO 2 emissions from fossil fuel consumption are extremely high, surpassing tens of billions of tons, yet our capacity to capture and utilize CO 2 remains below a small fraction of the amount generated. Microbial electrosynthesis (MES) systems, an integration of microbial metabolism with electrochemistry, have emerged as a highly efficient and promising bio-based carbon-capture-and-utilization technology over other conventional techniques. MES is a unique technology for lowering the atmospheric CO 2 as well as CO 2 in the biogas, and also simultaneously convert them to renewable bioenergy, such as biomethane. As such, MES techniques could be applied for biogas upgrading to generate high purity biomethane, which has the potential to meet natural gas standards. This article offers a detailed overview and assessment of the latest advancements in MES for biomethane production and biogas upgrading, in terms of selecting optimal methane production pathways and associated electron transfer processes, different electrode materials and types, inoculum sources and microbial communities, ion-exchange membrane, externally applied energy level, operating temperature and pH, mode of operation, CO 2 delivery method, selection of inorganic carbon source and its concentration, start-up time, and system pressure. It also highlights the current MES challenges associated with upscaling, design and configuration, long-term stability, energy demand, techno-economics, achieving net negative carbon emission, and other operational issues. Moreover, we provide a summary of current and future opportunities to integrate MES with other unique biosystems, such as methanotrophic bioreactors, and incorporate quorum sensing, 3D printing, and machine learning to further develop MES as a better biomethane-producer and biogas upgrading technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Hydrogen peroxide (H 2 O 2 ) 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 H 2 O 2 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 R 2 (0.983) and low root-mean-square error (RMSE) (0.647 kg H 2 O 2 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 H 2 O 2 production rate to as high as 9 kg m -3 d -1 ., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The land application of digestate from anaerobic digestion (AD) is considered a significant route for transmitting antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) to ecosystems. To date, efforts towards understanding complex non-linear interactions between AD operating parameters with ARG/MGE abundances rely on experimental investigations due to a lack of mechanistic models. Herein, three different machine learning (ML) algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were compared for their predictive capacities in simulating ARG/MGE abundance changes during AD. The models were trained and cross-validated using experimental data collected from 33 published literature. The comparison of model performance using coefficients of determination (R 2 ) and root mean squared errors (RMSE) indicated that ANN was more reliable than RF and XGBoost. The mode of operation (batch/semi-continuous), co-digestion of food waste and sewage sludge, and residence time were identified as the three most critical features in predicting ARG/MGE abundance changes. Moreover, the trained ANN model could simulate non-linear interactions between operational parameters and ARG/MGE abundance changes that could be interpreted intuitively based on existing knowledge. Overall, this study demonstrates that machine learning can enable a reliable predictive model that can provide a holistic optimization tool for mitigating the ARG/MGE transmission potential of AD., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interests or personal relationships that could influence this research work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The feasibility of enhancing quorum sensing (QS) in anode biofilm to improve the quantifications of commercial naphthenic acid concentrations (9.4-94 mg/L) in a microbial electrochemical cell (MXC) based biosensor was demonstrated in this study. First, three calibration methods were systematically compared, and the charging-discharging operation was selected for further experiments due to its 71-227 folds higher electrical signal outputs than the continuous closed-circuit operation and cyclic voltammetry modes. Then, the addition of acylase (5 μg/L) as an exogenous QS autoinducer (acylase) was investigated, which further improved the biosensor's electrical signal output by ∼70%, as compared to the control (without acylase). The addition of acylase increased the relative expression of QS-associated genes (lasR, lasI, rhlR, rhlI, lasA, and luxR) by 7-100%, along with increased abundances of known electroactive bacterial genera, such as Geobacter (from 42% to 47%) and Desulfovibrio (from 6% to 11%). Furthermore, toxicities of different NAs concentrations measured with the Microtox bioassay test were correlated with corresponding electrical signals, indicating that MXC-biosensor can provide a dual platform for rapid assessment of both NA concentrations and NA-associated toxicity., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The overuse and inappropriate disposal of antibiotics raised severe public health risks worldwide. Specifically, the incomplete antibiotics metabolism in human and animal bodies contributes to the significant release of antibiotics into the natural ecosystems and the proliferation of antibiotic-resistant bacteria carrying antibiotic-resistant genes. Moreover, the organic feedstocks used for anaerobic digestion are often highly-rich in residual antibiotics and antibiotic-resistant genes. Hence, understanding their fate during anaerobic digestion has become a significant research focus recently. Previous studies demonstrated that various process parameters could considerably influence the propagation of the antibiotic-resistant genes during anaerobic digestion and their transmission via land application of digestate. This review article scrutinizes the influences of process parameters on antibiotic-resistant genes propagation in anaerobic digestion and the inherent fundamentals behind their effects. Based on the literature review, critical research gaps and challenges are summarized to guide the prospects for future studies., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The development of low-cost analytical devices for on-site water quality monitoring is a critical need, especially for developing countries and remote communities in developed countries with limited resources. Microbial electrochemical cell-based (MXC) biosensors have been quite promising for quantitative and semi-quantitative (often qualitative) measurements of various water quality parameters due to their low cost and simplicity compared to traditional analytical methods. However, conventional MXC biosensors often encounter challenges, such as the slow establishment of biofilms, low sensitivity, and poor recoverability, making them unable to be applied for practical cases. In response, MXC biosensors assembled with paper-based materials demonstrated tremendous potentials to enhance sensitivity and field applicability. Furthermore, the paper-based platforms offer many prominent features, including autonomous liquid transport, rapid bacterial adhesion, lowered resistance, low fabrication cost (<$1 in USD), and eco-friendliness. Therefore, this review aims to summarize the current trend and applications of paper-based MXC biosensors, along with critical discussions on their field applicability. Moreover, future advancements of paper-based MXC biosensors, such as developing a novel paper-based biobatteries, increasing the system performance using an unique biocatalyst, such as yeast, and integrating the biosensor system with other advanced tools, such as machine learning and 3D printing, are highlighted., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Hydrogen peroxide (H 2 O 2 ) is an industrial chemical that has been widely adopted for various industrial applications, including water and wastewater treatment. Currently, the majority of H 2 O 2 is being produced through the anthraquinone oxidation process, which is disadvantageous due to the requirement of toxic raw materials and high energy input. Recently, microbial electrochemical cells (MXCs), such as microbial fuel cells and microbial electrolysis cells, have demonstrated great potential for effective H 2 O 2 production via cathodic oxygen-reduction reaction (ORR). Previous studies have specified key operational parameters for scaling-up of H 2 O 2 -producing MXCs, where improvements in production rate, conversion efficiency, product concentration and stability are attainable. Moreover, various systems have demonstrated their value proposition in the contaminant removal aspects through direct removal of various environmental pollutants, water disinfection, and many more. This review is intended to highlight promising ways of H 2 O 2 production with MXCs and on-site environmental applications of bioelectrochemically-produced H 2 O 2 ., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)