Recent innovations for scaling up microbial fuel cell systems: Significance of physicochemical factors for electrodes and membranes materials

Nanofiber applications in microbial fuel cells for enhanced energy generation: a mini review

Microbial fuel cells (MFCs) represent simple devices that harness the metabolic activities of microorganisms to produce electrical energy from diverse sources such as organic waste and sustainable biomass. Because of their unique advantage to generate sustainable energy, through the employment of biodegradable and repurposed waste materials, the development of MFCs has garnered considerable interest. Critical elements are typically the electrodes and separator. This mini-review article presents a critical assessment of nanofiber technology used as electrodes and separators in MFCs to enhance energy generation. In particular, the review highlights the application of nanofiber webs in each part of MFCs including anodes, cathodes, and membranes and their influence on energy generation. The role of nanofiber technology in this regard is then analysed in detail, focusing on improved electron transfer rate, enhanced biofilm formation, and enhanced durability and stability. In addition, the challenges and opportunities associated with integrating nanofibers into MFCs are discussed, along with suggestions for future research in this field. Significant developments in MFCs over the past decade have led to a several-fold increase in achievable power density, yet further improvements in performance and the exploration of cost-effective materials remain promising areas for further advancement. This review demonstrates the great promise of nanofiber-based electrodes and separators in future applications of MFCs.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers

CONTEXT

Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies.

METHODS

To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis.

Effect of Sulfonated Inorganic Additives Incorporated Hybrid Composite Polymer Membranes on Enhancing the Performance of Microbial Fuel Cells

Microbial fuel cells (MFCs) provide considerable benefits in the energy and environmental sectors for producing bioenergy during bioremediation. Recently, new hybrid composite membranes with inorganic additives have been considered for MFC application to replace the high cost of commercial membranes and improve the performances of cost-effective polymers, such as MFC membranes. The homogeneous impregnation of inorganic additives in the polymer matrix effectively enhances the physicochemical, thermal, and mechanical stabilities and prevents the crossover of substrate and oxygen through polymer membranes. However, the typical incorporation of inorganic additives in the membrane decreases the proton conductivity and ion exchange capacity. In this critical review, we systematically explained the impact of sulfonated inorganic additives (such as (sulfonated) sSiO₂, sTiO₂, sFe₃O₄, and s-graphene oxide) on different kinds of hybrid polymers (such as PFSA, PVDF, SPEEK, SPAEK, SSEBS, and PBI) membrane for MFC applications. The membrane mechanism and interaction between the polymers and sulfonated inorganic additives are explained. The impact of sulfonated inorganic additives on polymer membranes is highlighted based on the physicochemical, mechanical, and MFC performances. The core understandings in this review can provide vital direction for future development.

Energy Level Prediction of Organic Semiconductors for Photodetectors and Mining of a Photovoltaic Database to Search for New Building Units

Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields.

Spontaneous intra-electron transfer within rGO@Fe2O3-MnO catalyst promotes long-term NOx reduction at ambient conditions

Iron (Fe)-based catalysts are widely used for taming nitrogen oxides (NO_x) containing flue gas, but the regeneration and long-term reusability remains a concern. The reusability can be acquired by external additives, and resultantly can not only increase the cost but can also add to process complexity as well as secondary pollutants. Herein, a self-sustainable material is designed to regenerate the catalyst for long-term reusability without adding to process complexity. The catalyst is based on reduced graphene-oxide impregnated by Fe₂O₃-MnO (rGO@Fe₂O₃-MnO; G-F-M) for spontaneous intra electron (e^-)-transfer from Mn to Fe. The developed catalyst; G-M-F exhibited 93.7% NO_x reduction, which suggests its high catalytic activity. The morphological and structure characterizations confirmed the Fe/Mn loading, contributing to e^--transfer between Mn and Fe due to its conductivity. The synthesized G-F-M showed higher NO_x reduction about 2.5 folds, than rGO@Fe₂O₃ (G-FeO) and rGO@MnO_x (G-MnO_x). The performance of G-M-F without and with an electrochemical system was also compared, and the difference was only 5%, which is an evidence of the spontaneous e^- transfer between the Mn and Fe-NO_x complex. The designed catalyst can be used for a long time without external assistance, and its efficiency was not affected significantly (<3.7%) in the presence of high oxygen contents (8%). The as-prepared G-M-F catalyst has great potential for executing a dual role NOx removal and self-regeneration of catalyst (SRC), promoting a sustainable remediation approach for large-scale applications.

Biopolymer-based electrospun fibers in electrochemical devices: versatile platform for energy, environment, and health monitoring

Electrochemical power tools are regarded as essential keys in a world that is becoming increasingly reliant on fossil fuels in order to meet the challenges of rapidly depleting fossil fuel supplies. Additionally, due to the industrialization of societies and the growth of diseases, the need for sensitive, reliable, inexpensive, and portable sensors and biosensors for noninvasive monitoring of human health and environmental pollution is felt more than ever before. In recent decades, electrospun fibers have emerged as promising candidates for the fabrication of highly efficient electrochemical devices, such as actuators, batteries, fuel cells, supercapacitors, and biosensors. Meanwhile, the use of synthetic polymers in the fabrication of versatile electrochemical devices has raised environmental concerns, leading to an increase in the quest for natural polymers. Natural polymers are primarily derived from microorganisms and plants. Despite the challenges of processing bio-based electrospun fibers, employing natural nanofibers in the fabrication of electrochemical devices has garnered tremendous attention in recent years. Here, various natural polymers and the strategies employed to fabricate various electrospun biopolymers are briefly covered. The recent advances and research strategies used to apply the bio-based electrospun membranes in different electrochemical devices are carefully summarized, along with the scopes in various advanced technologies. A comprehensive and critical discussion about the use of biopolymer-based electrospun fibers as the potential alternative to non-renewable ones in future technologies is briefly highlighted. This review will serve as a field opening platform for using different biopolymer-based electrospun fibers to advance the electrochemical device-based renewable and sustainable technologies, which will be of high interest to a large community. Accordingly, future studies should focus on feasible and cost-effective extraction of biopolymers from natural resources as well as fabrication of high-performance nanofibrous biopolymer-based components applicable in various electrochemical devices.

Scale-up of the bioelectrochemical system: Strategic perspectives and normalization of performance indices

Electrochemists and ecological engineers find environmental bioelectrochemistry appealing; however, there is a big gap between expectations and actual progress in bioelectrochemical system (BES). Implementing such technology opens new opportunities for novel electrochemical reactions for resource recovery and effective wastewater treatment. Loopholes of BES exist in its scaling-up applications, and numerous attempts toward practical applications (200, 1000, and 1500 L) are key successive indicators toward its commercialization. This review emphasized the critical rethinking of standardization of performance indices i.e. current generation (A/m²), net energy recovery (kWh/kg·COD), product/resource yield (mM), and economic feasibility ($/kWh) to make fair comparison with the existing treatment system. Therefore, directional perspectives, including modularity, energy-cost balance, energy and resource recovery, have been proposed for the sustainable market of BES. The current state of the art and up-gradation in resource recovery and contaminant removal warrants a systematic rethinking of functional worth and niches of BES for practical applications.

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.

Separation of Fe from wastewater and its use for NOx reduction; a sustainable approach for environmental remediation

The nitrogen and sulphur oxide (NO_x and SO₂) emissions are causing a serious threat to the existence of life on earth, requiring their effective removal for a sustainable future. Among various approaches, catalytic or electrochemical reduction of air pollutants (NO_x) has gained much attention due to its high efficiency and the possibility of converting these gases into valuable products. However, the required catalysts are generally synthesized from lab-grade chemicals, which may not be a sustainable approach. Herein, a sustainable approach is presented to synthesize an efficient iron-based catalyst directly from industrial/lake wastewater (WW) for NO_x-reduction. According to the theoretical calculations and experimental results, Fe-ions could be readily recovered from wastewater because it has the best adsorption efficiency among all other co-existing metals (Ni²⁺, Cd²⁺, Co²⁺, Cu²⁺, and Cr⁶⁺). The subsequent experimental investigations confirmed the preferential Fe adsorption from different WW streams to develop Fe₃O₄@EDTA-Fe composite, whereby Fe₃O₄ could be used due to its high recycling ability, and ethylenediaminetetraacetic acid (EDTA) acted as a chelating agent to adsorb Fe-metal from effluents. The Fe₃O₄@EDTA-Fe exhibited high efficiency (≥87%) for NO_x reduction even in the presence of high-degree oxygen contents (10-12%). Moreover, Fe₃O₄-EDTA-Fe showed excellent long-term stability for 24 h and maintained more than 80% NO_x reduction. The fabricated catalyst has a great potential for executing a dual role simultaneously for Fe-recovery and NO_x removal, promoting the circular economy concept and providing a potentially sustainable remediation approach for large-scale applications.

Performance of bioelectrode based on different carbon materials in bioelectrochemical anaerobic digestion for methanation of maize straw

The performance of the bioelectrochemical anaerobic digestion (BEAD) reactor was investigated with different carbon material-modified electrodes for the methanation of maize straw. The carbon material-modified electrodes used titanium (Ti) mesh modified with carbon nanotube (CNT), carbon black (CB), and activated carbon (AC). The maximum cumulative methane production obtained in the Ti-CNT reactor was (616.4 ± 9.3) mL/g VS, while the maximum methane production rate in the Ti-AC reactor was (61.9 ± 1.0) mL/g VS.d.The electroactive bacteria were well enriched by the different electrodes, and the enriched electroactive bacteria further facilitate the direct interspecies electron transfer (DIET) for methane production. Additionally, we found the phylum Firmicutes showed a linear relationship to methanogenic performance, as well as the Genus Proteiniborus. The Ti-CNT electrode shows better performance by the electrochemical analysis. These findings provide critical knowledge for the large-scale use of the BEAD process and the treatment of maize straw.

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.

Effect of the bio-inspired modification of low-cost membranes with TiO2:ZnO as microbial fuel cell membranes

Microbial fuel cells (MFCs) are a novel technique for converting biodegradable materials into electricity. In this study, the efficiency of mixed crystal (TiO₂:ZnO) as a membrane modifier of a low-cost, antifouling and self-cleaning cation exchange membrane for MFCs was studied. The modification was prepared using polydopamine (PDA) as the bio-inspired glue, followed by gravity deposition of a mixture of catalyst nanoparticles (TiO₂:ZnO 0.03%, 1:1 ratio) as anti-biofouling agents. The effects of the membrane modification were evaluated in terms of power density, open circuit potential, coulombic efficiency, anti-biofouling properties and also color and COD removal efficiency. The results showed that the use of the PDA-modified membrane and a mixture of catalysts facilitated the transfer of cations released during the oxidation process in the anodic compartment of the MFC, which increased the power generation in the MFC by 2.5 times and 5.7 times the current compared to pristine and PDA pristine membranes, decreased the MFC operating cycle time from 5 to 3 days, doubled the lifetime of the membranes and demonstrated higher COD removal efficiency and color removal. Finally, SEM and AFM analysis showed that the modification significantly minimized surface fouling. The modified membranes in this study proved to be a potential alternative to the expensive membranes currently used in MFCs, furthermore, this modification could be an interesting alternative modification for other potential membranes for use in MFCs, due to the fact that the catalyst activation was only performed with visible light (artificial and solar), which could decrease operating costs.

Scalability of microbial electrochemical technologies: Applications and challenges

During wastewater treatment, microbial electrochemical technologies (METs) are a promising means for in situ energy harvesting and resource recovery. The primary constraint for such systems is scaling them up from the laboratory to practical applications. Currently, most research (∼90%) has been limited to benchtop models because of bioelectrochemical, economic, and engineering design limitations. Field trials, i.e., 1.5 m³ bioelectric toilet, 1000 L microbial electrolysis cell and industrial applications of METs have been conducted, and their results serve as positive indicators of their readiness for practical applications. Multiple startup companies have invested in the pilot-scale demonstrations of METs for industrial effluent treatment. Recently, advances in membrane/electrode modification, understanding of microbe-electrode interaction, and feasibility of electrochemical redox reactions have provided new directions for realizing the practical application. This study reviews the scaling-up challenges, success stories for onsite use, and readiness level of METs for commercialization that is inexpensive and sustainable.

Recent Breakthroughs and Advancements in NOx and SOx Reduction Using Nanomaterials-Based Technologies: A State-of-the-Art Review

Nitrogen and sulpher oxides (NO_x, SO_x) have become a global issue in recent years due to the fastest industrialization and urbanization. Numerous techniques are used to treat the harmful exhaust emissions, including dry, traditional wet and hybrid wet-scrubbing techniques. However, several difficulties, including high-energy requirement, limited scrubbing-liquid regeneration, formation of secondary pollutants and low efficiency, limit their industrial utilization. Regardless, the hybrid wet-scrubbing technology is gaining popularity due to low-costs, less-energy consumption and high-efficiency removal of air pollutants. The removal/reduction of NO_x and SO_x from the atmosphere has been the subject of several reviews in recent years. The goal of this review article is to help scientists grasp the fundamental ideas and requirements before using it commercially. This review paper emphasizes the use of green and electron-rich donors, new breakthroughs, reducing GHG emissions, and improved NO_x and SO_x removal catalytic systems, including selective/non-catalytic reduction (SCR/SNCR) and other techniques (functionalization by magnetic nanoparticles; NP, etc.,). It also explains that various wet-scrubbing techniques, synthesis of solid iron-oxide such as magnetic (Fe₃O₄) NP are receiving more interest from researchers due to the wide range of its application in numerous fields. In addition, EDTA coating on Fe₃O₄ NP is widely used due to its high stability over a wide pH range and solid catalytic systems. As a result, the Fe₃O₄@EDTA-Fe catalyst is projected to be an optimal catalyst in terms of stability, synergistic efficiency, and reusability. Finally, this review paper discusses the current of a heterogeneous catalytic system for environmental remedies and sustainable approaches.