A modeling approach to direct interspecies electron transfer process in anaerobic transformation of ethanol to methane

Conductive materials as fantastic toolkits to stimulate direct interspecies electron transfer in anaerobic digestion: new insights into methanogenesis contribution, characterization technology, and downstream treatment

Direct interspecies electron transfer (DIET) stimulated by conductive materials (CMs) enables intercellular metabolic coupling that can address the unfavorable thermodynamical dilemma inherent in anaerobic digestion (AD). Although the DIET mechanism and stimulation have been extensively summarized, the methanogenesis contribution, characterization techniques, and downstream processes of CMs-led DIET in AD are surprisingly under-reviewed. Therefore, this review aimed to address these gaps. First, the contribution of CMs-led DIET to methanogenesis was re-evaluated by comparing the effect of various factors, including volatile fatty acids, free ammonia, and functional enzymes. It was revealed that AD systems are usually intricate and cannot allow the methanogenesis stimulation to be singularly attributed to the establishment of DIET. Additionally, considerable attention has been attached to the characterization of DIET occurrence, involving species identification, gene expression, electrical properties, cellular features, and syntrophic metabolism, suggesting the significance of accurate characterization methods for identifying the syntrophic metabolism interactions. Moreover, the type of CMs has a significant impact on AD downstream processes involving biogas purity, sludge dewaterability, and biosolids management. Finally, the central bottleneck consists in building a mathematical model of DIET to explain the mechanism of DIET in a deeper level from kinetics and thermodynamics.

Contribution analysis of different electron transfer pathways to methane production in anaerobic digestion coupled with bioelectrochemical system

The contribution analysis of different electron transfer pathways to CH₄ production was investigated in bioelectrochemical anaerobic digestion (BEAD). It demonstrates that the indirect interspecies electron transfer (IIET) pathway and the direct interspecies electron transfer (DIET) pathways contributed to 41.7 % and 58.3 % of the CH₄ production in the BEAD reactor, respectively. The DIET pathway was further divided into DIET via electrode (eDIET) and biological DIET (bDIET) in the bulk solution, and contributed 11.1 % and 47.2 % of CH₄ production, respectively. This indicates that the dominant electron transfer pathway for CH₄ production is from the bulk solution, rather than on the polarized electrode. The electroactive microorganisms were well enriched in the bulk solution by the electric field generated between anode and cathode. The enriched electroactive microorganisms significantly improved the CH₄ production in the bulk solution through the bDIET pathway.

Insights into high-solids anaerobic digestion of food waste enhanced by activated carbon via promoting direct interspecies electron transfer

High-solids anaerobic digestion (HS-AD) of food waste frequently confronted the acidification and failure under high organic loading rates (OLRs). Results indicated powdered activated carbon (PAC) addition significantly enhanced methane production and process stability than granular activated carbon, and columnar activated carbon at higher OLRs via accelerating the propionate consumption. Potential direct interspecies electron transfer (DIET) partners, including various syntrophic oxidation bacteria and methanogens, were enriched with the activated carbon (AC) addition. Furthermore, DIET contribution to methane production was 35% by PAC, predicated by the modified Anaerobic Digestion Model No.1 (ADM1). This study deeply elucidated the DIET mechanism and offered the potential foundations for the selection and applications of AC-based materials in HS-AD of food waste.

Progress and prospects of applying carbon-based materials (and nanomaterials) to accelerate anaerobic bioprocesses for the removal of micropollutants

Carbon-based materials (CBM), including activated carbon (AC), activated fibres (ACF), biochar (BC), nanotubes (CNT), carbon xenogels (CX) and graphene nanosheets (GNS), possess unique properties such as high surface area, sorption and catalytic characteristics, making them very versatile for many applications in environmental remediation. They are powerful redox mediators (RM) in anaerobic processes, accelerating the rates and extending the level of the reduction of pollutants and, consequently, affecting positively the global efficiency of their partial or total removal. The extraordinary conductive properties of CBM, and the possibility of tailoring their surface to address specific pollutants, make them promising as catalysts in the treatment of effluents containing diverse pollutants. CBM can be combined with magnetic nanoparticles (MNM) assembling catalytic and magnetic properties in a single composite (C@MNM), allowing their recovery and reuse after the treatment process. Furthermore, these composites have demonstrated extraordinary catalytic properties. Evaluation of the toxicological and environmental impact of direct and indirect exposure to nanomaterials is an important issue that must be considered when nanomaterials are applied. Though the chemical composition, size and physical characteristics may contribute to toxicological effects, the potential toxic impact of using CBM is not completely clear and is not always assessed. This review gives an overview of the current research on the application of CBM and C@MNM in bioremediation and on the possible environmental impact and toxicity.

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.

Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning

Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment, Geobacter OTU650, which was phylogenetically close to Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation, indicating Ca. G. eutrophica's EET ability. The high-quality draft genome further showed high phylogenomic similarity with Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. A Bayesian network was trained with the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis from dominant fermentative bacteria, Geobacter, and Methanobacterium. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.

Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: Two-stage machine learning modeling based on a NARX-BP hybrid neural network

With the increasing of data in wastewater treatment, data-driven machine learning models are useful for modeling biological processes and complex reactions. However, few data-driven models have been developed for simulating the microbial electrolysis cells (MECs) and traditional models are too ambiguous to comprehend the mechanisms. In this study, a new general data-driven two-stage model was firstly developed to predict CH₄ production from in-situ biogas upgrading in the biocathode MECs via direct electron transfer (DET), named NARX-BP hybrid neural networks. Compared with traditional one-stage model, the model could well predict methane production via DET with excellent performance (all R² and MES of 0.918 and 6.52 × 10^-2, respectively) and reveal the mechanisms of biogas upgrading, for the new systematical modeling approach could improve the versatility and applicability by inputting significant intermediate variables. In addition, the model is generally available to support long-term prediction and optimal operation for anaerobic digestion or complex MEC systems.

Modeling of interspecies electron transfer in anaerobic microbial communities

Interspecies electron transfer (IET) is a key phenomenon in anaerobic ecosystems, which is traditionally modeled as hydrogen transfer. Recently discovered alternative mediated IET (MIET) or direct IET (DIET) offer exciting alternative mechanisms of microbial partnerships that could lead to new strategies for the improvement of biotechnologies. Here, we analyze mathematical modeling of DIET and MIET in anaerobic ecosystems. Bioenergetics approaches already enable the evaluation of different energy sharing scenarios between microorganisms and give interesting clues on redox mediators and on possible ways of driving microbial communities relying on IET. The modeling of DIET kinetics however is currently only in its infancy. Recent concepts introduced for the modeling of electroactive biofilms should be further exploited. Recent modeling examples confirms the potential of DIET to increase the IET rates compared to H₂-MIET, but also point out the need for additional characterizations of biological components supporting IET to improve predictions.

A new insight into the strategy for methane production affected by conductive carbon cloth in wetland soil: Beneficial to acetoclastic methanogenesis instead of CO2 reduction

Conductive materials/minerals can promote direct interspecies electron transfer (DIET) between syntrophic bacteria and methanogens in defined co-culture systems and artificial anaerobic digesters; however, little is known about the stimulation strategy of carbon material on methane production in natural environments. Herein, the effect of carbon cloth, as a representative of conductive carbon materials, on methane production with incubated wetland soil was investigated. Carbon cloth significantly promoted methanogenesis. With the application of electrochemical technology, calculation of the apparent electron transfer rate constant showed that carbon cloth significantly increased electron transfer rate (ETR) compared with the control experiment in presence of cotton cloth, from 0.0017 ± 0.0003 to 0.0056 ± 0.0015 s^-1. Results obtained from both stable carbon isotope measurements and application of specific inhibitor (CH₃F) for acetoclastic methanogenesis indicated that carbon cloth obviously promoted acetoclastic methanogenesis instead of CO₂ reduction. High-throughput sequencing showed that methane production may stem from the involvement of Methanosarcina for both treatments. Our findings suggested that conductive carbon material can promote acetoclastic methanogenesis instead of CO₂ reduction in a natural environment.

Enhancing Biochemical Methane Potential and Enrichment of Specific Electroactive Communities from Nixtamalization Wastewater using Granular Activated Carbon as a Conductive Material

Nejayote (corn step liquor) production in Mexico is approximately 1.4 × 1010 m3 per year and anaerobic digestion is an effective process to transform this waste into green energy. The biochemical methane potential (BMP) test is one of the most important tests for evaluating the biodegradability and methane production capacity of any organic waste. Previous research confirms that the addition of conductive materials significantly enhances the methane production yield. This study concludes that the addition of granular activated carbon (GAC) increases methane yield by 34% in the first instance. Furthermore, results show that methane production is increased by 54% when a GAC biofilm is developed 10 days before undertaking the BMP test. In addition, the electroactive population was 30% higher when attached to the GAC than in control reactors. Moreover, results show that electroactive communities attached to the GAC increased by 38% when a GAC biofilm is developed 10 days before undertaking the BMP test, additionally only in these reactors Geobacter was identified. GAC has two main effects in anaerobic digestion; it promotes direct interspecies electron transfer (DIET) by developing an electro-active biofilm and simultaneously it reduces redox potential from −223 mV to −470 mV. These results suggest that the addition of GAC to biodigesters, improves the anaerobic digestion performance in industrial processed food waste.