Enhanced redox conductivity and enriched Geobacteraceae of exoelectrogenic biofilms in response to static magnetic field

The Importance of Spin-Polarized Charge Reorganization in the Catalytic Activity of D-Glucose Oxidase

The reaction of D-glucose oxidase (GOx) with D- and L-glucose was investigated using confocal fluorescence microscopy and Hall voltage measurements, after the enzyme was adsorbed as a monolayer. By adsorbing the enzyme on a ferromagnetic substrate, we verified that the reaction is spin dependent. This conclusion was supported by monitoring the reaction when the enzyme is adsorbed on a Hall device that does not contain any magnetic elements. The spin dependence is consistent with the chiral-induced spin selectivity (CISS) effect; it can be explained by the improved fidelity of the electron transfer process through the chiral enzyme due to the coupling of the linear momentum of the electrons and their spin. Since the reaction studied often serve as a model system for enzymatic activity, the results may suggest the general importance of the spin-dependent electron transfer in bio-chemical processes.

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.

Synergistic effect of zero-valent iron and static magnetic field on wastewater purification and bioelectricity generation in electroactive constructed wetlands

This study investigated the enhancement effect of zero-valent iron and static magnetic field on the pollutant removal and power generation of electroactive constructed wetland. As demonstration, a conventional wetland was systematically modified by introducing zero-valent iron and then a static magnetic field, leading to progressive increases in pollutant (namely NH4+-N and chemical oxygen demand) removal efficiencies. By introducing both zero-valent iron and a static magnetic field, the power density increased four-fold to 9.2 mW/m2 and the internal resistance decreased by 26.7% to 467.4 Ω. Notably, static magnetic field decreased the relative abundance of electrochemically active bacteria (such as Romboutsia), while significantly enhancing species diversity. The permeability of the microbial cell membrane was improved, leading to a reduction in activation loss and internal resistance, thereby enhancing power generation capacity. Results showed that the addition of zero-valent iron and the applied magnetic field were beneficial to the pollutants removal and bioelectricity generation.

Optimization of biofilm conductance measurement with two-electrode microbial electrochemical cells (MECs)

This study was conducted to develop a standardized and consistent method for biofilm conductance measurement for an improved comprehension of extracellular electron transfer. Biofilm conductance (2.12 ± 0.25 × 10^-4 S) with and without a fixed anode potential did not show significant difference. The conductance showed a sigmoidal relationship with anode potential. The current-voltage profile of the tested biofilm at applied voltage larger than 100 mV showed deviation from Ohm's law. Up to 69% decrease in biofilm conductance and deviation from Ohm's law were observed in the current-voltage profile when the measurement time increased. By choosing the voltage range (0- 100 mV) and step (25 mV), measurement time (100-s at each voltage step), and anode control mode, these operation settings were found more suitable for consistent and accurate biofilm conductance measurement in the 2-Au MEC system. This represents the first study that comprehensively evaluated the environmental and instrumental parameters for biofilm conductance measurement.

The use of the electromagnetic field in microbial process bioengineering

An electromagnetic field (EMF) has been shown to have various stimulatory or inhibitory effects on microorganisms. Over the years, growing interest in this topic led to numerous discoveries suggesting the potential applicability of EMF in biotechnological processes. Among these observations are stimulative effects of this physical influence resulting in intensified biomass production, modification of metabolic activity, or pigments secretion. In this review, we present the current state of the art and underline the main findings of the application of EMF in bioprocessing and their practical meaning in process engineering using examples selected from studies on bacteria, archaea, microscopic fungi and yeasts, viruses, and microalgae. All biological data are presented concerning the classification of EMF. Furthermore, we aimed to highlight missing parts of contemporary knowledge and indicate weak spots in the approaches found in the literature.

Biocatalyst physiology and interplay: a protagonist of MFC operation

Microbial fuel cells (MFC) have been foreseen as a sustainable renewable energy resource to meet future energy demand. In the past, several studies have been executed in both benchtop and pilot scale to produce electrical energy from wastewater. The key role players in this technology that leads to the operation are microbes, mainly bacteria. The dominant among them is termed as "exoelectrogens" that have the capability to produce and transport electron by utilizing waste source. The current review focuses on such electrogenic bacteria's involvement for enhanced power generation of MFC. The pathway of electron transfer in their cell along and its conduction to the extracellular environment of the MFC system are critically discussed. The interaction of the microbes in various MFC operational conditions, including the role of substrate and solid electron acceptors, i.e., anode, external resistance, temperature, and pH, was also discussed in depth along with biotechnological advancement and future research perspective.

MnCo2O4 coated carbon felt anode for enhanced microbial fuel cell performance

A highly efficient anode is very crucial for an improved microbial fuel cell (MFC) performance. In this study, a binder-free manganese cobalt oxide (MnCo₂O₄@CF) anode was synthesized using a conventional carbon felt (CF) by a facile hydrothermal method. A large electrochemically active and rough electrode surface area of MnCo₂O₄@CF anode improved the substrate fluxes and microbial adhesion/growth. Furthermore, the electrochemical tests on the synthesized anode confirmed the superior bioelectrochemical activity, reduced ion transfer resistance, and excellent capacitance. This resulted in an improved power density (945 mW/m²), which was 3.8 times higher than that of CF anode. The variable valence state, high stability and biocompatibility of MnCo₂O₄@CF resulted in continuous current density performance for five MFC cycles. High-throughput biofilm analysis revealed the enrichment of electricity producing phylum of Proteobacteria and Bacteroidetes (∼90.0%), which signified that the modified MnCo₂O₄ anode accelerated the enrichment of electro-active microbes.

Hydrogen production from lignocellulosic hydrolysate in an up-scaled microbial electrolysis cell with stacked bio-electrodes

Hydrogen production from renewable resources via microbial electrolysis cells (MECs) is a promising approach for sustainable energy production. Yet high hydrogen yield from real feedstocks has not been demonstrated in up-scaled MECs. In this study, a 10-L single chamber MEC with a high electrode surface area to volume ratio (66 m²/m³) was constructed and electroactive cathodic biofilms were enriched for hydrogen evolution reaction. A high hydrogen yield of 91% was achieved using lignocellulosic hydrolysate with a hydrogen production rate of 0.71 L/L/D at an organic loading rate of 0.4 g/D. The anodic and cathodic microbial communities, with Enterococcus spp. as the known electroactive bacteria, were capable of achieving current densities of 13.7 A/m² and 16.5 A/m², respectively. A machine learning algorithm was used to investigate the correlation between community data and electrochemical performance, and the critical genera on determining current density were identified.

Novel trickling microbial fuel cells for electricity generation from wastewater

There are two main challenges associated with the scale-up of air-cathode microbial fuel cells (MFCs): performance reduction and cathode leakage/flooding. In this study, a novel 13.4 L reactor that contains 4 tubular MFCs was designed and operated in a trickling mode for 65 days under different conditions. The trickling water flow through the horizontally aligned MFCs alleviated the hydraulic pressure applied to the air-cathodes. With a total cathode working area of over 1700 cm², this reactor generated power densities up to 1 W/m² with coulombic efficiencies over 50% using acetate. Using a brewery waste stream as carbon source, an average power density of 0.27 W/m² was generated with ∼60% COD removal at hydraulic retention time of 1.6 h. The decent performance of this reactor compared with other air-cathode MFCs at the similar scale and the alleviated hydraulic pressure on air-cathodes demonstrate the great potential of this design and operation for future MFC optimization and scaling up.

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells

The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown feed substrate must be determined. The application of genomic data and well-developed data mining technologies can overcome these limitations and advance engineering development. In the present study, 69 samples with three different substrate types (acetate, carbohydrates and wastewater) collected from various laboratory environments were evaluated to determine the ability to identify feed substrates from the resultant microbial communities. Six machine learning algorithms with four different input variables were trained and evaluated on their ability to predict feed substrate from genomic datasets. The highest accuracies of 93 ± 6% and 92 ± 5% were obtained using NNET trained on datasets classified at the phylum and family taxonomic level, respectively. These accuracies corresponded to kappa values of 0.87 ± 0.10, 0.86 ± 0.09, respectively. Four out of six of the algorithms used maintained accuracies above 80% and kappa values higher than 0.66. Different sequencing method (Roche 454 or Illumina sequencing) did not affect the accuracies of all algorithms, except SVM at the phylum level. All algorithms trained on NMDS-compressed datasets obtained accuracies over 80%, while models trained on PCoA-compressed datasets presented a 10-30% reduction in accuracy. These results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.