Concomitant production of bioelectricity and hydrogen peroxide leading to the holistic treatment of wastewater in microbial fuel cell

Textile dye removal using diatomite nanocomposites: a metagenomic study in photosynthetic microalgae-assisted microbial fuel cells

In this study, Coomassie brilliant blue (CBB), brilliant green (BG), and rhodamine (Rh) dyes were used to simulate dye-rich wastewater. Adsorption and degradation of these dyes (2 μM, 10 μM, and 30 μM) on diatomite (DE) were evaluated under light (L) and dark (D) conditions. The adsorption of dye-DE composites followed pseudo-second-order kinetics at all concentrations and conditions had R 2 > 0.99, thus showing a good fit. The calculated equilibrium adsorption amount q e,(cal) was coherent with the value of experimental q e,(exp). The poorest adsorption and photocatalysis occurred at 30 μM, prompting the functionalization of dyes with TiO2 and Fe3O4 nanoparticles (NP(s)). The highest dye degradation efficiencies (DGeff) for 30 μM dyes were 86.79% (CBB-DE-Fe3O4, 72 h), 96.10% (BG-DE-TiO2, 52 h), and 81.74% (Rh-DE-TiO2, 48 h), with Rh-DE-TiO2 showing the fastest degradation. Functionalized DE-dye (30 μM) nanocomposites were further tested in a photosynthetic microalgae-assisted microbial fuel cell with dye-simulated wastewater at the anode (PMA-MFC-1 with CBB-DE-Fe3O4, PMA-MFC-2 with BG-DE-TiO2 and PMA-MFC-3 with Rh-DE-TiO2) and Asterarcys sp. GA4 microalgae at the cathode. In dark anode chambers, PMA-MFC-3 achieved the highest DGeff value of Rh dye as 88.23% and a polarization density of 30.06 mW m-2, outperforming PMA-MFC-2 with BG dye and PMA-MFC-1 with CBB dye. The molecular identifier analysis of microbes in wastewater at the anode showed the dominance of Sphingobacteria and Proteobacteria in PMA-MFC-3 (Rh-DE-TiO2) and COD removal of 61.36%, highlighting its potential for efficient dye degradation and bioelectricity generation. Furthermore, PMA-MFC-3 simultaneously demonstrated a superior microalgal lipid yield of 3.42 μg g-1 and an algal growth of 8.19 μg g-1 at the cathode.

Role of bacterial quorum sensing and quenching mechanism in the efficient operation of microbial electrochemical technologies: A state-of-the-art review

Microbial electrochemical technologies (METs) are a group of innovative technologies that produce valuables like bioelectricity and biofuels with the simultaneous treatment of wastewater from microorganisms known as electroactive microorganisms. The electroactive microorganisms are capable of transferring electrons to the anode of a MET through various metabolic pathways such as direct (via cytochrome or pili) or indirect (through transporters) transfer. Though this technology is promising, the inferior yield of valuables and the high cost of reactor fabrication are presently impeding the large-scale application of this technology. Therefore, to overcome these major bottlenecks, a lot of research has been dedicated to the application of bacterial signalling, for instance, quorum sensing (QS) and quorum quenching (QQ) mechanisms in METs to improve its efficacy in order to achieve a higher power density and to make it more cost-effective. The QS circuit in bacteria produces auto-inducer signal molecules, which enhances the biofilm-forming ability and regulates the bacterial attachment on the electrode of METs. On the other hand, the QQ circuit can effectively function as an antifouling agent for the membranes used in METs and microbial membrane bioreactors, which is imperative for their stable long-term operation. This state-of-the-art review thus distinctly describes in detail the interaction between the QQ and QS systems in bacteria employed in METs to generate value-added by-products, antifouling strategies, and the recent applications of the signalling mechanisms in METs to improve their yield. Further, the article also throws some light on the recent advancements and the challenges faced while incorporating QS and QQ mechanisms in various types of METs. Thus, this review article will help budding researchers in upscaling METs with the integration of the QS signalling mechanism in METs.

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.

Biodegradation of synthetic estrogen using bioelectrochemical system and degradation pathway analysis through Quadrupole-time-of-flight-mass spectrometry

Synthetic estrogenic compounds such as 17α-ethinylestradiol (EE2) are significant environmental contaminants. This research studied the biodegradation of EE2 utilizing the EE2 adapted cells isolated from a dairy farm waste site in suspension flask vis-a-vis Bioelectrochemical System (BES) and compared the power output in the BES with and without EE2 as a co-substrate. 78% removal of EE2 was observed in the BES as against 60% removal in suspension flasks. The maximum power density in the BES increased about 53% when EE2 is used as a co-substrate. The EE2 biodegradation studied using HPLC and Q-TOF methods, also proposes a hypothetical pathway for EE2 degradation by the newly isolated strain Rhodopseudomonas palustris MDOC01 and reports the significant metabolites like nicotinic acid and oxoproline being detected during bioelectrochemical treatment process of EE2. Study also suggests that Plasma peroxide treatment of anode material enhanced the overall performance in terms of biodegradation efficiency and power output.