Efficient extracellular electron transfer (EET) is critical to harnessing bacteria like Shewanella oneidensis MR-1 in microbial electrochemical systems (MESs) aimed at sustainable energy production, environmental remediation, and resource recovery. This study investigates how palladium ions (Pd2+), which are of significant interest for catalysis and resource recovery, impact the bioelectrocatalysis of S. oneidensis MR-1 using electrochemical, transcriptomic, and microscopic methods. Pd2+ exposure significantly increased the observed current via EET, which correlated with substantially increased viable biofilm formation on the electrode. Transcriptomics revealed a coordinated cellular response, including upregulation of genes facilitating EET (the Mtr pathway), metabolism, motility, and biofilm processes, alongside downregulation of competing pathways. Cellular reduction of Pd2+ to palladium nanoparticles, mainly near the outer membrane, was confirmed via microscopy. Pd2+ boosts S. oneidensis MR-1 bioelectrocatalysis through the combined effects of promoting biofilm development and upregulating essential cellular pathways, informing fundamental strategies for enhanced MESs targeted for sustainable engineering, bioremediation, and resource and material recovery applications.

About the Seminar "Enzymatic Bioelectrocatalysis for Electrosynthesis"In the last 5 years, there have been extensive studies and new materials designed for interfacing biocatalysts with electrode surf...

Functional enzyme–gold nanoparticle bioconjugates are becoming increasingly important in bioelectrocatalysis since they facilitate and improve the efficiency of long-range protein interfacial electron...

Published in Journal of environmental management

Published in Bioelectrochemistry (Amsterdam, Netherlands)

Bioelectrocatalysis for clean energy production and organic waste/environmental pollutant treatment has received a great deal of attention from scientists and engineers around the world. The introduction of machine learning (ML) in this field has just started. At present, ML is mainly applied to electrochemical biosensors and microbial fuel cells, and there are already visible achievements. Abstract 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.