Recent progress in electrochemical coupling for bio-electrochemical system optimization
Bibliographic record
Abstract
Bioelectrochemical systems (BES) are a promising renewable energy source that harnesses the interaction between biotic and abiotic components to generate power. However, microbial, along with electrode material compatibility factors, can hinder power generation, leading to energy losses in BES. This review explores recent advancements and challenges in BES power generation, with a focus on how best to optimize electrochemical coupling. Electrochemical coupling can help provide sustainable energy to drive reductive reactions at the biocathode, contributing to green synthesis of value-added chemicals . The review evaluates the role of electrochemical coupling devices and highlights the significance of microbial biofilm-to-EPS ratio at the electrode interface, which is critical to BES performance, and is influenced by applied currents. Strategies to enhance biofilm robustness and mitigate biofouling through electrode modifications are discussed. Further, we analyze electron transfer mechanisms at the microbe–electrode interface and explore bioengineering approaches to improve electron transfer efficiency. Advancements in electrode materials and novel modifications are also reviewed, along with the potential of plant-based fuel cells as alternative electron sources for driving reductive reactions. The review concludes by showcasing innovative hybrid system designs, paving the way for energy self-sufficient and multi-functional BES platforms that can help drive electrochemical coupling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".