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Record W4415517634 · doi:10.1007/s42452-025-07154-3

Recent progress in electrochemical coupling for bio-electrochemical system optimization

2025· article· en· W4415517634 on OpenAlexaff
Padmaja Shastri, Elyssa Jane Gargallano, Soheil Ghoreyshi

Bibliographic record

VenueDiscover Applied Sciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsCentennial College
Fundersnot available
KeywordsRenewable energyElectron transferElectrochemistryElectrodeMicrobial fuel cellElectrochemical energy storageCoupling (piping)Biofouling

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.233
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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