A Comprehensive Understanding of Electro-Fermentation
Bibliographic record
Abstract
Electro-fermentation (EF) is an upcoming technology that can control the metabolism of exoelectrogenic bacteria (i.e., bacteria that transfer electrons using an extracellular mechanism). The fermenter consists of electrodes that act as sink and source for the production and movement of electrons and protons, thus generating electricity and producing valuable products. The conventional process of fermentation has several drawbacks that restrict their application and economic viability. Additionally, metabolic reactions taking place in traditional fermenters are often redox imbalanced. Almost all metabolic pathways and microbial strains have been studied, and EF can electrochemically control this. The process of EF can be used to optimize metabolic processes taking place in the fermenter by controlling the redox and pH imbalances and by stimulating carbon chain elongation or breakdown to improve the overall biomass yield and support the production of a specific product. This review briefly discusses microbe-electrode interactions, electro-fermenter designs, mixed-culture EF, and pure culture EF in industrial applications, electro methanogenesis, and the various products that could be hence generated using this process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".