Biorefinery perspectives of microbial electrolysis cells (MECs) for hydrogen and valuable chemicals production through wastewater treatment
Bibliographic record
Abstract
The degradation of waste organics through microbial electrolysis cell (MEC) generates hydrogen (H2) gas in an economically efficient way. MEC is known as the advanced concept of the microbial fuel cell (MFC) but requires a minor amount of supplementary electrical energy to produce H2 in the cathode microenvironment. Different bio/processes could be integrated to generate additional energy from the substrate used in MECs, which would make the whole process more sustainable. On the other hand, the energy required to drive the MEC mechanism could be harvested from renewable energy sources. These integrations could advance the efficiency and economic feasibility of the whole process. The present review critically discusses all the integrations investigated to date with MECs such as MFCs, anaerobic digestion, microbial desalination cells, membrane bioreactors, solar energy harvesting systems, etc. Energy generating non-biological and eco-friendly processes (such as dye-sensitized solar cells and thermoelectric microconverters) which could also be integrated with MECs, are also presented and reviewed. Achieving a comprehensive understanding about MEC integration could help with developing advanced biorefineries towards more sustainable energy management. Finally, the challenges related to the scaling up of these processes are also scrutinized with the aim to identify the practical hurdles faced in the MEC processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".