Liquids and Microbial Electrolysis Cells for Boosted CO<sub>2</sub> Methanogenesis: Role of Interfacial Electron Transfer
Bibliographic record
Abstract
This study explores the improvement of CO 2 methanogenesis using microbial electrolysis systems (MESS) and ionic liquids (ILs). The microbial community adapted to CO 2 methanogenesis showed performance enhancement over time, achieving 0.46 mmol/cycle of specific methane production in the combined MESS and IL system, while it was around 0.28 mmol/cycle for MES only. Under non-electrified conditions, methane production was quite lower (0.1 mmol/cycle). The highest CO 2 conversion efficiency was achieved in the MESS/IL (M-I-E) group, followed by microbiology (M), MESS/IL (M-I), and MESS(M-E). ILs enhanced the electrochemical activity of MESS, resulting in a higher current to 0.61 ± 0.05 mA and a higher Coulombic efficiency to 68.8 ± 3%, compared to 0.45 ± 0.05 mA and 55.6 ± 2% for MESS alone. Further evidence for the improvements was shown by the reduced charge transfer resistance (2.37 ± 0.08 Ω) and enhanced biomass accumulation at the cathode. The microbial community analysis pointed out a significant shift in dominant species, including a significant increase in methanogens such as Methanobacterium sp. and Methanoculleus bourgensis . Metabolic responses showed upregulation of key genes involved in the transporters, Wood–Ljungdahl pathway, and tricarboxylic acid (TCA) cycle, indicating that IL layers could provide channels directly or through outside cellular entities for electrons to efficiently shuttle for enhanced methanogenesis. These findings gain insights into the synergistic benefits of ILs and MESS in boosting CO 2 methanogenesis and provide insights into the underlying mechanisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".