Liquids and Microbial Electrolysis Cells for Boosted CO<sub>2</sub> Methanogenesis: Role of Interfacial Electron Transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it