Simultaneous Biogas Upgrading and Desulfurization Using a Microbial Electrosynthesis System with Optimized Electrodes and Membrane Selection
Bibliographic record
Abstract
Biogas upgrading based on the principle of the microbial electrosynthesis (MES) system offers a promising avenue for biogas upgrading. Here, we explored 4 different combinations of cathode and membrane materials to optimize MES for biogas upgrading. MES equipped with a stainless steel cathode and Nafion 117 membrane (designated as MES-2) demonstrated optimal performance, achieving a maximum methane production of 268.5 ± 19.5 L methane /m cathode 3 with a bicarbonate medium. Furthermore, MES-2 showed superior performance with a CO 2 -rich gas (70% CO 2 and 30% N 2 ), achieving 100% CO 2 conversion to methane conversion after 3 days of gas recirculation. When testing different biogas sources (synthetic and real anaerobic digestion biogas), MES-2 also consistently provided >99% methane content within a relatively short time (<3 days) of biogas recirculation. Additionally, H 2 S content was significantly reduced from 214 ppmv to <1 ppmv, enabling the upgraded biogas to be widely utilized in various applications. The microbial community analysis indicated that this outcome was primarily due to the substantial growth of chemolithoautotrophic sulfide-oxidizing bacteria, such as Thiobacillus, which likely converted sulfide to elemental sulfur and/or sulfate. This study underscores the potential of MES as a highly effective and uniquely adaptable technology for biogas upgrading and desulfurization, promoting sustainable energy practices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".