Deciphering the microbial interactions and metabolic shifts at different COD/sulfate ratios in electro-assisted anaerobic digestion
Bibliographic record
Abstract
This research aims to investigate the influence of sulfate on the performance of microbial electrolysis cell-assisted anaerobic digester (MEC-AD) across varying sulfate conditions, including no sulfate and reduced COD/sulfate ratios from 20 to 1. The principal results indicate a gradual decline in methane yield in the MEC-AD from 78.7 ± 2.3 % under no sulfate conditions to 56.2 ± 2.0 % at a COD/sulfate ratio of 1, contrasting with a more substantial decrease in the control reactor (69.9 ± 3.6 % to 32.8 ± 1.5 %). The MEC-AD reactor exhibits heightened resilience to sulfide toxicity, showcasing higher specific methanogenic activities. Key findings suggest that the MEC-AD reactor maintains lower free sulfide concentrations, attributed to its higher pH and potential anodic sulfide oxidation. Additionally, the study reveals the promotion of syntrophic partnerships in the MEC-AD reactor, particularly between sulfate-reducing bacteria (SRB) such as Desulfovibrio, Desulfomicrobium, and Desulfobulbus, and other microbial groups, including hydrogenotrophic methanogens and electroactive bacteria. The integration of these mechanisms highlights the MEC-AD reactor's ability to effectively mitigate sulfate-induced challenges and enhance overall anaerobic digestion performance. This study presents a significant step forward in the development of resilient anaerobic digestion systems capable of efficiently handling sulfate stress.
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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) | 0.000 | 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 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".