Exploration of Optimum Configurations of Electro-Assisted Anaerobic Digester for Sulfate-Reducing Conditions: Significance of Membranes and Mechanistic Insights
Bibliographic record
Abstract
Sulfides generated from sulfate reduction commonly act as inhibitors in anaerobic digestion (AD). This study examined the performance of three configurations of microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) systems: single-chamber, dual-chamber with an anion exchange membrane (AEM), and dual-chamber with a cation exchange membrane (CEM), operated under sulfate-reducing conditions. In general, the single-chamber MEC-AD reactors exhibited significantly higher methane yields (74.5–75.0%) than the control (64.4%). Upon converting a single-chamber MEC-AD to a dual-chamber configuration using an AEM, methane yield further increased up to 80.6%. The membrane facilitated the transfer of buffer anions to the anode and sulfate to the cathode, which substantially reduced un-ionized sulfide concentrations in the anode chamber. The MEC-AD with an AEM also demonstrated increased specific methanogenic activities, current densities, and microbial diversity with the enrichment of electroactive bacteria (e.g., Geobacter, Aeromonas ) and hydrogenotrophic Methanobacterium . However, the MEC-AD reactor with a CEM experienced a significant reduction in methane yield (63.2%), mainly due to anolyte acidification, which increased the un-ionized sulfide concentrations.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".