Effect of cathode material and charge loading on the nitrification performance and bacterial community in leachate treating Electro-MBRs
Bibliographic record
Abstract
Electro-MBR technology, which combines an electrocoagulation process inside the mixed liquor of a membrane bioreactor , was studied for the treatment of a high-strength ammonia leachate (124 ± 4 mg NH 4 -N L −1 ). A lab-scale aerobic Electro-MBR was operated with a solid retention time of 45 days, hydraulic retention times of 24h and 12h, and charge loading ranging from 100 to 400 mAh L −1 . At 400 mAh L −1 , with a combination of a Ti/Pt cathode and a sacrificial iron anode, removal percentages for ammonia nitrogen, total organic carbon , and total phosphorus were 99.8%, 38%, and 99.0%, respectively. At 400 mAh L −1 , the estimated ferric ion dosage was 325 mg Fe 3+ L −1 . Experiments conducted with different cathode materials showed that previously reported inhibition phenomena may result from a cathodic nitrate reduction into ammonia nitrogen. Conventional cathode materials , such as graphite, have electrochemical nitrate reduction rates of −0.03 mg NO 3 -N mAh −1 . By comparison, when using Ti/Pt, the rate was −0.0045 mg NO 3 -N mAh −1 (85% lower than graphite due to its low hydrogen overpotential). Charge loading tested in this study had no significant impact on both nitrification performance and microbial population diversity. However, the relative abundance of the mixed liquor’s Nitrosomonas increased from 4.8% to 8.2% when the charge loading increased from 0 to 400 mAh L −1 . Results from this study are promising for future applications of the Ti/Pt - Iron Electro-MBR in various high-strength ammonia wastewater treatment applications.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".