Anodic performance of microbial electrolysis cells in response to ammonia nitrogen
Bibliographic record
Abstract
In this study, the authors investigated how various total ammonia nitrogen (TAN) concentrations (128–1231 mg nitrogen/l) in the substrate medium affect the anodic performance of dual-chamber microbial electrolysis cells (MECs) with an anion-exchange membrane (AEM) and a cation-exchange membrane (CEM). The current densities and midpoint potentials of the bioanode in the AEM-MEC were independent of TAN concentration changes. In comparison, current densities in the CEM-MEC considerably dropped at a high TAN concentration of 1231 mg nitrogen/l, and the midpoint potential of the bioanode became increasingly positive, indicating that the anode biofilm was negatively affected by a high TAN concentration. Comparison of pH and TAN concentration changes in the anolyte in the CEM-MEC suggests that high levels of TAN did not directly influence anode-respiring bacteria (ARB) metabolism; accumulation of protons was the primary reason for the deterioration of anodic performance. However, the patterns of changes in chemical oxygen demand (COD) removal efficiencies and coulombic efficiencies in response to increased TAN concentrations were quite similar in the two MECs. The COD removal efficiencies also decreased at elevated TAN concentrations, while coulombic efficiencies increased, suggesting that non-ARB activity was reduced in both MECs.
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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) | 0.001 | 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".