Enhanced Denitrification and Microbial Mechanism in Secondary Effluent Treatment Using Combined Iron–Carbon Microelectrolysis and Deep Bed Filters
Bibliographic record
Abstract
This study developed an innovative system coupling iron–carbon microelectrolysis with a deep bed denitrification filter (DBDF) for the advanced treatment of secondary effluent. The key innovation lay in revealing the synergistic pollutant removal mechanisms through microbial community succession and metabolism pathway enhancement. Results showed that the effluent concentrations of total nitrogen, nitrate, and total phosphorus were stabilized below 2.5, 0.55, and 0.25 mg/L, and 41.05% of chemical oxygen demand was removed. Microorganisms in the microelectrolysis column (MEC) and DBDF all varied with the change of reactors height. The dominant genera in the MEC were Dechloromonas, Dechlorosoma, and uncultured_bacterium_f_Rhodocyclaceae, and the abundance of NO 3 – dependent Fe oxidizing Dechloromonas reached 20.15% at sampling point I. Acinetobacter, Hydrogenophaga, uncultured_bacterium_f_Rhodocyclaceae, Flavobacterium, and Thauera were the dominant genera in the DBDF. The pathways of N metabolic, carbohydrate metabolism, and energy metabolism all maintained high abundance in the combined process.
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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".