Effect of pyrite particle size on the denitrification performance of autotrophic or split‐mixotrophic bioreactors supported by pyrite/polycaprolactone
Bibliographic record
Abstract
Abstract In this study, a pyrite‐based autotrophic denitrification (PAD) system, a polycaprolactone (PCL)‐supported heterotrophic denitrification (PHD) system, and a pyrite+PCL‐based split‐mixotrophic denitrification (PPMD) system were constructed. The pyrite particle size was controlled in 1–3, 3–5, or 5–8 mm in both the PAD and PPMD systems to investigate the effect of pyrite particle size on the denitrification performance of autotrophic or split‐mixotrophic bioreactors. It was found that the PAD system achieved the best denitrification efficiency with an average removal rate of 98.98% in the treatment of 1‐ to 3‐mm particle size, whereas it was only 19.24% in the treatment of 5‐ to 8‐mm particle size. At different phases of the whole experiment, the nitrate removal rates of both the PHD and PPMD systems remained stable at a high level (>94%). Compared with the PAD or PHD system, the PPMD system reduced the concentrations of sulfate and chemical oxygen demand in the final effluent efficiently. The interconnection network diagram explained the intrinsic metabolic pathways of nitrogen, sulfur, and carbon in the three denitrification systems at different phases. In addition, the microbial community analysis showed that the PPMD system was beneficial for the enrichment of Firmicutes. Finally, the impact mechanism of pyrite particle size on the performance of the PPMD system was proposed. Practitioner Points The reduction of pyrite particle size was beneficial for improving the efficiency of the PAD process. The change in particle size had an effect on NO2−‐N accumulation in the PAD system. The accumulation of NH4+‐N in the PPMD system increased with the decrease in particle size. The reduction of pyrite particle size increased the production of SO42− in the PAD and PPMD systems. The correlations among the effluent indicators of the PAD and PPMD systems could be well explained.
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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 | 0.001 | 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".