Flow rate impact on the performance of immobilized <scp>nanoFeCu</scp> for sewage treatment and its reusability
Bibliographic record
Abstract
Abstract Ammonia pollution is a global issue, and it endangers human and aquatic life. The role of nanoFeCu in oxidizing ammonia has been identified, but its practicality and performance on the pilot scale are still unclear. This study was conducted to investigate immobilized nanoFeCu's performance in terms of ammonia, nitrate, nitrite, biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS) by using sewage as feed. The effect of sewage flow rate was studied to determine the optimum operating condition of a pilot‐scale reactor. The reusability test was conducted to address sustainability concerns. The nanoFeCu was synthesized, immobilized in polymer clay, and placed into the reactor filled with sewage at a varied flow rate of from 210 to 1200 mL/min. Results showed that a higher sewage flow rate increased the ammonia removal rate within a shorter time but exhausted the immobilized nanoFeCu at a higher rate. Nitrate, nitrite, and TSS have a similar removal trend as ammonia. 800 mL/min was identified as the optimum flow rate with effluent ammonia concentration below 10 ppm in 9 h and maintained for 12 h. A reusability study showed that immobilized nanoFeCu could be reused for at least 10 successive cycles. The stability and performance of immobilized nanoFeCu suggested that it could be an alternative to treat sewage wastewater for real‐life 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".