Embedded <scp>nanoFeCu</scp> for sewage treatment: Laboratory‐scale and pilot studies
Bibliographic record
Abstract
Abstract Bimetallic nanoparticles have been widely studied for wastewater treatment, but the study of nanoFeCu for sewage treatment is minimal. In the previous work, ammonia was removed by nanoFeCu via an oxidation reaction, and nitrogen gas was released. However, the performance and reusability of nanoFeCu in treating industrial wastewater have not been reported elsewhere. This study revealed the performance of nanoFeCu for sewage treatment on both laboratory‐scale and pilot‐scale for the first time. A varied mass of embedded nanoFeCu (eFeCu4) was exposed to sewage water, and the quality of the effluent was measured in terms of ammonia, biological oxygen demand (BOD), and chemical oxygen demand (COD) removal. Fe 2+ and Cu 2+ concentrations were measured to determine the stability of eFeCu4 in nine reuse cycles. Results showed that the laboratory‐scale experiment removed 20%–30% ammonia from sewage. A similar removal rate was reported in all nine cycles of reuse, which confirmed the usability and reliability of eFeCu4. In the pilot‐scale study, ammonia was removed from ~22.3 to ~4.8 mg/L, while BOD and COD were reduced from ~204 to ~56 mg/L and ~71 to ~39.7 mg/L, respectively. The treated effluent quality complies with the effluent discharge standard of Malaysia, and it is also comparable with the effluent quality at sewage treatment plants in Malaysia and overseas. In conclusion, nanoFeCu could be an alternative method for sewage treatment due to its stability and pollutant removal performance. A sustainability and cost‐effectiveness study should be conducted to determine the feasibility of a full‐scale application.
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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) | 0.001 | 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".