Removal of Cefixime from Wastewater Using a Superb nZVI/Copper Slag Nanocomposite: Optimization and Characterization
Bibliographic record
Abstract
Nowadays, hospital wastewater contains a high concentration of toxic pharmaceutical contaminants, posing a significant threat to the environment, and human and animal life. Cefixime (CFX) is one such toxic contaminant that has a detrimental impact on both aquatic and terrestrial ecosystems. Therefore, it is essential to remove this compound using non-toxic and environmentally friendly procedures to ensure healthy drinking water. In this study, a low-cost and eco-friendly nano adsorbent (nZVI/copper slag) was synthesized and characterized using FESEM, XRD, EDX, FTIR, and zeta potential to remove CFX from wastewater. The Response Surface Methodology (RSM) was used to evaluate the effects of experimental factors including adsorbent dosage (2–10 g/L), pollutant concentration (10–30 mg/L), pH (2–10), and contact time (10–50 min) for efficient CFX elimination. The optimal conditions (adsorbent dosage: 7.79 g/L, pollutant concentration: 19.42 mg/L, pH: 4.59, and reaction time: 36.17 min) resulted in 98.71% CFX removal. The adsorption isotherm and kinetics data showed that the pseudo-second-order kinetics and Langmuir isotherm models were appropriate for CFX elimination. Furthermore, the nano adsorbent demonstrated 90% CFX elimination after up to six repeated cycles in regeneration and reusability testing. Finally, the nZVI/CS nano adsorbent can be an effective and promising solution for removing CFX from wastewater.
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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 | 0.001 | 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".