A novel magnetically separable <scp>pH</scp> sensitive surface‐active iron oxide nanoparticles for the removal of antibiotics (tetracycline) from aquatic environments
Bibliographic record
Abstract
Abstract This research examines the application of magnetic iron‐oxide (Fe 3 O 4 ) nanoparticles (IONPs) for the targeted removal and recovery of tetracycline (TC) from aqueous systems. The IONPs were synthesized through a steady‐state headspace with NH 3(g) at room temperature and pressure without mechanical agitation. IONPs were found to be in a single phase with uniform size distribution and magnetic separability. Effects of pH on surface charge, dispersity, and particle size were studied using zeta potential and DLS. Sorption profiles at various mass loadings for antibiotics (1–25 ppm) and nanoparticles (0.1–0.5 mg/mL) were studied using UV–Vis spectroscopy, Fourier‐transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) imaging and energy dispersive X‐ray (EDX) analysis. Results suggested a rapid sorption of TC onto IONPs with overall TC removal efficiency from wastewater ranging between 70% and 95% depending on temperature (10, 25, and 45°C) and contact time (1–90 min). The investigation into adsorption mechanisms demonstrated that adsorption of TC onto IONPs was feasible, spontaneous, and an endothermic process primarily governed by physisorption. The process well aligned with Freundlich isotherm and pseudo‐second order kinetics. Further, stability of IONPs and desorption ability were also evaluated. The results suggest that IONPs can be used as a sustainable alternative to commercial adsorbent for removal of antibiotics from waterbodies.
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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".