Modelling and optimization of amoxicillin degradation over <scp>ZnO</scp> and glucose oxidase modified <scp> TiO <sub>2</sub> </scp> nanowire
Bibliographic record
Abstract
Abstract Recently, antibiotics posing threats to health and the environment have been highlighted. According to previous reports, amoxicillin is the most hazardous antibiotic in Tehran. In order to overcome the risks associated with the storage and contamination of oxidizing agents, a Janus structure of Ti/TiO 2 /FA/GOx/ZnO is introduced for in situ steady generation and consumption of H 2 O 2 . The most efficient nanowire structure of TiO 2 is achieved at 140°C and 4.5 h. The footprints of GOx as the bioactive site to produce H 2 O 2 and ZnO as the photoactive site to dope TiO 2 have been proved by Raman, Fourier transform infrared (FTIR), X‐ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analyses. For the first time, a Minimum Run Resolution V screening has been implemented for seven factors in an amoxicillin degradation process by the so‐called bio‐photo‐catalyst. According to analysis of variance (ANOVA), the significance of the three most important variables is as follows: amoxicillin concentration (C) > the area covered by TiO 2 nanowires (E) > ZnO/GOx ( F ) ratio. The significance of the model over the 95% confidence interval is confirmed by R 2 of 0.9970, F ‐value of 108.10.
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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 | 0.001 |
| 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 | 0.001 | 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".