Solar and <scp>UV</scp> for photocatalytic degradation of spiramycin using nitrogen‐doped <scp>TiO<sub>2</sub></scp>
Bibliographic record
Abstract
Abstract The extensive use of antibiotics in both veterinary and clinical settings has unintentionally led to their presence in surface waters, raising significant concerns. Macrolides, a class of antibiotics classified as ‘emerging contaminants’, have the potential to infiltrate the environment and negatively impact human health. To address this issue, band gap engineering through surface modification of titanium dioxide (TiO2) has shown promising efficacy in mitigating such harmful contaminants. In our study, spiramycin (SPR) was subjected to both UV and solar radiation in the presence of a suitable catalyst in a slurry batch reactor. The synthesized catalysts were characterized using various techniques, including X‐ray diffraction (XRD), field emission scanning electron microscopy with energy dispersive X‐ray spectroscopy (FESEM‐EDX), ultraviolet–visible diffuse reflectance spectroscopy (UV–Vis DRS), and Brunauer–Emmett–Teller (BET) analysis. Optimization of key parameters indicated a maximum degradation of 91.08% degradation for 10 mgL−1 SPR with 2NTiO2 within 180 minutes under solar radiation. The reaction kinetics revealed that SPR degradation followed the Langmuir–Hinshelwood (L–H) model. Additionally, the intermediates formed during the degradation process were identified using liquid chromatography–mass spectroscopy (LCMS) and a degradation pathway was proposed. A significant reduction in the toxicity of SPR was observed following the photocatalytic treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".