TiO2/SnO2 photocatalysts by electrospinning and atomic layer deposition for pharmaceutical contaminant removal
Bibliographic record
Abstract
Advanced technologies, including photocatalysis, are required to address the increasing global need of clean water. Titanium dioxide (TiO 2 ) is often used as photocatalyst for pollutant removal, but its performance is hampered by its large band gap and fast charge carrier recombination. This study describes the synthesis, characterization, and photocatalytic performance of TiO 2 /tin oxide (SnO 2 ) core-shell nanofibers for the degradation of acetaminophen (ACT), a persistent pharmaceutical pollutant. TiO 2 nanofibers, fabricated by electrospinning, were coated with thin SnO 2 films by atomic layer deposition (ALD). After their structural, morphological, and chemical characterization, TiO 2 and TiO 2 /SnO 2 composites were tested as photocatalysts to degrade ACT under UV light. Within 40 minutes, 99.8% and 70% of ACT was degraded in the presence of the optimal TiO 2 /SnO 2 composite (SnO 2 layer thickness of 5 nm) and of TiO 2 nanofibers, respectively. Moreover, the optimal TiO 2 /SnO 2 composite showed excellent recyclability and stability over five consecutive cycles. Hydroxyl radicals ( • OH), superoxide anions ( • O 2 - ), and holes (h + ) were the main reactive species implicated in ACT removal. Density functional theory (DFT) modeling confirmed that the band alignment between TiO 2 and SnO 2 enhanced charge separation. This study demonstrates that TiO 2 /SnO 2 is a promising photocatalyst to remove pharmaceutical contaminants from the environment.
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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) | 0.001 | 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".