Photocatalytic and antibacterial activities of silver and iron doped titania nanoparticles in solution and polyaspartic coatings
Bibliographic record
Abstract
Abstract Visible region active photocatalytic coatings are of interest for antimicrobial activity in low light applications or those employing LED lights with limited UV content. This work examined Ag and Fe doped titania nanoparticles (nTiO 2 ) with varying dopant ranges in polyaspartic polymer coatings for potential light and dark activity. First, the Ag and Fe doped nTiO 2 were synthesized by sol–gel chemistry with varying dopant concentrations, then characterized with respect to their size and aggregate size distribution, crystallinity, and surface and band gap features. The photocatalytic activity was then tested with methylene blue under both AM 1.5 G and visible light. From both sample sets (Ag and Fe doped nTiO 2 ), the best photo catalytically active sample materials were chosen for antibacterial tests with gram-negative Escherichia coli ( E. coli ) and gram-positive Bacillus subtilis ( B. subtilis ) in (a) solution and (b) polyaspartic nanocomposites under UV and visible irradiation. The results showed that Ag doped nTiO 2 samples delivered the best and excellent antibacterial action, even in the dark, attributed to both an enhanced band gap and surface area, as well as a combination of photocatalytic activity and Ag being present at the nanoparticle’s surface. No leaching of Ag at room temperature was observed from the nTiO 2 structure, giving potential for next generation coatings that are both light and dark active.
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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) | 0.001 | 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".