Enhanced solar light photocatalytic and antimicrobial activity of green noble metal/TiO<sub>2</sub> nanorods
Bibliographic record
Abstract
Au/TiO2 and Pt/TiO2 nanocomposites have been processed using a green method. Au and Pt colloidal nanoparticles have been primarily synthesized by mixing their corresponding metal ions with an aqueous solution of corn husk extract, followed by anchoring on the as-synthesized TiO2 nanorods. The structural and morphological properties of green-prepared nanomaterials were systematically investigated by various techniques. The UV–VIS absorption measurements confirmed the formation of colloidal Au and Pt with λmax at 550 and 345 nm, respectively. TEM results show anchoring of spherical Au particles (40 nm) on TiO2 nanorods while smaller Pt particles have been observed on Pt/TiO2 composite. It has been shown that the highest visible light harvesting capability was earned for Au/TiO2 composite due to the surface plasmon resonance (SPR) of Au nanoparticles. The photocatalytic and antimicrobial activity of the nanomaterials was investigated for disinfection of Escherichia coli under solar light irradiation. The green synthesized nanocomposites showed enhanced solar light photocatalytic and antimicrobial activity. The best photocatalytic and antimicrobial activity was obtained for Au/TiO2. This evidences the enhanced SPR of Au nanoparticles and hence an enhancement in the solar light accessible by TiO2 so that a higher amount of reactive oxygen species can be generated, which enhances photocatalytic and antibacterial activity.
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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".