Whiter, Brighter, and More Stable Cellulose Paper Coated with TiO<sub>2</sub>/SiO<sub>2</sub> Core/Shell Nanoparticles using a Layer‐by‐Layer Approach
Bibliographic record
Abstract
To inhibit the photocatalytic degradation of organic material supports induced by small titania (TiO2 ) nanoparticles, four kinds of TiO2 nanoparticles, that is, commercial P25-TiO2 , commercial rutile phase TiO2 , rutile TiO2 nanorods and rutile TiO2 spheres, prepared from TiCl4 , were coated with a thin, but dense, coating of silica (SiO2 ) using a conventional sol-gel technique to form TiO2 /SiO2 core/shell nanoparticles. These core/shell particles were deposited and fixed as a very thin coating onto the surface of cellulose paper samples by a wet-chemistry polyelectrolyte layer-by-layer approach. The TiO2 /SiO2 nanocoated paper samples exhibit higher whiteness and brightness and greater stability to UV-bleaching than comparable samples of blank paper. There are many potential applications for this green chemistry approach to protect cellulosic fibres from UV-bleaching in sunlight and to improve their whiteness and brightness.
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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) | 0.002 | 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".