The role of Cu and film thickness on the photocatalytic activity of mesoporous spin coated TiO2 films
Bibliographic record
Abstract
Most practical applications of photocatalysts will involve coatings on an inert support; here we have examined how copper doping of spin coated porous TiO2 films affects their physical characteristics and photocatalytic activity. The photocatalytic degradation of stearic acid was used as a measure of photocatalytic activity for catalysts spin coated from a sol-gel onto glass with 0, 0.1, 0.5, 1, 2.5 and 5 wt% copper introduced into the catalyst lattice before gelation. The effects of spin coating speed on film thickness, structure and band gap were studied and the nature of the copper incorporated into the films examined with XPS and XANES measurements. Increased spin coating speeds reduces the thickness of the deposited films from ~ 50 μm to ~ 20 μm until a spin speed of ~ 3000 rpm at which point non-Newtonian behaviour of the gels prevents further reductions in film thickness. A larger effect on film thickness is the presence of the added copper nitrate which results in thinner films. After calcining, XANES shows the bulk of the copper to be in a Cu(II) state but at the surface of the thinnest, most active films XPS shows only Cu(I). Photocatalytic activity is much more strongly affected by the presence of the copper than the thickness of the films with 0.1 wt% Cu catalysts as much as 10 times more active than the undoped catalysts. Increasing the copper content, however, reduces activity until at ~ 5 wt% activity is lower than for the pure TiO2 films.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".