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 TiO 2 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 TiO 2 films. • Polymer templating creates mesoporous TiO 2 films as effective photocatalysts. • Contaminant does not penetrate below the surface of the porous catalyst. • Added porosity does not give contaminant more access to catalyst surface. • Low wt% Cu dramatically increases the photocatalytic activity due to Cu(I) ions. • Film thickness not a significant contributor to the photocatalytic activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".