Enhanced CH4 yield by photocatalytic CO2 reduction using TiO2 nanotube arrays grafted with Au, Ru, and ZnPd nanoparticles
Bibliographic record
Abstract
Metal nanoparticle (NP) co-catalysts on metal oxide semiconductor supports are attracting attention as photocatalysts for a variety of chemical reactions. Related efforts seek to make and use Pt-free catalysts. In this regard, we report here enhanced CH 4 formation rates of 25 and 60 μmol·g –1 ·h –1 by photocatalytic CO 2 reduction using hitherto unused ZnPd NPs as well as Au and Ru NPs. The NPs are formed by colloidal synthesis and grafted onto short n-type anatase TiO 2 nanotube arrays (TNAs), grown anodically on transparent glass substrates. The interfacial electric fields in the NP-grafted TiO 2 nanotubes were probed by ultraviolet photoelectron spectroscopy (UPS). Au NP-grafted TiO 2 nanotubes (Au-TNAs) showed no band bending, but a depletion region was detected in Ru NP-grafted TNAs (Ru-TNAs) and an accumulation layer was observed in ZnPd NP-grafted TNAs (ZnPd-TNAs). Temperature programmed desorption (TPD) experiments showed significantly greater CO 2 adsorption on NP-grafted TNAs. TNAs with grafted NPs exhibit broader and more intense UV–visible absorption bands than bare TNAs. We found that CO 2 photoreduction by nanoparticle-grafted TNAs was driven not only by ultraviolet photons with energies greater than the TiO 2 band gap, but also by blue photons close to and below the anatase band edge. The enhanced rate of CO 2 reduction is attributed to superior use of blue photons in the solar spectrum, excellent reactant adsorption, efficient charge transfer to adsorbates, and low recombination losses.
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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".