Enhanced CH4 yield by photocatalytic CO2 reduction using TiO2 nanotube arrays grafted with Au, Ru, and ZnPd nanoparticles
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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 CH4 formation rates of 25 and 60 μmol·g–1·h–1 by photocatalytic CO2 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 TiO2 nanotube arrays (TNAs), grown anodically on transparent glass substrates. The interfacial electric fields in the NP-grafted TiO2 nanotubes were probed by ultraviolet photoelectron spectroscopy (UPS). Au NP-grafted TiO2 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 CO2 adsorption on NP-grafted TNAs. TNAs with grafted NPs exhibit broader and more intense UV–visible absorption bands than bare TNAs. We found that CO2 photoreduction by nanoparticle-grafted TNAs was driven not only by ultraviolet photons with energies greater than the TiO2 band gap, but also by blue photons close to and below the anatase band edge. The enhanced rate of CO2 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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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 | 0.001 |
| 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 | 0.001 |
| 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 it