Plasmonic photocatalysis applied to solar fuels
Why this work is in the frame
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Bibliographic record
Abstract
The induction of chemical processes by plasmonic systems is a rapidly growing field with potentially many strategic applications. One of them is the transformation of solar energy into chemical fuel by the association of plasmonic metal nanoparticles (M NPs) and a semi-conductor (SC). When the localized surface plasmon resonance (LSPR) and the SC absorption do not match, one limitation of these systems is the efficiency of hot electron transfer from M NPs to SC through the Schottky barrier formed at the M NP/SC interfaces. Here we show that high surface area 1 wt% Au/TiO2-UV100, prepared by adsorption of a NaBH4-protected 3 nm gold sol, readily catalyzes the photoreduction of carbon dioxide with water into methane under both solar and visible-only irradiation with a CH4vs. H2 selectivity of 63%. Tuning Au NP size and titania surface area, in particular via thermal treatments, highlights the key role of the metal dispersion and of the accessible Au-TiO2 perimeter interface on the direct SC-based solar process. The impact of Au NP density in turn provides evidence for the dual role of gold as co-catalyst and recombination sites for charge carriers. It is shown that the plasmon-induced process contributes up to 20% of the solar activity. The plasmon-based contribution is enhanced by a large Au NP size and a high degree of crystallinity of the SC support. By minimizing surface hydroxylation while retaining a relatively high surface area of 120 m2 g-1, pre-calcining TiO2-UV100 at 450 °C leads to an optimum monometallic system in terms of activity and selectivity under both solar and visible irradiation. A state-of-the-art methane selectivity of 100% is achieved in the hot electron process.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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