Plasmonic photocatalysis applied to solar fuels
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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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".