Engineering the Absorption and Field Enhancement Properties of Au–TiO<sub>2</sub> Nanohybrids <i>via</i> Whispering Gallery Mode Resonances for Photocatalytic Water Splitting
Bibliographic record
Abstract
Recently, surface plasmon resonance (SPR) effects have been widely used to construct photocatalysts which are active in the visible spectral region. Such plasmonic photocatalysts usually comprise a semiconductor material transparent in the visible range (such as TiO2) and plasmonic nano-objects (e.g., Au nanoparticles (Au NPs)). Specific SPRs, though, only partially cover the visible spectrum and feature weak light absorption. Here, we explore the unique role played by whispering gallery mode (WGM) resonances in the expression of the photocatalytic activity of plasmonic photocatalysts. Using numerical simulations, we demonstrate that, by solely exploiting a proper geometrical arrangement and WGM resonances in a TiO2 sphere, the plasmonic absorption can be extended over the entire visible range and can be increased by more than 40 times. Furthermore, the local electric field at the Au-TiO2 interface is also considerably enhanced. These results are experimentally corroborated, by means of absorption spectroscopy and Raman measurements. Accordingly, such WGM-assisted plasmonic photocatalysts, when employed in water splitting experiments, exhibit enhanced activity in the visible range. Our findings show a promising and straightforward way to design full solar spectrum photocatalysts.
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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".