Hot Electrons in TiO2–Noble Metal Nano-Heterojunctions: Fundamental Science and Applications in Photocatalysis
Bibliographic record
Abstract
Plasmonic photocatalysis enables innovation by harnessing photonic energy across a broad swathe of the solar spectrum to drive chemical reactions. This review provides a comprehensive summary of the latest developments and issues for advanced research in plasmonic hot electron driven photocatalytic technologies focusing on TiO2–noble metal nanoparticle heterojunctions. In-depth discussions on fundamental hot electron phenomena in plasmonic photocatalysis is the focal point of this review. We summarize hot electron dynamics, elaborate on techniques to probe and measure said phenomena, and provide perspective on potential applications—photocatalytic degradation of organic pollutants, CO2 photoreduction, and photoelectrochemical water splitting—that benefit from this technology. A contentious and hitherto unexplained phenomenon is the wavelength dependence of plasmonic photocatalysis. Many published reports on noble metal-metal oxide nanostructures show action spectra where quantum yields closely follow the absorption corresponding to higher energy interband transitions, while an equal number also show quantum efficiencies that follow the optical response corresponding to the localized surface plasmon resonance (LSPR). We have provided a working hypothesis for the first time to reconcile these contradictory results and explain why photocatalytic action in certain plasmonic systems is mediated by interband transitions and in others by hot electrons produced by the decay of particle plasmons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".