Collective excitation of plasmon-coupled Au-nanochain boosts photocatalytic hydrogen evolution of semiconductor
Bibliographic record
Abstract
Localized surface plasmon resonance (LSPR) offers a valuable opportunity to improve the efficiency of photocatalysts. However, plasmonic enhancement of photoconversion is still limited, as most of metal-semiconductor building blocks depend on LSPR contribution of isolated metal nanoparticles. In this contribution, the concept of collective excitation of embedded metal nanoparticles is demonstrated as an effective strategy to enhance the utilization of plasmonic energy. The contribution of Au-nanochain to the enhancement of photoconversion is 3.5 times increase in comparison with that of conventional isolated Au nanoparticles. Experimental characterization and theoretical simulation show that strongly coupled plasmonic nanostructure of Au-nanochain give rise to highly intensive electromagnetic field. The enhanced strength of electromagnetic field essentially boosts the formation rate of electron-hole pair in semiconductor, and ultimately improves photocatalytic hydrogen evolution activity of semiconductor photocatalysts. The concept of embedded coupled-metal nanostructure represents a promising strategy for the rational design of high-performance 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) | 0.000 | 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 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".