Plasmonic nanomaterials for solar-driven photocatalysis
Bibliographic record
Abstract
Plasmonic nanomaterials have spurred significant research interest in enhanced solar-driven photocatalysis due to their strong localized surface plasmon resonance (LSPR). As this rapid-developing research area has begun to raise and answer fundamental questions that determine the photocatalytic performance of plasmonic photocatalysts, it is an opportune time to evaluate the advancement and propose future trajectories. We first outline the fundamentals of LSPR, including its excitation, decay, and influencing factors. We then discuss three main enhancement mechanisms and their applicable scenarios for plasmonic photocatalysis. We then critically assess the recent works performed by our groups concerning plasmon-enhanced photocatalytic reactions. By introducing related works from other researchers, we demonstrate our contributions to the advancements of plasmonic photocatalysis. Finally, we discuss the current challenges and suggest future directions in three aspects: material development, mechanism exploration, and application extension. It is anticipated to delineate the state-of-the-art and direct future research in plasmon-enhanced value-added chemical transformations.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".