SERS Monitoring Hot Electron Generation in Plasmonic Silver Nanocube Monolayers
Bibliographic record
Abstract
Optimizing hot electron generation is important for increasing the efficiency of light harvesting in photovoltaics and photocatalysis. Surface-enhanced Raman spectroscopy (SERS) is a valuable tool to assess the extent of such electron production as it enables monitoring of the hot electron-driven chemical reactions. In this work, SERS was used to compare the hot electrons generated when two localized surface plasmon resonance (LSPR) modes present in plasmonic substrates prepared using silver nanocubes (AgNC) were excited: the dipole LSPR mode and the coupled LSPR mode. The silver plasmonic monolayers were prepared on polystyrene-coated glass slides at various nanoparticle densities and therefore varying intensities of the dipolar and coupled resonances. Dehalogenation of bromothiophenol and chlorothiophenol was used as a hot electron sensitive reaction. The dependence of the reaction yield on the excitation wavelength, power, and extent of plasmonic coupling was assessed by monitoring SERS spectral evolution. It was found that excitation of the dipole mode resulted in a hot electron yield higher than that of the coupling mode for the substrates used. Additionally, it was found that the hot electron yield for the AgNC substrates decreased as the coupling mode strength increased. This work shows the need for a better control over the plasmonic substrate fabrication and characterization methods to allow improved hot electron generation from the coupled modes.
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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".