Tuning plasmon transitions and their applications in organic photochemistry
Bibliographic record
Abstract
The ketone-photoinduced formation of Au, Ag, and Cu nanoparticles from their corresponding ions in solution has been carried out using benzoin photoinitiators. Ketones are good photosensitizers for nanoparticle synthesis not because of the energy they can absorb or deliver, but rather because of the reducing free radicals they can generate. Efficient photochemical nanoparticle generation thus requires a careful selection of substrates and experimental conditions such that free radical generation occurs with high quantum efficiency, where metal ion precursors do not inhibit radical formation. A key consideration to achieve nanoparticle synthesis with short exposure times is to minimize excited-state quenching by metal ions. Applications of nanostructures in catalysis require control of the nanoparticle characteristics, such as dimension, morphology, and surface properties. Part of this article describes the strategies to modify photochemically prepared particles. Finally, we illustrate some of the nanoparticle applications that interest us, with some emphasis on plasmon-mediated processes.
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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".