Enhancing Singlet Oxygen Photocatalysis with Plasmonic Nanoparticles
Bibliographic record
Abstract
Photocatalysts able to trigger the production of singlet oxygen species are the topic of intense research efforts in organic synthesis. Yet, challenges still exist in improving their activity and optimizing their use. Herein, we exploited the benefits of plasmonic nanoparticles to boost the activity of such photocatalysts via an antenna effect in the visible range. We synthesized silica-coated silver nanoparticles (Ag@SiO 2 NPs), with silica shells which thicknesses ranged from 7 to 45 nm. We showed that they served as plasmonically active supports for tris(bipyridine)ruthenium(II), [Ru(bpy) 3 ] 2+, and demonstrated an enhanced catalytic activity under white light-emitting diode (LED) irradiation for citronellol oxidation, a key step in the commercial production of rose oxide fragrance. A maximum enhancement of the plasmon-mediated reactivity of approximately 3-fold was observed with a 28 nm silica layer along with a 4-fold enhancement in the emission intensity of the photocatalyst. Using electron energy loss spectroscopy (EELS) and boundary element method simulations, we mapped the decay of the plasmonic signal around the Ag core and provided a rationale for the observed catalytic enhancement. This work provides a systematic analysis of the promising properties of plasmonic NPs used as catalysis-enhancing supports for common homogeneous photocatalysts and a framework for the successful design of such systems in the context of organic transformations.
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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".