Rationalizing the Plasmonic Contributions to the Enhancement of Singlet Oxygen Production
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Bibliographic record
Abstract
The strong interaction between plasmonic metal nanoparticles and photosensitizers can significantly amplify their singlet oxygen (¹O₂) production. However, improving the performance of these hybrid plasmonic nanostructures is hampered by the lack of understanding of how their plasmonic properties impact the enhancement of ¹O₂ production. Here, we report that a Au core-based nanoparticle can outperform a Ag one. This result is striking as Ag is referred to as a better plasmonic metal than Au and forms the basis of our investigation. We use a novel approach based on a mini meta-analysis to elucidate and quantify the near- and far-field contributions to the plasmon-enhanced ¹O₂ production by using a highly tunable model hybrid photosensitizer–metal core@shell nanoparticle. The correlation between time-resolved ¹O₂ measurements and the experimental and simulated plasmonic optical properties was achieved by comparing the results of four new nanoparticles of different core composition (Au and Ag) and sizes (from 20 to 120 nm in diameter) with the data published in previous studies. Altogether, experiments and modeling in conjunction with statistical analysis revealed that, while the near and far fields work in synergy, it is the near field that dominates the photosensitizer–metal interactions and ultimately dictates the enhancement of ¹O₂ production. This work improves our understanding of factors important in determining ¹O₂ enhancement and paves the way to a quantitative description of plasmon–photosensitizer interactions for the rational design of complex nanostructures for boosting ¹O₂ production.
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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 | 0.001 | 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 it