Impact of Incoherent Coupling within Localized Surface Plasmon Resonance on Singlet Oxygen Production in Rose Bengal-Modified Silica-Coated Silver Nanoshells (SiO<sub>2</sub>@Ag@SiO<sub>2</sub>-RB)
Bibliographic record
Abstract
The use of metal-containing nanoparticles capable of localized surface plasmon resonance (LSPR)-enhanced singlet oxygen ( 1 O 2 ) production, via photosensitization, are currently promising candidates for cytotoxic medical purposes. Despite the ongoing advances in fabricating plasmonic nanomaterials, much of the insight into the fundamental mechanisms governing the photosensitizer (PS)–plasmon interactions remain unexplored. Silver nanoshells (SiO 2 @Ag) possess seemingly promising optical properties to investigate the underlying LSPR effects on photosensitization, while also raising questions about the nature of LSPR generation in such dielectric-core metallic-shell nanostructures. In the present study, we synthesize and use silica-coated hybrid SiO 2 @Ag nanoparticles with an outer surface-conjugated PS, and Ag shell densities ranging from ∼50 to >90%, to experimentally investigate these nanostructures’ LSPR-enhanced 1 O 2 generation properties. Using both direct and indirect 1 O 2 detection approaches, and with rigorous consideration of 1 O 2 kinetics, we show that the SiO 2 @Ag-based structures produce severe inner filter effects in bulk-solution measurements, making them impractical for photodynamic applications. Furthermore, we make a case for the SiO 2 @Ag nanoparticles’ poor LSPR generation efficiency to be the consequence of the species’ incoherently coupled plasmonic field and resultant lack of enhancement of the nearby chromophore’s photophysical properties.
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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".