A Comprehensive Investigation Into How Metal Nanoparticles Enhance Photochemical Processes
Bibliographic record
Abstract
Integrated nanoparticle-fluorophore systems are an emerging class of hybrid materials with potential for a variety of applications. Within these systems, Metal-Enhanced Fluorescence (MEF) is a phenomenon that arises through the unique near-field interactions between organic fluorophores and metallic nanoparticles. These interactions can enhance fluorophore brightness by operating under two mechanistic pathways: increased fluorophore excitation, and increased fluorophore emission. It is critical, however, to garner an understanding of the balance between the two mechanisms in order to optimize its use for customized applications. In this work, silver nanoplates (AgNP) were synergistically integrated with a photoactivatable system consisting of a fluorogenic boron dipyrromethene (BODIPY) and select photoacid generators (PAGs). Light irradiation of the PAGs promotes the release of acid, which encourages the formation of a fluorescent BODIPY analogue. AgNP were found to enhance the output of steady-state emission via MEF. Furthermore, single molecule total internal reflection fluorescence microscopy (TIRFM) was executed to distinguish the relative contributions of the two MEF mechanisms between the quenched and fluorescent BODIPY fluorochromes and AgNP. Interestingly, increased excitation was the dominant MEF mechanism amongst both dyes. Finally, the rational design of a ‘uninanomolecular’ superstructure incorporating both a photoactivatable dihydrofluorescein dye and AgNP within a single system was investigated. Two synthetic approaches were proposed, however neither method yielded superstructures that photoactivated with subsequent MEF. Overall, this work aids to advance the understanding of complex particle-molecule interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".