Fluorescence activation, patterning and enhancement with photogenerated radicals, a prefluorescent probe and silver nanostructures
Bibliographic record
Abstract
We synthesized a dual fluorogenic system based on a boron-dipyrromethene (BODIPY) dye coupled with a paramagnetic 2,2,6,6-tetramethylpiperidine-N-oxyl (TEMPO) nitroxide. Electron exchange facilitates the non-radiative relaxation of the singlet state of the chromophore, partially quenching the fluorescence from the BODIPY moiety. Nonetheless, the system can be ‘activated’ in solution or thin polymer films by ultraviolet (UVA) light in the presence of a free radical initiator. UVA illumination promotes the decomposition of the initiator, resulting in the release of a carbon-centered free radical. The dye-TEMPO probe can concomitantly trap the radical or undergo H-abstraction. Both processes consequently turn on the fluorescence and are reversible in nature. We used this protocol to optically imprint a fluorescent pattern on a surface. Moreover, we investigated metal-enhanced fluorescence (MEF) effects by silver nanostructures (AgNP) as a means to improve the performance of our molecular strategy for fluorescence activation. Overall, this work contributes toward the development of improved nanoparticle–dye systems for applications of light-activated fluorescence such as multicolour fluorescence patterning and microscopy, where shorter reaction times, milder conditions, reversibility and a more diverse selection of both excitation sources and emission wavelengths are beneficial.
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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".