Optical writing and single molecule reading of photoactivatable and silver nanoparticle-enhanced fluorescence
Bibliographic record
Abstract
-toluenesulfonic, triflic or hydrobromic acid, any of which facilitate optical 'writing' by promoting the formation of a fluorescent species. Metal-enhanced fluorescence (MEF) by AgNP was achieved through rational design of the nano-molecular system in accordance with the principles of radiative decay engineering. In addition to increasing signal to noise, AgNP permitted shorter reaction times and low irradiance - all of which have important implications for applications of fluorescence activation in portable fluorescence patterning, bioimaging and super-resolution microscopy. Single molecule fluorescence microscopy provided unique insights into the MEF mechanism which were hidden by ensemble-averaged measurements, demonstrating that single molecule 'reading' is a valuable tool for characterizing particle-molecule interactions such as those responsible for the relative contributions of increased excitation and plasmophoric emission toward overall MEF. This work represents a step forward in the contemporary design of synergistic nano-molecular systems, and showcases the advantage of fusion between classic spectroscopic techniques and single molecule methods in terms of improved quantitative understanding of fluorophore-nanoparticle interactions, and how these interactions can be exploited to the fullest extent possible.
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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".