Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fluorescence spectroscopy with strong emitters is a remarkable tool with ultra-high sensitivity for detection and imaging down to the single-molecule level. Plasmon-enhanced fluorescence (PEF) not only offers enhanced emissions and decreased lifetimes, but also allows an expansion of the field of fluorescence by incorporating weak quantum emitters, avoiding photobleaching and providing the opportunity of imaging with resolutions significantly better than the diffraction limit. It also opens the window to a new class of photostable probes by combining metal nanostructures and quantum emitters. In particular, the shell-isolated nanostructure-enhanced fluorescence, an innovative new mode for plasmon-enhanced surface analysis, is included. These new developments are based on the coupling of the fluorophores in their excited states with localized surface plasmons in nanoparticles, where local field enhancement leads to improved brightness of molecular emission and higher detection sensitivity. Here, we review the recent progress in PEF with an emphasis on the mechanism of plasmon enhancement, substrate preparation, and some advanced applications, including an outlook on PEF with high time- and spatially resolved properties.
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.
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) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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