Thinking outside the shell: novel sensors designed from plasmon-enhanced fluorescent concentric nanoparticles
Bibliographic record
Abstract
The alteration of photophysical properties of fluorophores in the vicinity of a metallic nanostructure, a phenomenon termed plasmon- or metal-enhanced fluorescence (MEF), has been investigated extensively and used in a variety of proof-of-concept demonstrations over the years. A particularly active area of development in this regard has been the design of nanostructures where fluorophore and metallic core are held in a stable geometry that imparts improved luminosity and photostability to a plethora of organic fluorophores. This minireview presents an overview of MEF-based concentric core-shell sensors developed in the past few years. These architectures expand the range of applications of nanoparticles (NPs) beyond the uses possible with fluorescent molecules. Design aspects that are being described include the influence of the nanocomposite structure on MEF, notably the dependence of fluorescence intensity and lifetime on the distance to the plasmonic core. The chemical composition of nanocomposites as a design feature is also discussed, taking as an example the use of non-noble plasmonic metals such as indium as core materials to enhance multiple fluorophores throughout the UV-Vis range and tune the sensitivity of halide-sensing fluorophores operating on the principle of collisional quenching. Finally, the paper describes how various solid substrates can be functionalized with MEF-based nanosensors to bestow them with intense and photostable pH-sensitive properties for use in fields such as medical therapy and diagnostics, dentistry, biochemistry and microfluidics.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".