Inter‐Nanoparticle FRET for Biosensing: Photophysics Versus Size
Bibliographic record
Abstract
Förster resonance energy transfer (FRET) enables the quantification of nanoscale distances and biomolecular interactions. Luminescent nanoparticles (NPs) are frequently combined with fluorescent dyes or proteins in FRET. However, their use in inter-NP FRET with luminescent NPs as both donor and acceptor remains less common due to the inherent size constraints that can limit FRET efficiencies. This review critically examines the early advances and current state-of-the-art of inter-NP FRET with a focus on the most commonly used NPs, namely quantum dots (QDs), upconversion nanoparticles (UCNPs), and fluorescent organic nanoparticles (FONs). We show how NP sizes, surface shells, and coatings increase FRET-distances; propose how these drawbacks can be overcome or outcompeted by the unique photophysical properties of the NPs; and discuss representative examples of inter-NP FRET for biosensing. High luminescence brightness, outstanding photostability, near-infrared excitation and emission, spectral and temporal multiplexing, and large surfaces for multifunctional bioconjugation are only some of the features that make luminescent NPs very attractive for biosensing, and FRET efficiencies up to 90% have demonstrated their potential for successful translation into bioanalytical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".