FRET Materials for Biosensing and Bioimaging
Bibliographic record
Abstract
Förster resonance energy transfer (FRET) spectroscopy and microscopy are constantly expanding sensing techniques for analyzing biomolecular interactions. In addition to the biological recognition molecules and biological or chemical analytes, the most important components for designing FRET biosensing systems are the materials that constitute the FRET donor–acceptor pair. These FRET materials consist of small molecules, biological or chemical nanoscaffolds, or nanomaterials that function in the ultraviolet, visible, or infrared spectral range. They can absorb light, fluoresce or phosphoresce with lifetimes ranging from picoseconds to milliseconds, and can be applied for sensing in situ, in vitro, and in vivo . Organic dyes and quenchers, fluorescent or light harvesting proteins, or quantum dots are only some examples from the ever growing FRET material toolbox. A particular example are gold nanoparticles, whose strong localized surface plasmon resonance makes them frequently used as nanosurface energy transfer (NSET) acceptors. After a short recapitulation of FRET and NSET theory, we review a wide variety of FRET and NSET materials, provide representative examples of FRET/NSET systems and applications for each material, and critically discuss the benefits and drawbacks of their properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".