Self-Quenching, Dimerization, and Homo-FRET in Hetero-FRET Assemblies with Quantum Dot Donors and Multiple Dye Acceptors
Bibliographic record
Abstract
The combination of semiconductor quantum dots (QDs) and Förster resonance energy transfer (FRET) is a powerful tool for bioanalysis and imaging. Through FRET, the dye is able to borrow brightness from the QD, and the FRET efficiency can be tuned through the assembly of multiple acceptor dyes per QD. In principle, the fluorescence intensity from acceptor dyes assembled to a QD donor should always exceed that from the dyes alone, but we observed anomalously low acceptor dye fluorescence intensities in FRET systems with a QD donor and multiple Alexa Fluor 610 (A610) or Alexa Fluor 633 (A633) acceptors. In contrast, fluorescence from Alexa Fluor 555 (A555) or Alexa Fluor 647 (A647) acceptors was well-behaved and agreed with theoretical expectations. The difference between these systems was studied using a combination of UV–visible absorption and fluorescence intensity, lifetime, and anisotropy measurements. Anomalous fluorescence from A610 and A633 arose from the formation of nonfluorescent, H-type dimers of these dyes. The monomer–dimer equilibrium was shifted strongly in favor of the dimer as a result of the locally high concentration of dyes assembled to the QD. Both the lower number of monomeric dyes per QD and the introduction of a competitive energy transfer pathway from the QD to dimeric dyes contributed to the low dye fluorescence. Another consequence of the close proximity between the dyes was homo-FRET, which was particularly evident with A555 and A647 acceptors. Homo-FRET did not appear to lead to significant quenching of dye fluorescence, although there was some evidence of low-efficiency energy transfer to dyes that may act as modest energy sinks. The results of this study help inform the rational design of optimized QD–FRET probes for biological applications.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".