Energy Transfer Pathways in a Quantum Dot-Based Concentric FRET Configuration
Bibliographic record
Abstract
The unique optical properties of semiconductor quantum dots (QDs) are highly advantageous for biological imaging and analysis, particularly when combined with Förster resonance energy transfer (FRET). A recent innovation in this area has been concentric FRET (cFRET), wherein QDs are assembled with multiple copies of two different types of fluorescent label. Although multifunctional biological probes have been developed utilizing cFRET, a detailed photophysical analysis of cFRET has not been undertaken, and energy transfer in these probes has been understood only qualitatively. Here, we characterize a prototypical QD-(A555) M -(A647) N cFRET configuration through photoluminescence (PL) intensity, decay, and photobleaching measurements. This cFRET configuration combines a central, green-emitting QD with Alexa Fluor 555 (A555) and Alexa Fluor 647 (A647) dyes that are assembled to QDs through peptide linkers, where M and N are the numbers of A555 and A647 per QD. Following initial photoexcitation of the QD, the energy transfer pathways are QD-to-A555 and QD-to-A647, which compete with one another, and A555-to-A647, which occurs subsequent to QD-to-A555 energy transfer. A rate analysis, calibrated to the conventional QD-(A555) M and QD-(A647) N FRET systems, accurately predicts quenching efficiencies and permits a first approximation of dye/QD PL intensity ratios in the cFRET configurations. CdSe/CdS/ZnS QDs and CdSeS/ZnS QDs of different sizes but similar emission characteristics are used for these experiments, and they demonstrate the general applicability of the analysis. The interplay between the three FRET pathways and nonideal behavior within this system is discussed with directions for future research. Overall, this study provides a framework and predictive power for the rational design and optimization of novel cFRET probes and biosensors for biological 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".