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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".