Evaluation of quantum dot-based concentric FRET configurations with a fluorescent dye and dark quencher for multiplexed bioanalyses
Bibliographic record
Abstract
Semiconductor quantum dots (QDs) continue to emerge as a highly advantageous platform for bioanalysis. Their unique physical and optical properties are especially well suited for Förster resonance energy transfer (FRET)-based bioprobes. Concentric FRET configurations are a recent development in this area of research and are best described as QD bioconjugates where multiple energy transfer pathways have been assembled around the central QD. Concentric FRET configurations permit multiplexed bioanalysis using one type of QD vector, but require more sophisticated analyses than conventional FRET pairs. In this paper, we describe the design and characterization of a new concentric FRET configuration that assembles both a fluorescent dye, Alexa Fluor 555 or Alexa Fluor 647, and a dark quencher, QSY9, at different ratios around a central CdSeS/ZnS QD. It was found that the magnitudes of the total photoluminescence (PL) intensity and either the A555/QD or A647/QD PL ratio can be related to the number of QSY9 and A555 or A647 per QD. The trends in these parameters with changes in the number of each dye molecule per QD have both similarities and differences between configurations with A555 and A647. In each case, a system of equations can be defined to permit calculation of the number of each dye molecule per QD from PL measurements. Both of these dark quencher-based concentric FRET configurations are therefore good candidates for quantitative, multiplexed bioanalysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".