Concurrent Modulation of Quantum Dot Photoluminescence Using a Combination of Charge Transfer and Förster Resonance Energy Transfer: Competitive Quenching and Multiplexed Biosensing Modality
Bibliographic record
Abstract
An emerging trend with semiconductor quantum dots (QDs) is their use as scaffolds to assemble multiple energy transfer pathways. Examples to date have combined various competitive and sequential Förster resonance energy transfer (FRET) pathways between QDs and fluorescent dyes, luminescent lanthanide complexes, and bioluminescent proteins. Here, we show that the photoluminescence (PL) of QD bioconjugates can also be modulated by a combination of FRET and charge transfer (CT), and characterize the concurrent effects of these mechanistically different pathways using PL measurements at both the ensemble and the single particle level. Peptides were distally labeled with either a fluorescent dye that quenched QD PL through FRET or a ruthenium(II) phenanthroline complex that quenched QD PL through electron transfer. The labeled peptides were assembled around a central CdSe/ZnS QD at different ratios, tuning the relative rates of FRET and CT, which were competitive quenching pathways. The concurrent effects of FRET and CT were predictable from a rate analysis that was calibrated to the isolated effects of each of these pathways. Notably, the dye/QD PL intensity ratio reflected changes in the relative rate of FRET but was approximately independent of CT. In turn, the sum of the QD and dye PL intensities, when adjusted for quantum yields, reflected changes in the relative rate of CT quenching, approximately independent of FRET. The capacity for multiplexed sensing of protease activity was demonstrated using these two orthogonal detection channels. Combined CT-FRET configurations with QDs are thus promising for applications in bioanalysis, sensing, and imaging, and may prove useful in other photonic 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 | 0.001 |
| 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".