Quantum Dots as Simultaneous Acceptors and Donors in Time-Gated Förster Resonance Energy Transfer Relays: Characterization and Biosensing
Bibliographic record
Abstract
The unique photophysical properties of semiconductor quantum dot (QD) bioconjugates offer many advantages for active sensing, imaging, and optical diagnostics. In particular, QDs have been widely adopted as either donors or acceptors in Förster resonance energy transfer (FRET)-based assays and biosensors. Here, we expand their utility by demonstrating that QDs can function in a simultaneous role as acceptors and donors within time-gated FRET relays. To achieve this configuration, the QD was used as a central nanoplatform and coassembled with peptides or oligonucleotides that were labeled with either a long lifetime luminescent terbium(III) complex (Tb) or a fluorescent dye, Alexa Fluor 647 (A647). Within the FRET relay, the QD served as a critical intermediary where (1) an excited-state Tb donor transferred energy to the ground-state QD following a suitable microsecond delay and (2) the QD subsequently transferred that energy to an A647 acceptor. A detailed photophysical analysis was undertaken for each step of the FRET relay. The assembly of increasing ratios of Tb/QD was found to linearly increase the magnitude of the FRET-sensitized time-gated QD photoluminescence intensity. Importantly, the Tb was found to sensitize the subsequent QD-A647 donor-acceptor FRET pair without significantly affecting the intrinsic energy transfer efficiency within the second step in the relay. The utility of incorporating QDs into this type of time-gated energy transfer configuration was demonstrated in prototypical bioassays for monitoring protease activity and nucleic acid hybridization; the latter included a dual target format where each orthogonal FRET step transduced a separate binding event. Potential benefits of this time-gated FRET approach include: eliminating background fluorescence, accessing two approximately independent FRET mechanisms in a single QD-bioconjugate, and multiplexed biosensing based on spectrotemporal resolution of QD-FRET without requiring multiple colors of QD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".