Enhancement of Quantum Dot Förster Resonance Energy Transfer within Paper Matrices and Application to Proteolytic Assays
Bibliographic record
Abstract
Brightly luminescent semiconductor quantum dots (QDs) continue to play an increasing role in biophotonic research and applications such as bioassays. Here, we present methods for the immobilization of QDs on the cellulose fibers of paper substrates for Förster resonance energy transfer (FRET)-based assays of proteolytic activity. Steady-state and time-resolved fluorescence characterization of FRET between immobilized QDs and self-assembled dye-labeled peptides within the paper matrix revealed a substantial enhancement in energy transfer efficiency. Compared to bulk solution, the rate of energy transfer increased approximately fourfold resulting in a concomitant sevenfold increase in the ratio of FRET-sensitized acceptor dye emission and quenched QD emission. Spots of immobilized QDs with different amounts of dye-labeled peptide had bright luminescence under UV/violet illumination and the net QD and Alexa Fluor 555 (A555) dye emission was visible by eye as different colors. Tryptic digestion of the peptides linking the QD donor and the acceptor dyes resulted in loss of FRET. Changes in the dye/QD photoluminescence (PL) ratio permitted the tracking of proteolytic activity, including the effect of increasing amounts of aprotinin, a potent inhibitor of trypsin. The combination of QDs, a paper substrate, and enhanced FRET has strong potential for developing bioassays.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".