Quantum Dot-Based Multidonor Concentric FRET System and Its Application to Biosensing Using an Excitation Ratio
Bibliographic record
Abstract
A plethora of semiconductor quantum dot (QD)-based probes that rely on Förster resonance energy transfer (FRET) have been developed for the optical detection of a wide array of biological targets. To date, the vast majority of these probes have utilized one-step energy transfer between individual donor-acceptor pairs. Here, we report a new multidonor concentric FRET configuration that comprised two fluorescent dyes assembled around a central CdSeS/ZnS QD through peptide linkers. One of these dyes, either Alexa Fluor 555 (A555) or Alexa Fluor 647 (A647), served as an acceptor for both the central QD and the other coassembled dye, Alexa Fluor 488 (A488). The unresolved emission between the A488 and the QD precluded a standard analysis of FRET efficiency from quenching of donor emission intensity or decay time, instead necessitating an analysis of the two energy transfer pathways from deconvolved excitation spectra. When A647 was the terminal acceptor, both the QD-to-A647 and A488-to-A647 energy transfer pathways could be interrogated with blue light, but only the former could be interrogated with violet light. The different degrees of A647 sensitization between these two excitation wavelengths was a predictable function of the above energy transfer efficiencies and dye stoichiometry, and was exploited for quantitative bioanalysis through an excitation ratio, which is in contrast to the conventional use of an emission ratio with FRET-based probes. Detection of the activity of nanomolar concentrations of trypsin, a model protease that hydrolyzed the A488-labeled peptide linker, was demonstrated using both a fluorescence plate reader and a low-cost, compact device that used two low-power light-emitting diodes (LEDs) as excitation sources and a silicon photodiode to detect A647 emission. This multidonor concentric FRET configuration represents a new modality for ratiometric biosensing with QDs and is potentially useful for portable in vitro diagnostics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".