Fluorescence Resonance Energy Transfer between a Monolayer of Quantum Dots as Donors adjacent to a Monolayer of Biorecognition Elements as Acceptors
Bibliographic record
Abstract
The unique optical properties of quantum dots (QDs) have been widely used to develop bioassays based on Fluorescence Resonance Energy Transfer (FRET). The solid-phase assays using QDs as FRET donors have numerous practical advantages, including at least 10-fold enhancement in FRET efficiency, which is not immediately explained by theoretical predictions that model energy transfer processes of QDs in two-dimensional layers. Donor-acceptor separation distance, acceptor and donor concentrations were found to influence FRET efficiency in solid-phase assays. A novel immobilization strategy was implemented which made use of the high affinity of imidazole moieties to QD shells to build solid-phase QD bioassays. A 96-well polystyrene plate is presented as a platform suitable for rapid and convenient multiplexed detection. A typical microtiter plate reader was shown to be capable of discriminating different FRET pairs to picomol detection levels of target oligonucleotides. Furthermore, the QD-FRET bioassays provided for mismatch discrimination, and multiple cycles of regeneration were also demonstrated.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".