Immobilization of CdSe/ZnS quantum dots on glass beads for the detection of nucleic acid hybridization using fluorescence resonance energy transfer
Bibliographic record
Abstract
The photoluminescence (PL) properties of quantum dots (QD) are of significant interest in the development of new methods for bioanalysis. Multiplexed solid-phase nucleic acid hybridization assays that use immobilized QDs as donors in fluorescence resonance energy transfer (FRET) are one such example, and offer several unique advantages over other methods. In this work, new interfacial chemistry is described for the immobilization of red-emitting CdSe/ZnS QDs on glass beads for use in hybridization assays. The beads were chemically modified with a dithiolate surface ligand and the QDs immobilized via self-assembly. Further derivatization of the QDs with dithiolate-terminated probe oligonucleotides enabled a hybridization assay that could detect unlabeled target down to nanomolar levels with discrimination of single base-pair mismatches. The use of beads as an immobilization platform afforded shorter analysis times and superior reusability compared to previous studies using optical fibers. Hybridization between probe, target, and Alexa Fluor 647 (A647) labeled reporter oligonucleotides in a sandwich format generated a spectroscopic signal by introducing the proximity needed for FRET between the QDs and A647. The results indicate clear directions for the optimization of solid-phase hybridization assays, and are important for the future development of true multiplexed biosensors based on QDs and FRET.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".