Adsorption and Hybridization of Oligonucleotides on Mercaptoacetic Acid-Capped CdSe/ZnS Quantum Dots and Quantum Dot−Oligonucleotide Conjugates
Bibliographic record
Abstract
Interest in the unique optical properties of quantum dots (QDs) has resulted in the development QD-bioconjugates for imaging and diagnostics. Although these applications are numerous, considerably less is known about the interactions between QDs and biomolecules. In this work, we describe hydrogen-bonding interactions between oligonucleotides and CdSe/ZnS quantum dots capped with mercaptoacetic acid ligands. The strength of the interactions can be modulated by changes in the pH and ionic strength, the addition of formamide, and differences between ssDNA and dsDNA. Fluorescence resonance energy transfer experiments have shown that conjugated oligonucleotides adopt a conformation that lies across the surface of the QD. The hydrogen-bonding interactions also affect the kinetics of hybridization with QD-DNA conjugates and the thermal stability of QD-conjugated dsDNA. The former is analogous to conventional solid-phase hybridization, where stronger oligonucleotide adsorption leads to faster kinetics. With respect to the latter, interactions with the QD surface can sharpen the melt transition and alter the melt temperature of dsDNA. These effects are largely absent when adsorptive interactions are minimized.
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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 | 0.000 | 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".