Development of methods to study the conformational dynamics of quantum dot-oligonucleotide conjugates by single molecule spectroscopy
Bibliographic record
Abstract
The optical properties and significant surface area of CdSe/ZnS QDs make such nanoparticles an interesting platform for the preparation of nucleic acid biosensors based on fluorescence resonance energy transfer (FRET). Interactions between QDs and oligonucleotides affect biosensor performance and are not fully understood. Ensemble data obtained via FRET experiments indicated that, on average, 4-5 added oligonucleotides saturated the surface of green emitting QDs. An increase in the number of oligonucleotides per QD appeared to cause the oligonucleotides to transition from collapsed to upright conformations. Since bulk averaging hides details of such processes, methods must be developed and materials identified for studying QD-oligonucleotide conjugates at the single molecule level. Single QDs have been immobilized and fluorescence intensity trajectories measured. High count rates and good photostability were achieved using carboxyl polymer-coated QDs. Modeling of FRET efficiency based on the dimensions of QDs and oligonucleotides indicated that transitions between collapsed and upright conformations can be accurately measured based on changes in QD fluorescence lifetime. The ultimate goal of this work is to elucidate QD-oligonucleotide dynamics for better design and optimization of nucleic acid biosensors based on QDs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".