Assembly of DNA-Functionalized Nanoparticles in Alcoholic Solvents Reveals Opposite Thermodynamic and Kinetic Trends for DNA Hybridization
Bibliographic record
Abstract
DNA has been a key molecule in biotechnology and nanotechnology. To date, the majority of the experiments involving DNA have been performed in aqueous solutions, which may be related to the perception that DNA hybridization is slower and less stable in organic solvents. All studies on the effect of organic solvents have focused on thermodynamic properties such as DNA melting temperature and the B-to-A form transition for very long DNAs, but not on the hybridization kinetics of short synthetic DNAs. We employed DNA-functionalized gold nanoparticles (AuNPs) as a model system and found that if the alcohol content is less than approximately 30%, more alcohol leads to a faster DNA hybridization, although with a decreased melting temperature. The generality of this observation was independently verified with two molecular beacon systems (in the absence of AuNPs) using fluorophore and quencher-labeled DNAs. With 25% ethanol, the hybridization rates are three to four times faster than in the case with water. This discovery will extend the application of DNA bio- and nanotechnology to organic solvents with improved performance.
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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".