Sharpening the Thermal Release of DNA from Nanoparticles: Towards a Sequential Release Strategy
Bibliographic record
Abstract
Unlike the sharp melting behavior of DNA-linked nanoparticle aggregates, the melting of DNA strands from individual gold nanoparticles is broad despite the high surface density of bound DNA. Here, it is demonstrated how sharpened melting can be achieved in colloidal nanoparticle systems using branched DNA-doubler structures hybridized with complementary DNA-doublers bound to the gold nanoparticle. Moreover, sharpened transitions are observed when DNA-doublers are hybridized with linear DNA-modified gold nanoparticles. This result suggests that the DNA density on nanoparticles is intrinsically great enough to form cooperative structures with the DNA-doublers. Finally, by introducing abasic destabilizing groups, the melting temperature of these DNA-doublers decreases without decreasing the sharpness. Consequently, by varying the temperature, two DNA-doublers with different stabilities dissociate sequentially from the gold nanoparticle surface, without overlapping and within a narrow temperature window. Owing to the excellent thermal selectivities exhibited by this system, the implementation of DNA-doublers in sequential photothermal therapies and with other nanomedicine delivery agents that rely on DNA dissociation as the mechanism of selective release is anticipated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".