Thermal Stability of DNA Functionalized Gold Nanoparticles
Bibliographic record
Abstract
Therapeutic uses of DNA functionalized gold nanoparticles (DNA-AuNPs) have shown great potential and exciting opportunities for disease diagnostics and treatment. Maintaining stable conjugation between DNA oligonucleotides and gold nanoparticles under thermally stressed conditions is one of the critical aspects for any of the practical applications. We systematically studied the thermal stability of DNA-AuNPs as affected by organosulfur anchor groups and packing densities. Using a fluorescence assay to determine the kinetics of releasing DNA molecules from DNA-AuNPs, we observed an opposite trend between the temperature-induced and chemical-induced release of DNA from DNA-AuNPs when comparing the DNA-AuNPs that were constructed with different anchor groups. Specifically, the bidentate Au-S bond formed with cyclic disulfide was thermally less stable than those formed with thiol or acyclic disulfide. However, the same bidentate Au-S bond was chemically more stable under the treatment of competing thiols (mercaptohexanol or dithiothreitol). DNA packing density on AuNPs influenced the thermal stability of DNA-AuNPs at 37 °C, but this effect was minimum as temperature increased to 85 °C. With the improved understanding from these results, we were able to design a strategy to enhance the stability of DNA-AuNPs by conjugating double-stranded DNA to AuNPs through multiple thiol anchors.
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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) | 0.000 | 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".