Dissociation and Degradation of Thiol-Modified DNA on Gold Nanoparticles in Aqueous and Organic Solvents
Bibliographic record
Abstract
Gold nanoparticles functionalized with thiol-modified DNA have been widely used in making various nanostructures, colorimetric biosensors, and drug delivery vehicles. Over the past 15 years, significant progress has been made to improve the stability of such functionalized nanoparticles. The stability of the gold-thiol bond in this system, however, has not been studied in a systematic manner. Most information on the gold-thiol bond was obtained from the study of self-assembled monolayers (SAMs). In this study, we employed two fluorophore-labeled and thiol-modified DNAs. The long-term stability of the thiol-gold bond as a function of time, salt, temperature, pH, and organic solvent has been studied. We found that the bond spontaneously dissociated under all tested conditions. The dissociation was favored at high salt, high pH, and high temperature, and little DNA degradation was observed in our system. Most organic solvents showed a moderate protection effect on the gold-thiol bond. The stability of the gold-thiol bond in the DNA system was also compared with that in SAMs. While there are many similarities, we also observed opposite trends for the salt and ethanol effect. This study suggests that the purified DNA-functionalized gold nanoparticles should be freshly prepared and used in a day or two. Long-term storage should be carried out at relatively low temperature in low salt and slightly acidic buffers.
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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 | 0.000 |
| 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) | 0.000 | 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".