Cation-Size-Dependent DNA Adsorption Kinetics and Packing Density on Gold Nanoparticles: An Opposite Trend
Bibliographic record
Abstract
The property of DNA is strongly influenced by counterions. Packing a dense layer of DNA onto a gold nanoparticle (AuNP) generates an interesting colloidal system with many novel physical properties such as a sharp melting transition, protection of DNA against nucleases, and enhanced complementary DNA binding affinity. In this work, the effect of monovalent cation size is studied. First, for free AuNPs without DNA, larger group 1A cations are more efficient in inducing their aggregation. The same trend is observed with group 2A metals using AuNPs capped by various self-assembled monolayers. After establishing the salt range to maintain AuNP stability, the DNA adsorption kinetics is also found to be faster with the larger Cs(+) compared to the smaller Li(+). This is attributed to the easier dehydration of Cs(+), and dehydrated Cs(+) might condense on the AuNP surface to reduce the electrostatic repulsion effectively. However, after a long incubation time with a high salt concentration, Li(+) allows ∼30% more DNA packing compared to Cs(+). Therefore, Li(+) is more effective in reducing the charge repulsion among DNA, and Cs(+) is more effective in screening the AuNP surface charge. This work suggests that physicochemical information at the bio/nanointerface can be obtained by using counterions as probes.
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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) | 0.002 | 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".