Surface Science of DNA Adsorption onto Citrate-Capped Gold Nanoparticles
Bibliographic record
Abstract
Single-stranded DNA can be adsorbed by citrate capped gold nanoparticles (AuNPs), resulting in increased AuNP stability, which forms the basis of a number of biochemical and analytical applications, but the fundamental interaction of this adsorption reaction remains unclear. In this study, we measured DNA adsorption kinetics, capacity, and isotherms, demonstrating that the adsorption process is governed by electrostatic forces. The charge repulsion among DNA strands and between DNA and AuNPs can be reduced by adding salt, reducing pH or by using noncharged peptide nucleic acid (PNA). Langmuir adsorption isotherms are obtained, indicating the presence of both adsorption and desorption of DNA from AuNPs. While increasing salt concentration facilitates DNA adsorption, the desorption rate is also enhanced in higher salt due to DNA compaction. DNA adsorption capacity is determined by DNA oligomer length, DNA concentration, and salt. Previous studies indicated faster adsorption of short DNA oligomers by AuNPs, we find that once adsorbed, longer DNAs are much more effective in protecting AuNPs from aggregation. DNA adsorption is also facilitated by using low pH buffers and high alcohol concentrations. A model based on electrostatic repulsion on AuNPs is proposed to rationalize the DNA adsorption/desorption behavior.
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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".