Instantaneous and Quantitative Functionalization of Gold Nanoparticles with Thiolated DNA Using a pH-Assisted and Surfactant-Free Route
Bibliographic record
Abstract
The attachment of thiolated DNA to gold nanoparticles (AuNPs) has enabled many landmark works in nanobiotechnology. This conjugate chemistry is typically performed using a salt-aging protocol where, in the presence of an excess amount of DNA, NaCl is gradually added to increase DNA loading over 1-2 days. To functionalize large AuNPs, surfactants need to be used, which may generate difficulties for downstream biological applications. We report herein a novel method using a pH 3.0 citrate buffer to complete the attachment process in a few minutes. More importantly, it allows for quantitative DNA adsorption, eliminating the need to quantify the number of adsorbed DNA and allowing the adsorption of multiple DNAs with different sequences at predetermined ratios. The method has been tested for various DNAs over a wide range of AuNP sizes. Our work suggests a synergistic effect between pH and salt in DNA attachment and reveals the fundamental kinetics of AuNP aggregation versus DNA adsorption, providing a novel means to modulate the interactions between DNA and AuNPs.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".