Parallel Polyadenine Duplex Formation at Low pH Facilitates DNA Conjugation onto Gold Nanoparticles
Bibliographic record
Abstract
DNA-functionalized gold nanoparticles (AuNPs) have been extensively used in sensing, drug delivery, and materials science. A key step is to attach DNA to AuNPs, forming a stable and functional conjugate. Although the traditional salt-aging method takes a full day or longer, a recent low-pH method allows DNA conjugation in a few minutes. The effect of low pH was attributed to the protonation of adenine (A) and cytosine (C), resulting in an overall lower negative charge density on DNA. In this work, the effect of DNA conformation at low pH is studied. Using circular dichroism (CD) spectroscopy, the parallel poly-A duplex (A-motif) is detected when a poly-A segment is linked to a random DNA, a design typically used for DNA conjugation. A DNA staining dye, thiazole orange, is identified for detecting such A-motifs. The A-motif structure is ideal for DNA conjugation because it exposes the thiol group to directly react with the gold surface while minimizing nonspecific DNA base adsorption. For nonthiolated DNA, the optimal procedure is to incubate DNA and AuNPs followed by lowering the pH. The i-motif formed by poly-C DNA at low pH is less favorable to the conjugation reaction because of its unique way of folding. The stability of poly-A and poly-G DNA at low pH is examined. An excellent stability of poly-A DNA is confirmed, but poly-G has lower stability. This study provides new fundamental insights into a practically useful technique of conjugating DNA to 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 | 0.000 | 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".