A Proposed Mechanism of the Influence of Gold Nanoparticles on DNA Hybridization
Bibliographic record
Abstract
A combination of gold nanoparticles (AuNPs) and nucleic acids has been used in biosensing applications. However, there is a poor fundamental understanding of how gold nanoparticle surfaces influence the DNA hybridization process. Here, we measured the rate constants of the hybridization and dehybridization of DNA on gold nanoparticle surfaces to enable the determination of activation parameters using transition state theory. We show that the target bases need to be detached from the gold nanoparticle surfaces before zipping. This causes a shift of the rate-limiting step of hybridization to the mismatch-sensitive zipping step. Furthermore, our results propose that the binding of gold nanoparticles to the single-stranded DNA segments (commonly known as bubbles) in the duplex DNA stabilizes the bubbles and accelerates the dehybridization process. We employ the proposed mechanism of DNA hybridization/dehybridization to explain the ability of 5 nm diameter gold nanoparticles to help discriminate between single base-pair mismatched DNA molecules when performed in a NanoBioArray chip. The mechanistic insight into the DNA-gold nanoparticle hybridization/dehybridization process should lead to the development of new biosensors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".