The Influence of Gap Length on Cooperativity and Rate of Association in DNA-Modified Gold Nanoparticle Aggregates
Bibliographic record
Abstract
Polyvalent gold nanoparticle–DNA conjugates hybridize with complementary linker DNA strands to form aggregates that exhibit sharp dissociation curves indicative of cooperative behavior. Introducing single-stranded gaps consisting of thymidines (T 1 –T 20 ) into the linker strand resulted in a decrease in the number of duplexes that dissociate cooperatively. Upon adding one base insertion (T 1 ) the cooperative number drops from 6.3(2) to 2.8(2) duplexes. The cooperative number then increases slightly for the T 3 gap and thereafter decreases for T 8 and T 10, with a slight increase again for the T 20 gap. As the presence of a shared condensed cation cloud has been implicated in neighboring duplex cooperativity, we measured the salt-dependent behavior of T n gap-linked unmodified duplexes and the number of ions released per duplex dissociation. Interestingly, the number of cations released for the duplexes with a longer gap sequence is significantly larger than the number released for a T 1 gap-linked duplex or a nicked duplex (T 0 ). Overall there is a correlation between the change in condensed cation density and the dissociation entropy for the unmodified T n gap-linked duplexes, and the cooperative unit for the T n gap-linked GNP–DNA aggregates. Using dynamic light scattering and changes in optical absorbance, we also found that aggregation of GNP–DNA is more rapid when hybridization occurs at a nicked versus gap site, which was previously observed but attributed to slower hybridization as a result of the longer linker strand. By comparing the aggregation rate of a prehybridized GNP–DNA:T 10 -linker complex with a completely complementary GNP–DNA and a GNP–DNA that led to a T 10 gap, we were able to establish that the presence of the gap, not DNA length or accessibility, caused the decrease in aggregation rate. Our results support that flexibility in aggregates decreases the rate of aggregation as well as the extent of cooperativity, which has important implications in genomic DNA detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".