Tuning Toehold Length and Temperature to Achieve Rapid, Colorimetric Detection of DNA from the Disassembly of DNA–Gold Nanoparticle Aggregates
Bibliographic record
Abstract
Gold nanoparticles have been widely utilized to achieve colorimetric detection for various diagnostic applications. One of the most frequently used methods for DNA detection involves the aggregation of DNA-modified gold nanoparticles driven by target DNA hybridization. This process, however, is intrinsically slow, limiting its use in rapid diagnostics. Here we take advantage of the reverse process: the disassembly of preformed aggregates triggered by the addition of target DNA via a strand displacement mechanism. A systematic study of the dependence of the disassembly rate on temperature, with and without toeholds, has delivered a system that produces an extremely rapid colorimetric response. Furthermore, using an optimal toehold length of 5 nucleotides, target triggered disassembly is rapid over a wide range of ambient temperatures. Using this overhang system, simple visualization of low picomole amounts of target DNA is possible within 10 min at room temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".