DNA-Functionalized Monolithic Hydrogels and Gold Nanoparticles for Colorimetric DNA Detection
Bibliographic record
Abstract
Highly sensitive and selective DNA detection plays a central role in many fields of research, and various assay platforms have been developed. Compared to homogeneous DNA detection, surface-immobilized probes allow washing steps and signal amplification to give higher sensitivity. Previously research was focused on developing glass or gold-based surfaces for DNA immobilization; we herein report hydrogel-immobilized DNA. Specifically, acrydite-modified DNA was covalently functionalized to the polyacrylamide hydrogel during gel formation. There are several advantages of these DNA-functionalized monolithic hydrogels. First, they can be easily handled in a way similar to that in homogeneous assays. Second, they have a low optical background where, in combination with DNA-functionalized gold nanoparticles, even ∼0.1 nM target DNA can be visually detected. By using the attached gold nanoparticles to catalyze the reduction of Ag+, as low as 1 pM target DNA can be detected. The gels can be regenerated by a simple thermal treatment, and the regenerated gels perform similarly to freshly prepared ones. The amount of gold nanoparticles adsorbed through DNA hybridization decreases with increasing gel percentage. Other parameters including the DNA concentration, DNA sequence, ionic strength of the solution, and temperature have also been systematically characterized in this study.
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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) | 0.001 | 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 | 0.001 | 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".