Thermal drying synthesized density-tunable and high stability nonthiolated spherical nucleic acids for split aptamer and lateral flow biosensors
Bibliographic record
Abstract
Spherical nucleic acids (SNAs) have attracted considerable interest in designing biosensors. Our group recently developed a thermal drying method to fabricate SNAs with an ultrahigh density of cost-effective nonthiolated DNA containing a polyadenine block. In this study, functional properties of such SNAs in two types of biosensors were investigated using aptamer and hybridization-based model detection systems. For aptamer-based target recognition, we observed that a high DNA density on SNAs hindered target binding. To address this, short dilution strands were introduced to decrease probe density. Using oxytetracycline as a target, a plasmonic colorimetric sensor with a detection limit of 0.5 μM was achieved. For hybridization-based recognition, we identified key parameters for avoiding false positives in lateral flow assays (LFA) and demonstrated that high DNA density did not compromise sensitivity. Using genetically modified soybean MON87705 as a target, we developed a portable LFA platform by integrating loop-mediated isothermal amplification with CRISPR/Cas12a, achieving a detection limit of 0.08 wt%. Overall, this study provides specific guidance for the practical application of nonthiolated SNA probes in biosensors.
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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".