Non-thiolated spherical nucleic acids for biosensors and assembly of nanomaterials
Bibliographic record
Abstract
Spherical nucleic acids (SNAs) refer to a nanoparticle core decorated with a high density of single-stranded DNA or RNA. SNAs have garnered significant attention for their unique physicochemical properties and advantages in biomedical, nanotechnology and biosensing applications. The preparation of traditional SNAs typically relies on the strong bonding between thiolated DNA and gold nanoparticles (AuNPs) to ensure a high-density and stable DNA attachment. Interestingly, non-thiolated DNA also strongly interacts with gold surfaces through the coordination of its nucleobases, enabling the preparation of cost-effective non-thiolated SNAs. In this review, we introduce the adsorption properties of DNA on AuNPs, followed by a review of the current methods for the synthesis of non-thiolated SNAs and a discussion of their stability based on existing data. The reviewed methods include salt-aging, low-pH, freezing, microwaving, and thermal drying. Most methods rely on a poly-adenine block to anchor onto the surface of AuNPs. Furthermore, two types of non-thiolated SNA products are discussed, which are characterized by their DNA density as a function of the length of the poly-adenine block. Finally, we briefly outline the current applications of SNAs, including biosensing and DNA-directed assembly, and discuss potential future developments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".