Rapid Thermal Drying Synthesis of Nonthiolated Spherical Nucleic Acids with Stability Rivaling Thiolated DNA
Bibliographic record
Abstract
Abstract Attaching DNA oligonucleotides to gold nanoparticles (AuNPs) to prepare spherical nucleic acids (SNAs) has offered tremendous insights into surface chemistry with resulting bioconjugates serving as critical reagents in biosensors and nanotechnology. While thiolated DNA is generally required to achieve stable conjugates, we herein communicate that using a thermal drying method, a high DNA density and excellent SNA stability was achieved using nonthiolated DNA, rivaling the performance of thiolated DNA such as surviving 1 M NaCl, 2 month stability in 0.3 M NaCl and working in 50 % serum. A poly‐adenine block with as few as two consecutive terminal adenine bases is sufficient for anchoring on AuNPs. By side‐by‐side comparison with the salt‐aging method, the conjugation mechanism was attributed to competitive adenine adsorption at high temperature along with an extremely high DNA concentration upon drying. Bioanalytical applications of nonthiolated SNAs were validated in both solution and paper‐based sensor platforms, facilitating cost‐effective applications for SNAs.
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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) | 0.000 | 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) | 0.001 | 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".