Freezing Directed Construction of Bio/Nano Interfaces: Reagentless Conjugation, Denser Spherical Nucleic Acids, and Better Nanoflares
Bibliographic record
Abstract
While nanoparticle solutions cannot freeze in general, they may remain stable in the presence of polymer stabilizers. We herein communicate that gold nanoparticles (AuNPs) are stable in the presence of thiolated DNA after a freeze-thaw cycle. The DNA is conjugated to AuNPs during freezing without additional reagents and the conjugation can be completed in a few minutes. More importantly, the DNA density is 20-30% higher than that prepared by the typical salt-aging method. By lowering temperature, DNA hybridization is also promoted, allowing the construction of better nanoflares with doubled probe density and signaling sensitivity. This freezing method works for AuNPs from 5 to 100 nm and all tested DNA sequences. The mechanism was studied by separating the effect of temperature, freezing and thawing, where the exclusion of salt and AuNPs by the growing ice crystals is deemed critical. In addition to developing a simple method, this study articulates unique physical processes during freezing with important fundamental surface science implications, and it could be extended to other systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".