Cooperative Metal Ion-Mediated Adsorption of Spherical Nucleic Acids with a Large Hysteresis
Bibliographic record
Abstract
Spherical nucleic acids (SNA) refer to nanoparticles attached with a high density of oligonuleotides. Linear and spherical nucleic acids have many differences such as hybridization affinity, melting transition, and cellular uptake. In this work, these two types of DNA of the same sequence were compared for adsorption on polydopamine (PDA) nanoparticles and graphene oxide (GO). We focused on the effect of metal ions including Na +, Ca 2+, and Zn 2+ since metal ions are indispensible for DNA adsorption on PDA and GO. Gold nanoparticles (AuNPs) of various sizes were used to prepare the SNAs. For both PDA and GO, a normal binding curve of one metal ion was obtained for adsorbing the linear DNA, while the spherical DNAs larger than 5 nm showed a sigmoidal binding curve requiring multiple metal ions. Urea and EDTA were used to probe DNA adsorption affinity, where the spherical DNA showed stronger adsorption in general. In the presence of 300 mM Na +, 4 M urea or 4 mM EDTA failed to desorb the 13 nm spherical DNA. The spherical DNA showed a very large hysteresis of metal-dependent adsorption. This study demonstrates another unique property of SNA compared to linear DNA, revealing interesting orientation and packing of DNA on AuNPs, which has deepened our understanding of DNA interface chemistry.
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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 | 0.001 |
| 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 | 0.001 | 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".