Hydrogel porosity controlling DNA-directed immobilization of gold nanoparticles revealed by DNA melting and scanning helium ion microscopy
Bibliographic record
Abstract
Immobilization of nanomaterials is important for many applications, including sensor development, biomaterials design and catalysis. DNA-directed immobilization has been widely used because of its high specificity and programmability. While most previous work has been carried out using inorganic surfaces such as gold, silica, and carbon, we recently found that hydrogels are also useful for immobilization. For non-porous inorganic surfaces, DNA-directed immobilization is governed mainly by probe density, while porosity might play a major role for hydrogels. Herein, we test the effect of gel porosity on DNA-directed immobilization of gold nanoparticles (AuNPs). Porosity was varied by changing the hydrogel percentage and crosslinker density. The number of immobilized AuNPs and its binding strength were characterized by DNA melting experiments. Using scanning helium ion microscopy, the AuNP density on hydrogel was studied. The number of AuNP binding sites decreased with decreasing gel porosity or increasing AuNP size, implying that the associated AuNPs were inside the gel pores. Polyvalent binding is a key feature for nanoparticle immobilization. For a non-porous surface, polyvalent binding occurs only at one small spot. We found that hydrogels take advantage of its porous nature to establish 3-dimensional polyvalent binding. Even with a very low surface DNA density, effective AuNP immobilization can still be achieved.
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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".