Altering DNA-Programmable Colloidal Crystallization Paths by Modulating Particle Repulsion
Bibliographic record
Abstract
Colloidal crystal engineering with DNA can be used to realize precise control over nanoparticle (NP) arrangement. Here, we investigate a case of DNA-based assembly where the properties of DNA as a polyelectrolyte brush are employed to alter a hybridization-driven NP crystallization pathway. Using the coassembly of DNA-conjugated proteins and spherical gold nanoparticles (AuNPs) as a model system, we explore how steric repulsion between noncomplementary, neighboring NPs due to overlapping DNA shells can influence their ligand-directed behavior. Specifically, our experimental data coupled with coarse-grained molecular dynamics (MD) simulations reveal that, by changing factors related to NP repulsion, two structurally distinct outcomes can be achieved. When steric repulsion between DNA-AuNPs is significantly greater than that between DNA-proteins, a lower packing density crystal lattice is favored over the structure that is predicted by design rules based on DNA hybridization considerations alone. This is enabled by the large difference in DNA density on AuNPs versus proteins and can be tuned by modulating the flexibility, and thus conformational entropy, of the DNA on the constituent particles. At intermediate ligand flexibility, the crystallization pathways are energetically similar, and the structural outcome can be adjusted using the density of DNA duplexes on DNA-AuNPs and by screening the Coulomb potential between them. Such lattices are shown to undergo dynamic reorganization upon changing the salt concentration. These data help elucidate the structural considerations necessary for understanding repulsive forces in DNA-mediated assembly and lay the groundwork for using them to increase architectural diversity in engineering colloidal crystals.
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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".