Modulating Nanoparticle Superlattice Structure Using Proteins with Tunable Bond Distributions
Bibliographic record
Abstract
Abstract Herein, we investigate the use of proteins with tunable DNA modification distributions to modulate nanoparticle superlattice structure. Using beta-galactosidase (βgal) as a model system, we have employed the orthogonal chemical reactivities of surface amines and thiols to synthesize protein–DNA conjugates with 36 evenly distributed or 8 specifically positioned oligonucleotides. When these are assembled into crystalline superlattices with gold nanoparticles, we find that the distribution of DNA modifications modulates the favored structure: βgal with uniformly distributed DNA bonding elements results in body-centered cubic crystals, whereas DNA functionalization of cysteines results in AB2 packing. We probe the role of protein oligonucleotide number and conjugate size on this observation, which revealed the importance of oligonucleotide distribution in this observed assembly behavior. These results indicate that proteins with defined DNA modification patterns are powerful tools for controlling nanoparticle superlattices architecture, and establish the importance of oligonucleotide distribution in the assembly behavior of protein–DNA conjugates.
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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".