Designing disordered materials using DNA-coated colloids of bacteriophage fd and gold
Bibliographic record
Abstract
DNA has emerged as an exciting binding agent for programmable colloidal self-assembly. Its popularity derives from its unique properties: it provides highly specific short-ranged interactions and at the same time it acts as a steric stabilizer against non-specific van der Waals and Coulomb interactions. Because complementary DNA strands are linked only via hydrogen bonds, DNA-mediated binding is thermally reversible: it provides an effective attraction that can be switched off by raising the temperature only by a few degrees. In this article we introduce a new binary system made of DNA-functionalized filamentous fd viruses of ∼880 nm length with an aspect ratio of ∼100, and 50 nm gold nanoparticles (gold NPs) coated with the complementary DNA strands. When quenching mixtures below the melt temperature Tm, at which the attraction is switched on, we observe aggregation. Conversely, above Tm the system melts into a homogenous particulate 'gas'. We present the aggregation behavior of three different gold NP to virus ratios and compare them to a gel made solely of gold NPs. In particular, we have investigated the aggregate structures as a function of cooling rate and determine how they evolve as function of time for given quench depths, employing fluorescence microscopy. Structural information was extracted in the form of an effective structure factor and chord length distributions. Rapid cooling rates lead to open aggregates, while slower controlled cooling rates closer to equilibrium DNA hybridization lead to more fine-stranded gels. Despite the different structures we find that for both cooling rates the quench into the two-phase region leads to initial spinodal decomposition, which becomes arrested. Surprisingly, although the fine-stranded gel is disordered, the overall structure and the corresponding length scale distributions in the system are remarkably reproducible. Such highly porous systems can be developed into new functional materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".