Colloidal analogs of molecular chain stoppers
Bibliographic record
Abstract
A similarity between chemical reactions and self-assembly of nanoparticles offers a strategy that can enrich both the synthetic chemistry and the nanoscience fields. Synthetic methods should enable quantitative control of the structural characteristics of nanoparticle ensembles such as their aggregation number or directionality, whereas the capability to visualize and analyze emerging nanostructures using characterization tools can provide insight into intelligent molecular design and mechanisms of chemical reactions. We explored this twofold concept for an exemplary system including the polymerization of bifunctional nanoparticles in the presence of monofunctional colloidal chain stoppers. Using reaction-specific design rules, we synthesized chain stoppers with controlled reactivity and achieved quantitative fine-tuning of the self-assembled structures. Analysis of the nanostructures provided information about polymerization kinetics, side reactions, and the distribution of all of the species in the reaction system. A quantitative model was developed to account for the reactivity, kinetics, and side reactions of nanoparticles, all governed by the design of colloidal chain stoppers. This work provided the ability to test theoretical models developed for molecular polymerization.
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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) | 0.002 | 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".