Probing the Growth Kinetics for the Formation of Uniform 1D Block Copolymer Nanoparticles by Living Crystallization-Driven Self-Assembly
Bibliographic record
Abstract
Living crystallization-driven self-assembly (CDSA) is a seeded growth method for crystallizable block copolymers (BCPs) and related amphiphiles in solution and has recently emerged as a highly promising and versatile route to uniform core-shell nanoparticles (micelles) with control of dimensions and architecture. However, the factors that influence the rate of nanoparticle growth have not been systematically studied. Using transmission electron microscopy, small- and wide-angle X-ray scattering, and super-resolution fluorescence microscopy techniques, we have investigated the kinetics of the seeded growth of poly(ferrocenyldimethylsilane)- b-(polydimethylsiloxane) (PFS- b-PDMS), as a model living CDSA system for those employing, for example, crystallizable emissive and biocompatible polymers. By altering various self-assembly parameters including concentration, temperature, solvent, and BCP composition our results have established that the time taken to prepare fiber-like micelles via the living CDSA method can be reduced by decreasing temperature, by employing solvents that are poorer for the crystallizable PFS core-forming block, and by increasing the length of the PFS core-forming block. These results are of general importance for the future optimization of a wide variety of living CDSA systems. Our studies also demonstrate that the growth kinetics for living CDSA do not exhibit the first-order dependence of growth rate on unimer concentration anticipated by analogy with living covalent polymerizations of molecular monomers. This difference may be caused by the combined influence of chain conformational effects of the BCP on addition to the seed termini and chain length dispersity.
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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".