Probing the Heteroepitaxial Seeded Growth and Self‐Sorting Processes of Segmented Co‐Micelles with Chemically Distinct Crystalline Cores
Bibliographic record
Abstract
The ability to produce uniform micellar nanoparticles with controlled dimension and spatially controlled functionality is a key challenge in nanoscience. Living crystallization-driven self-assembly (CDSA) of block copolymers (BCP) has emerged as an effective approach to generate uniform size-tunable core-shell micellar nanoparticles; however, most core-shell micelles generated via CDSA consist of a continuous crystalline core from BCPs with the same core-forming block. Herein, we perform insightful studies of heteroepitaxial CDSA process from chemical distinct core-forming poly(ferrocenyldimethylgermane) (PFDMG) and poly(ferrocenyldimethylsilane) (PFDMS) based BCPs to produce segmented block comicelles. The heteroepitaxial growth process produced micelles with kinetically trapped crystalline cores that are thermodynamically less stable than the materials formed via spontaneous nucleation. This was rationalized by determining the previously unknown core lattice of PFDMG micelles, self-assembly experiments, and theoretical lattice energy calculations, providing an insight into the energetic penalty associated with heteroepitaxial growth. These methods for determining the theoretical core lattice energies in these BCP systems could provide a way to screen BCP candidates that can undergo heteroepitaxial growth. Furthermore, by using our newfound understanding of these micelle systems, we achieved the formation of micelles with crystalline cores that undergo self-sorting, driven by self-seeding from fragmented triblock comicellar structures.
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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) | 0.000 | 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".