Effect of Concentration on the Dissolution of One-Dimensional Polymer Crystals: A TEM and NMR Study
Bibliographic record
Abstract
We report a study of the dissolution of core-crystalline polyferrocenyldimethylsilane- block -polyisoprene (PFS 53 - b -PI 637, where the subscripts are the degrees of polymerization of the two blocks) micelle fragments in decane for different concentrations (ranging from 0.01 to 6 mg mL –1 ) by a combination of transmission electron microscopy (TEM) and high-temperature 1 H NMR. We used self-seeding experiments at different temperatures as an efficient, although indirect, way to evaluate the dissolution of these micelles fragments. We annealed micelle fragment solutions at five different temperatures (50, 60, 65, 70, and 75 °C) for 30 min and cooled them to room temperature to regrow the micelles. The amount of micelle fragments that dissolved at the annealing temperature was then evaluated by comparing the length of the regrown micelles with that of the starting micelle fragments. We show that seed crystallites are less prone to dissolution as their concentration increases. In addition, by combining results of self-seeding experiments and 1 H NMR measurements at 75 °C, we evaluated the percentage of unimer released upon the partial dissolution of seed fragments at 75 °C and established that the mechanism of seed fragment dissolution is also concentration dependent: at low concentrations, they dissolve in a cooperative process, whereas at high concentrations, they dissolve partially from both ends.
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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 | 0.001 |
| 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".