Inhibiting the Self-Sorting Behavior in the Blends of a Homologous Set of Polyurethane Model Compounds
Bibliographic record
Abstract
Self-sorting is the phenomenon in which there is high fidelity recognition and preference only for self and avoidance of nonself (narcissistic self-sorting). It has been observed in a number of biological systems and chiral synthetic molecules. We found that blends of biscarbamates, which are model compounds for polyurethanes, self-sort during crystallization [ J. Phys. Chem. B 2008 , 112 , 4223 - 4232 ], although these are not chiral molecules. Even if the two components in the blend differ only by a couple of CH2 groups in the side chain length, no intercomponent hydrogen bond forms, and the molecules self-sort. They do not show any cocrystallization despite being part of a homologous series. We believe that it is the first reported example such behavior among synthetic nonchiral molecules. This is similar to the behavior of blends of hydrogen-bonding polymers including polyurethanes. We show that the difference in the growth rates of the individual species is responsible for the self-sorting behavior in these nonchiral synthetic compounds. While self-sorting might be advantageous for separation of blends, it poses a challenge for modifying properties such as the melting temperatures, spherulite size, etc., for various applications. We will discuss methods that were attempted to bridge the self and nonself that would lead to a more homogeneous system. We evaluated the miscibility using differential scanning calorimetry (DSC), since the occurrence of a single or multiple endotherms would indicate molecular level miscibility. This is similar to the behavior of glass transition temperatures in the case of polymer blends. Optical microscopy (OM) and X-ray diffraction (XRD) were also used. It is concluded that irrespective of the protocol followed for preparing the mixtures, mutual plasticization occurred in most cases (i.e., mixing of domains of the two species) and not molecular mixing.
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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 | 0.001 | 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 | 0.001 | 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".