Bibliographic record
Abstract
ABSTRACT Using small molecules in polymer matrices is common in applications such as (i) plasticizing polymers to modify the glass transition and mechanical properties and (ii) dispersion of photoactive or electroactive small molecules in polymer matrices in organic‐electronic devices Aggregation of these small molecules and phase separation leading to crystallization often cannot be morphologically controlled. If these are designed with self‐assembling codes such as hydrogen bonding or aromatic interactions, their phase separation behavior would be distinctly different. This review summarizes the studies on morphologies in such situations, such as (i) sub‐surface assembly in polymer matrices, (ii) controlled polymerization‐induced phase separation to create polymer blends, (iii) using the polymer to direct the assembly of small molecules in liquid crystalline devices, (iv) functionalizing a polymer with self‐assembling small molecules to cause organo‐gelation which the polymer itself would not by itself, and (v) using such systems as templates to create porous polymer structures. Organic–inorganic hybrids using polymers as templates for nanostructures and imprinted porous membranes is an emerging area. Since self‐assembly is one of the dominating area of research with respect to both small molecules, polymers as well as the combination of the two, this review summarizes the studies on the aforementioned topics. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018 , 56 , 451–478
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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 | 0.001 |
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".