Supramolecular elastomers: Switchable mechanical properties and tuning photohealing with changes in supramolecular interactions
Bibliographic record
Abstract
ABSTRACT To study light‐triggered self‐healing in supramolecular materials, we synthesized supramolecular thermoplastic elastomers with mechanical properties that were reversibly modulated with temperature. By changing the supramolecular architecture, we created polymers with different temperature responses. Detailed characterization of the hydrogen‐bonding material revealed dramatically different temperature and mechanical stress response due to two different stable states with changes in the hydrogen bonding interactions. A semi‐crystalline state showed no response to oscillatory shear deformations while the melt state behaved as a typical energy dissipative material with a clear crossover between storage and loss moduli. Comparison studies on heat generation after light excitation revealed no differences in photo‐thermal conversion when an Fe(II)‐phenanthroline chromophore was either physically blended into the H‐bonding polymer or covalently attached to the supramolecular network. These materials showed healing of scratches with light‐irradiation, as long as the overlap of material absorbance and laser excitation was sufficient. Differences in the efficiency and rate of photohealing were observed, depending on the type of supramolecular interaction, and these were attributed to the differences in the thermal response of the materials' moduli. Such results provide insight into how materials can be designed with chromophores and supramolecular bonding interactions to tune the light‐healing efficiency of the materials. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2018, 56, 1003–1011
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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".