Mechanochemically Responsive Viscoelastic Elastomers
Bibliographic record
Abstract
Mechanochemically responsive (MCR) polymers have been designed to possess unconventional properties such as changing colors, self-healing, and releasing catalysts under deformation. These properties of MCR polymers stem from a class of molecules, referred to as mechanophores, whose chemical reactions can be controlled by mechanical forces. Although extensive studies have been devoted to the syntheses of MCR polymers by incorporating various mechanophores into polymer networks, the intricate interactions between mechanical forces and chemical reactions in MCR polymers across multiple length and time scales are still not well understood. In this paper, we focus on mechanochemical responses in viscoelastic elastomers and develop a theoretical model to characterize the coupling between viscoelasticity and chemical reactions of MCR elastomers. We show that the kinetics of viscoelasticity and mechanophore reactions introduce different time scales into the MCR elastomers. The model can consistently represent experimental data on both mechanical properties and chemical reactions of MCR viscoelastic elastomers. In particular, we explain recent experimental observations on the increasing chemical activation during stress relaxation of MCR elastomers, which cannot be explained with existing models. The proposed model provides a theoretical foundation for the design of future MCR polymers with desirable properties.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".