Tailoring Diels–Alder Cross-Linked Liquid Crystal Elastomers for Spatially Programmable Monolithic Actuators
Bibliographic record
Abstract
Liquid crystal elastomers with thermo-reversible Diels-Alder cross-links (DALCEs) offer exceptional reprocessability and mild-temperature reprogrammability, enabling repeated fabrication of diverse actuators. However, optimizing their molecular design and refabrication protocols remains crucial to further unlocking their potential. This work systematically investigates DALCEs synthesized via aza-Michael addition reactions between RM82, furfurylamine, and various chain extenders (phenylethylamine, ethylamine, butylamine, hexylamine, octylamine, and 6-amino-1-hexanol). The effects of cross-linking density and chain extender selection on phase behavior, thermomechanical properties, and actuation performance have been thoroughly examined. The results show that a PEA-based formulation with moderate cross-linking density achieves the most balanced performance. Based on this optimized formulation, a novel (re)fabrication strategy is introduced by harnessing DALCEs' intrinsic reprocessability, reprogrammability, and self-healing properties. This strategy employs multilevel fiber programming before monolithic actuator formation, enabling spatially controlled liquid crystal alignment and facilitating iterative actuator refinement through reconstruction. Consequently, complex morphing behaviors in disk films and stress-modulating functions in tubular actuators were demonstrated. This work establishes a versatile, easily synthesized material platform for spatially programmable, dynamic monolithic actuators, paving the way for advanced applications in soft robotics and adaptive devices.
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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".