Mechanically robust liquid crystal elastomer actuator using combined covalent and topological sliding-ring crosslinking
Bibliographic record
Abstract
Liquid crystal elastomers (LCEs) have attracted considerable interest due to their ability to undergo reversible actuation with large deformation, positioning them among the most promising materials for soft actuators and robotics. Traditional LCE actuators rely on using covalent or dynamic covalent chain crosslinking to fix the alignment of mesogens required for reversible deformation. In this study, we introduce cyclodextrin (CD)-based polyrotaxane (PR) topological crosslinkers in a covalently crosslinked LCE actuator to enhance its mechanical properties without compromising its actuation performance, which is beneficial for applications where the actuator is subjected to large and repeated deformations. We show that the incorporation of a small amount of PR crosslinkers, which allow for energy dissipation through CD ring sliding, can maintain the reversible actuation capabilities of the LCE actuator and, in the same time, improve significantly its fracture toughness, fatigue resistance and tear resistance. The demonstrated approach of combining a primary covalent crosslinking for actuation and a secondary topological crosslinking for energy dissipation, resulting in synergetic enhancement of mechanical properties and retention of actuation performance, opens new avenues for the development of LCE actuators for applications requiring both superior actuation and mechanical 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 | 0.000 |
| 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".