Stiffening Liquid Crystal Elastomers with Liquid Crystal Inclusions
Bibliographic record
Abstract
Liquid crystal elastomers (LCEs) are promising building blocks for soft robots, given their large, programmable, reversible, and stimuli-responsive shape change. Enhancing LCEs' stiffness and toughness has been a longstanding desire previously explored by reinforcing them with fillers, crystalline microdomains, and interpenetrating polymer networks. While promising, these methods adversely affect molecular order and thermal strain. Here, a significant enhancement of the stiffness of LCEs is reported by loading them with low molecular weight liquid crystals (LMWLCs) without sacrificing thermal strain and molecular order. While pristine LCEs rapidly transition to a soft elastic plateau when strained from poly- to monodomain, LC-loaded samples (LC-LCEs) first experience a pronounced linear elasticity, followed by a soft elastic plateau at higher stresses. Further thermomechanical and X-ray analysis confirm the emergence of an additional mesophase in polydomain LC-LCEs, which evolves to short-range smectic (cybotactic) during the poly- to monodomain transition. Monodomain LC-LCEs show between 6.5- and 9.0-fold stiffness enhancement with improved molecular order and thermal strain. Their work densities are more than double that of pristine LCEs, with active thermal stroke of up to 25% under loads of over 2000 times their weight. Such remarkable behaviors are attributed to the interplay between post-polymerization phase separation of LCs and their strain-enhanced smectic ordering. The results suggest that LMWLC inclusion can be a simple yet robust method to significantly improve the mechanical properties of LCEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".