ReviewMacromolecular rapid communications2026
Liquid Crystal Elastomers Filaments for Adaptive Textiles and Soft Robotics: A Processing-Centric Review.
Review in Macromolecular rapid communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Liquid crystal elastomers (LCEs) combine mesogenic orientational order with rubber elasticity, enabling large, reversible actuation for applications in soft robotics, smart textiles, artificial muscles, and adaptive structures. However, translating LCEs from lab-scale films to continuous, industrially relevant filaments remains constrained by fragmented and non-standardized processing strategies. This review presents an integrated synthesis-to-filament framework linking molecular formulation, rheological conditioning, flow-induced alignment, and network fixation. Benchmark systems based on diacrylate mesogens (RM82/257) and triazine crosslinkers (TATATO) are highlighted, where staged Michael-addition produces shear-thinning oligomers (30-200 Pa s) suitable for extrusion, wet spinning, and melt drawing. Distinct viscosity regimes (low, intermediate, and high) are systematically compared, clarifying their effects on filament stability, alignment retention, and actuation performance. We further examine how mesogen architecture, crosslinker geometry, and photopolymerization kinetics define processable viscosity windows and defect-free filament formation. The coupled interplay between viscosity, extrusion pressure, and flow rate is emphasized to establish realistic and scalable processing windows. Processing strategies, including nozzle design, draw ratios, in-line UV curing, and thermal annealing, are outlined for encoding anisotropy and improving durability. The review concludes with a roadmap for standardized processing metrics, sustainable chemistries, and emerging machine learning-assisted formulation strategies to enable scalable LCE filament manufacturing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.