Molecular architecture dependence of mesogen rotation during uniaxial elongation of liquid crystal elastomers

Regression analysis for predicting the elasticity of liquid crystal elastomers

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

Direct laser writing of liquid crystal elastomers oriented by a horizontal electric field

Background: The ability to fabricate components capable of performing actuation in a reliable and controlled manner is one of the main research topics in the field of microelectromechanical systems (MEMS). However, the development of these technologies can be limited in many cases by 2D lithographic techniques employed in the fabrication process. Direct Laser Writing (DLW), a 3D microprinting technique based on two-photon polymerization, can offer novel solutions to prepare, both rapidly and reliably, 3D nano- and microstructures of arbitrary complexity. In addition, the use of functional materials in the printing process can result in the fabrication of smart and responsive devices. Methods: In this study, we present a novel methodology for the printing of 3D actuating microelements comprising Liquid Crystal Elastomers (LCEs) obtained by DLW. The alignment of the mesogens was performed using a static electric field (1.7 V/µm) generated by indium-tin oxide (ITO) electrodes patterned directly on the printing substrates. Results: When exposed to a temperature higher than 50°C, the printed microstructures actuated rapidly and reversibly of about 8% in the direction perpendicular to the director. Conclusions: A novel methodology was developed that allows the printing of directional actuators comprising LCEs via DLW. To impart the necessary alignment of the mesogens, a static electric field was applied before the printing process by making use of flat ITO electrodes present on the printing substrates. The resulting microelements showed a reversible change in shape when heated higher than 50 °C.