Modeling the polydomain-monodomain transition of liquid crystal elastomers

Phase patterning of liquid crystal elastomers by laser-induced dynamic crosslinking

Liquid crystal elastomers hold promise in various fields due to their reversible transition of mechanical and optical properties across distinct phases. However, the lack of local phase patterning techniques and irreversible phase programming has hindered their broad implementation. Here we introduce laser-induced dynamic crosslinking, which leverages the precision and control offered by laser technology to achieve high-resolution multilevel patterning and transmittance modulation. Incorporation of allyl sulfide groups enables adaptive liquid crystal elastomers that can be reconfigured into desired phases or complex patterns. Laser-induced dynamic crosslinking is compatible with existing processing methods and allows the generation of thermo- and strain-responsive patterns that include isotropic, polydomain and monodomain phases within a single liquid crystal elastomer film. We show temporary information encryption at body temperature, expanding the functionality of liquid crystal elastomer devices in wearable applications.

A Dual-Responsive Liquid Crystal Elastomer for Multi-Level Encryption and Transient Information Display

Information security has gained increasing attention in the past decade, leading to the development of advanced materials for anti-counterfeiting, encryption and instantaneous information display. However, it remains challenging to achieve high information security with simple encryption procedures and low-energy stimuli. Herein, a series of strain/temperature-responsive liquid crystal elastomers (LCEs) are developed to achieve dual-modal, multi-level information encryption and real-time, rewritable transient information display. The as-prepared polydomain LCEs can change from an opaque state to a transparent state under strain or temperature stimuli, with the transition strains or temperatures highly dependent on the concentration of long-chain flexible spacers. Information encrypted by different LCE inks can be decrypted under specific strains or temperatures, leading to multi-level protection of information security. Furthermore, with the combination of the phase transition of polydomain LCEs and the photothermal effect of multi-walled carbon nanotubes (MWCNTs), we achieved a repeatable transient information display by using near-infrared (NIR) light as a pen for writing. This study provides new insight into the development of advanced encryption materials with versatility and high security for broad applications.

Multiphysics modelling of light-actuated liquid crystal elastomers

Liquid crystalline elastomers (LCEs) represent a promising class of responsive polymers whose physical properties are peculiar to both fluids and solids. Thanks to their microscale structure made of elongated rigid molecules (mesogens)—characterized by their capability to reversibly switch from an isotropic to an ordered state—LCEs exhibit a number of remarkable physical effects, such as self-deformation and mechanical actuation triggered by external stimuli. Efficient and physics-based modelling, aimed at designing and optimizing LCE-based devices (such as artificial muscles, deployable structures, soft actuators, etc.), is a fundamental tool to quantitatively describe their mechanical behaviour in real applications. In the present study, we illustrate the multi-physics modelling of light-driven deformation of LCEs, based on the photo-thermal energy conversion. The role played by the light diffusion and heat transfer within the medium is considered and their effect on the obtainable actuation is studied through numerical simulations based on the multi-physics theory developed.

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.

Numerical Methods in Studies of Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) are a type of material with specific features of polymers and of liquid crystals. They exhibit interesting behaviors, i.e., they are able to change their physical properties when met with external stimuli, including heat, light, electric, and magnetic fields. This behavior makes LCEs a suitable candidate for a variety of applications, including, but not limited to, artificial muscles, optical devices, microscopy and imaging systems, biosensor devices, and optimization of solar energy collectors. Due to the wide range of applicability, numerical models are needed not only to further our understanding of the underlining mechanics governing LCE behavior, but also to enable the predictive modeling of their behavior under different circumstances for different applications. Given that several mainstream methods are used for LCE modeling, viz. finite element method, Monte Carlo and molecular dynamics, and the growing interest and reliance on computer modeling for predicting the opto-mechanical behavior of complex structures in real world applications, there is a need to gain a better understanding regarding their strengths and weaknesses so that the best method can be utilized for the specific application at hand. Therefore, this investigation aims to not only to present a multitude of examples on numerical studies conducted on LCEs, but also attempts at offering a concise categorization of different methods based on the desired application to act as a guide for current and future research in this field.

The effect of alignment on the rate-dependent behavior of a main-chain liquid crystal elastomer

This study investigated the effect of alignment on the rate-dependent behavior of a main-chain liquid crystal elastomer (LCE). Polydomain nematic LCE networks were synthesized from a thiol-acrylate Michael addition reaction in the isotropic state. The polydomain networks were stretched to different strain levels to induce alignment then crosslinked in a second stage photopolymerization process. The LCE networks were subjected to dynamic mechanical tests to measure the temperature-dependent storage modulus and uniaxial tension load-unload tests to measure the rate-dependence of the Young's modulus, mechanical dissipation, and characteristics of the soft stress response. Three-dimensional (3D) digital image correlation (DIC) was used to characterize the effect of domain/mesogen relaxation on the strain fields. All LCE networks exhibited a highly rate-dependent stress response with significant inelastic strains after unloading. The Young's modulus of the loading curve and hysteresis of the load-unload curves showed a power-law dependence on the strain rate. The Young's modulus increased with alignment and larger anisotropy and a smaller power-law exponent was measured for the Young's modulus and hysteresis for the highly aligned monodomains. The polydomain and pre-stretched networks loaded perpendicular to the alignment direction exhibited a soft stress response that featured a rate-dependent peak stress, strain-softening, and strain-stiffening. The 3D-DIC strain fields for the polydomain network and programmed networks stretched in the perpendicular direction were highly heterogeneous, showing regions of alternating higher and lower strains. The strain variations increased initially with loading, peaked during the strain softening part of the stress response, then decreased during the strain stiffening part of the stress response. Greater variability was measured for lower strain rates. These observations suggest that local domain/mesogen relaxation led to the development of the heterogeneous strain patterns and strain softening in stress response. These findings improved understanding of the kinetics of mesogen relaxation and its contributions to the rate-dependent stress response and mechanical dissipation.

Machine learning-aided analysis for complex local structure of liquid crystal polymers

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

Development of Coarse-Grained Liquid-Crystal Polymer Model with Efficient Electrostatic Interaction: Toward Molecular Dynamics Simulations of Electroactive Materials

Liquid-crystal polymers (LCPs) are well known materials for functional sensor and actuators, because of their high-responsiveness to an electric field. Owing to their complex physical nature, however, the prediction of the functions of LCPs is a challenge. To attack this problem from a molecular point of view, a simulation study is a promising approach. In this work, for future applications of molecular dynamics simulations to problems involving an electric field, we develop an LCP model which consists of coarse-grained mesogenic molecules and smeared charges. For the smearing function of the electrostatic force, the Gauss error function is introduced. This smearing is optimized to attain a reasonable accuracy for phase transition phenomena of liquid crystal while numerical instabilities arising from the singularity of the Coulomb potential are circumvented. For swelling systems, our LCP model exhibits the characteristics of both liquid crystals and unentangled polymer chains; orientational order of the mesogenic units and Rouse-like relaxation dynamics. Our coarse-grained LCP model successfully incorporates electric charges and dipoles and is therefore applicable to problems concerning an electric field.

Dual relaxation and structural changes under uniaxial strain in main-chain smectic-C liquid crystal elastomer

Relaxation rate of the chevron angle, α becomes about ten times faster at strains exceeding 0.7 than at low strains.

Tricritical behavior of soft nematic elastomers

We propose a lattice statistical model to investigate the phase diagrams and the soft responses of nematic liquid-crystal elastomers. Using suitably scaled infinite-range interactions, we obtain exact self-consistent equations for the tensor components of the nematic order parameter in terms of temperature, the distortion and stress tensors, and the initial nematic order. These equations are amenable to simple numerical calculations, which are used to characterize the low-temperature soft regime. We find a peculiar phase diagram, in terms of temperature and the diagonal component of the distortion tensor along the stretching direction, with first- and second-order transitions to the soft phase, and the prediction of tricritical points. This behavior is not qualitatively changed if we use different values of the initial nematic order parameter.