Liquid Crystalline Systems from Nature and Interaction of Living Organisms with Liquid Crystals

Ionic Liquid Crystal-Polymer Composite Electromechanical Actuators: Design of Two-Dimensional Molecular Assemblies for Efficient Ion Transport and Effect of Electrodes on Actuator Performance

We present the development of free-standing ionic liquid crystal-polymer composite electrolyte films aimed at achieving high-frequency response electromechanical actuators. Our approach entails designing novel layered ionic liquid-crystalline (LC) assemblies by complexing a mesomorphic dimethylphosphate with either a lithium salt or a room-temperature ionic liquid through the formation of ion-dipole interactions or hydrogen bonds. These electrolytes, exhibiting room-temperature ionic conductivities on the order of 10-4 S cm-1 and wide LC temperature ranges up to 77 °C, were successfully integrated into porous polymer networks. We systematically investigated the impact of ions and electrodes on the performance of ionic electroactive actuators. Specifically, the Li+-based liquid crystal-polymer composite actuator with PEDOT:PSS electrodes demonstrated the highest bending deformation, achieving a strain of 0.68% and exhibiting a broad frequency response up to 110 Hz, with a peak-to-peak displacement of 3 μm. In contrast, the ionic-liquid-based liquid crystal-polymer composite actuator with active carbon electrodes showcased a bending response at a maximum frequency of 50 Hz and a force generation of 0.48 mN, without exhibiting the back relaxation phenomenon. These findings offer valuable insights for advancing high-performance electromechanical systems with applications ranging from soft robotics to haptic interfaces.

Bioinspired Confined Assembly of Cellulosic Cholesteric Liquid Crystal Bubbles

Construction of biomimetic models for structural color evolution not only gives new photonic phenomena but also provide cues for biological morphogenesis. Here, a novel confined self-assembly method is proposed for the generation of hydroxypropyl cellulose (HPC)-based cholesteric liquid crystals (CLCs) microbubbles. The assembly process relies on the combination of droplet microfluidics, solvent extraction, and a volume confined environment. The as-prepared HPC structural color microbubbles have a transparent shell, an orderly arranged cholesteric liquid crystal (CLC) middle layer, and an innermost bubble core. The size of the microbubble, shell thickness, and the color of the CLC layer can be adjusted by altering the microfluidic parameters. Intriguingly, benefited from the compartmentalization effect provided by droplet microfluidics, microbubbles with multiple cores of different color combinations are generated under precise control. The self-assembled CLCs microbubbles have bright structural color, suspending ability, and good temperature-sensitive characteristics, making them ideal underwater sensors. The present confined assembly approach will shed light on creating novel photonic structures and the HPC microbubble will find widespread applications in multifunctional sensing, optical display, and other related fields are believed.

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.

Understanding the Role of Trapezoids in Honeycomb Self-Assembly-Pathways between a Columnar Liquid Quasicrystal and its Liquid-Crystalline Approximants

Quasiperiodic patterns and crystals-having long range order without translational symmetry-have fascinated researchers since their discovery. In this study, we report on new p-terphenyl-based T-shaped facial polyphiles with two alkyl end chains and a glycerol-based hydrogen-bonded side group that self-assemble into an aperiodic columnar liquid quasicrystal with 12-fold symmetry and its periodic liquid-crystalline approximants with complex superstructures. All represent honeycombs formed by the self-assembly of the p-terphenyls, dividing space into prismatic cells with polygonal cross-sections. In the perspective of tiling patterns, the presence of unique trapezoidal tiles, consisting of three rigid sides formed by the p-terphenyls and one shorter, incommensurate, and adjustable side by the alkyl end chains, plays a crucial role for these phases. A delicate temperature-dependent balance between conformational, entropic and space-filling effects determines the role of the alkyl chains, either as network nodes or trapezoid walls, thus resulting in the order-disorder transitions associated with emergence of quasiperiodicity. In-depth analysis suggests a change from a quasiperiodic tiling involving trapezoids to a modified one with a contribution of trapezoid pair fusion. This work paves the way for understanding quasiperiodicity emergence and develops fundamental concepts for its generation by chemical design of non-spherical molecules, aggregates, and frameworks based on dynamic reticular chemistry.