arXiv subjects

Haijie Ren

Publications and source records attributed to Haijie Ren.

3 recordsLinked to original sources

arXiv2026

Hydrodynamic Modeling of Odd Nematic Elasticity in Liquid Crystals

There is a recent interest in studying odd elasticity in soft solids. Current focus has been on simple solids. However, many soft solids are structured and can exhibit nematic elasticity or viscoelasticity. Here we generalize the concept of odd elasticity to nematic elasticity. By rewriting the governing equation for two-dimensional nematic liquid crystals (LCs) in terms of complex Ginzburg--Landau equation, we propose an odd nematic elastic term and its stress term in the hydrodynamic model of nematic LCs. The odd nematic elasticity can be physically interpreted as non-reciprocal interactions between neighboring directors. In odd nematics we find that domain walls become self-propelled and are accompanied by a bidirectional flow, and point defects can self-spin, develop a spiral pattern, and induce a vortical flow. Interactions of a pair of defects show rich dynamics that are distinct from those in active nematics. As such, we have developed an odd general elasticity, which can be further generalized to other viscoelastic materials, and proposed a novel way to manipulate topological defects in nematic LCs.

cond-mat.soft↗

arXiv2025

Quorum sensing of light-activated colloids in nematic liquid crystals

Motile living organisms routinely probe their surroundings to adapt in ever-evolving environments. Although synthetic microswimmers offer surrogates for self-propelled living entities, they often lack the complex feedback mechanisms that enable organisms to adapt. In this work, we present an experimental platform in which light-activated colloids dispersed in a nematic liquid crystal can (i) switch from directed to active Brownian motion depending on the nematic anchoring and (ii) mechanically adjust their motility in response to crowding, effectively enforcing quorum-sensing interactions. Both features are caused by a distinctive self-propulsion mechanism as unveiled through experiments, simulations, and theory. We characterize the dynamics of a single colloid and demonstrate that its motion is captured by an active Brownian particle model if the nematic anchoring is homeotropic, and by directed self-propulsion along the nematic director if the anchoring is planar. Next, we investigate the many-body dynamics, showing that it undergoes a clustering phase separation through effective quorum-sensing interactions. Our work suggests how to create adaptive materials with life-like capabilities using readily accessible properties of liquid crystals and colloids without explicitly engineering any of the needed mechano-chemical feedbacks.

cond-mat.soft↗

arXiv2024

Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials

Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of $\pm 1/2$ defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay--Berne particles. MED performs faster than conventional defect detection method and can achieve over 90\% accuracy on recognizing $\pm1/2$ defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED provides correct structural information about $\pm 1$ defects, i.e., the phase angle for $+1$ defects and the orientation angle for $-1$ defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.

cond-mat.soft↗