Collision-induced torque mediates the transition of chiral dynamic patterns formed by active particles

Literature Information

Publication Date 2022-10-26
DOI 10.1039/D2CP03879J
Impact Factor 3.676
Authors

Ryo Akiyama, Daisuke Inoue, Arif Md. Rashedul Kabir, Akira Kakugo


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Abstract

Controlling the patterns formed by self-propelled particles through dynamic self-organization is a challenging task. Although varieties of patterns associated with chiral self-propelled particles have been reported, essential factors that determine the morphology of the patterns have remained unclear. Here, we explore theoretically how torque formed upon collision of the particles affects the dynamic self-organization of the particles and determine the patterns. Based on a particle-based model with collision-induced torque and torque associated with self-propulsion, we find that introducing collision-induced torque turns the homogeneous bi-directionally aligned particles into rotating mono-polar flocks, which helps resolve a discrepancy in the earlier observations in microfilament gliding assays.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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