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Creators/Authors contains: "Gao, Tong"

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  1. Free, publicly-accessible full text available June 1, 2024
  2. We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of small ions such as inorganic ions and large salt ions that mimic ionic liquids (ILs) on lithium dendrite growth. In our cellular automaton model, molecular and atomistic details are largely coarse-grained to reduce the number of model parameters. During lithium deposition, the cations of the salt and ILs form positively charged electrostatic shields around the tip of the dendrites, and the anions of the salt and ILs form negative local potential lumps in adjacent areas to the dendrite. Both of the effects change the distribution of the electrostatic potential and notably inhibit dendrite formation between electrodes. The applied voltage and the physical properties of the salt ions and ILs, such as the size of the ions, the size asymmetry between the cation and anion, the dielectric constant, the excluded volume of the ions, and the model parameter η , notably affect electric-field screening and hence the variation in the local potential, resulting in substantial changes in the aspect ratio and the average height of the dendrites. Our present results suggest that the large salts such as ILs more significantly inhibit the dendrite growth than the small ions, mainly because the ions highly dissociated in ILs can participate in electrostatic shielding to a greater degree. To reduce the computational complexity and burden of the MC simulation, we also constructed a surrogate model with ensemble neural networks. 
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  3. Dense assemblies of self-propelling rods (SPRs) may exhibit fascinating collective behaviors and anomalous physical properties that are far away from equilibrium. Using large-scale Brownian dynamics simulations, we investigate the dynamics of disclination defects in 2D fluidized swarming motions of dense dry SPRs ( i.e. , without hydrodynamic effects) that form notable local positional topological structures that are reminiscent of smectic order. We find the deformations of smectic-like rod layers can create unique polar structures that lead to slow translations and rotations of ±1/2-order defects, which are fundamentally different from the fast streaming defect motions observed in wet active matter. We measure and characterize the statistical properties of topological defects and reveal their connections with the coherent structures. Furthermore, we construct a bottom-up active-liquid-crystal model to analyze the instability of polar lanes, which effectively leads to defect formation between interlocked polar lanes and serves as the origin of the large-scale swarming motions. 
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  4. Microorganisms can efficiently navigate in anisotropic complex fluids, but the precise swimming mechanisms remain largely unexplored. Their dynamics are determined by the interplay between multiple effects, including the fluid's orientation order, swimmer's undulatory gait, and the finite length. Here we extend the numerical study of the two-dimensional undulatory motions of a flexible swimmer in lyotropic liquid-crystalline polymers (LCPs) by Lin et al. (2021) to the scenarios of arbitrary swimming directions with respect to the nematic director. The swimmer is modeled as a nearly inextensible yet flexible fiber with imposed traveling-wave like actuation. We investigate the orientation-dependent swimming behaviors in nematic LCPs for an infinite long sheet (i.e., Taylor's swimming sheet model) and finite-length swimmers. We demonstrate that the swimmer must be sufficiently stiff to produce undulatory deformations to gain net motions. Moreover, a motile finite-length swimmer can reorient itself to swim parallel with the nematic director, due to a net body torque arising from the asymmetric distribution of the polymer force along the body. 
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  5. null (Ed.)
    Microorganisms may exhibit rich swimming behaviours in anisotropic fluids, such as liquid crystals, which have direction-dependent physical and rheological properties. Here we construct a two-dimensional computation model to study the undulatory swimming mechanisms of microswimmers in a solution of rigid, rodlike liquid crystal polymers. We describe the fluid phase using Doi's $Q$ -tensor model, and treat the swimmer as a finite-length flexible fibre with imposed propagating travelling waves on the body curvature. The fluid–structure interactions are resolved via an immersed boundary method. Compared with the swimming dynamics in Newtonian fluids, we observe non-Newtonian behaviours that feature both enhanced and retarded swimming motions in lyotropic liquid crystal polymers. We reveal the propulsion mechanism by analysing the near-body flow fields and polymeric force distributions, together with asymptotic analysis for an idealized model of Taylor's swimming sheet. 
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  6. Interest in soft robotics has increased in recent years due to their potential in a myriad of applications. A wide variety of soft robots has emerged, including bio-inspired robotic swimmers such as jellyfish, rays, and robotic fish. However, the highly nonlinear fluid-structure interactions pose considerable challenges in the analysis, modeling, and feedback control of these soft robotic swimmers. In particular, developing models that are of high fidelity but are also amenable to control for such robots remains an open problem. In this work, we pro- pose a data-driven approach that exploits Koopman operators to obtain a linear representation of the soft swimmer dynamics. Specifically, two methodologies are explored for obtaining the basis functions of the the operator, one based on data-based derivatives estimated using high-gain observers, and the other based on the dynamics structure of a tail-actuated rigid-body robotic fish. The resulting approximate finite-dimensional operators are trained and evaluated using data from high-fidelity CFD simulations that incorporate fluid-structure interactions. Validation results demonstrate that, while both methods are promising in producing control-oriented models, the approach based on derivative estimates shows higher accuracy in state prediction. 
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