非线性统计力学驱动Polymer网络的本征电致伸缩和体积转矩
Nonlinear Statistical Mechanics Drives Intrinsic Electrostriction and Volumetric Torque in Polymer Networks
摘要：
统计力学是理解聚合物电弹性的重要工具，因为聚合物的弹性主要是由于熵。然而，一个常见的方法，统计力学的聚合物链，高斯链近似，错过了关键的物理。通过考虑问题的非线性，我们显示了一个强耦合的聚合物链的变形和其介电响应，即它的净偶极子。当具有这种耦合的链在弹性体网络中交联并且施加电场时，场打破弹性体的弹性性质的对称性，并且与静电转矩和不可压缩性结合，导致固有电致伸缩。相反，变形可以破坏介电响应的对称性，导致体积扭矩（即，每单位体积的力偶应力或扭矩）和不对称致动。这两种现象对设计高效的软致动器和软电活性材料具有重要意义;特别是体积扭矩机制的存在，可用于开发更高自由度的致动器并实现仿生运动。
英文摘要：
Statistical mechanics is an important tool for understanding polymer electroelasticity because the elasticity of polymers is primarily due to entropy. However, a common approach for the statistical mechanics of polymer chains, the Gaussian chain approximation, misses key physics. By considering the nonlinearities of the problem, we show a strong coupling between the deformation of a polymer chain and its dielectric response; that is, its net dipole. When chains with this coupling are cross-linked in an elastomer network and an electric field is applied, the field breaks the symmetry of the elastomer's elastic properties, and, combined with electrostatic torque and incompressibility, leads to intrinsic electrostriction. Conversely, deformation can break the symmetry of the dielectric response leading to volumetric torque (i.e., a couple stress or torque per unit volume) and asymmetric actuation. Both phenomena have important implications for designing high-efficiency soft actuators and soft electroactive materials; and the presence of mechanisms for volumetric torque, in particular, can be used to develop higher degree of freedom actuators and to achieve bioinspired locomotion.
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