二维电子流体中普遍流体动力学流动的几何控制
Geometric control of universal hydrodynamic flow in a two dimensional electron fluid
摘要：
流体动力学是现代物理学的基石之一，最近在固体中电子的传输中得到了应用。在大多数固体中，电子输运受外在因素支配，如样品几何形状和杂质散射。然而，在流体动力学机制中，库仑相互作用将电子运动从独立粒子转变为粘性“电子流体”的集体运动。流体粘度是电子系统的固有性质，仅由电子-电子相互作用决定。解析通用特性粘度具有挑战性，因为它仅通过与样品边界的相互作用影响电阻，其粗糙度不仅未知，而且因器件而异。在这里，我们消除了所有的未知参数，通过制造具有光滑侧壁的样品，以实现完美的滑移边界条件，这一直是难以捉摸的分子流体和电子系统。我们设计了设备的几何形状，以创建粘性耗散，并揭示了2D系统的真实内在流体动力学特性。我们观察到一个明确的过渡，从弹道到流体动力学电子运动，由温度和磁场驱动。我们直接测量的粘度和电子-电子散射寿命（费米准粒子寿命）在很宽的温度范围内没有拟合参数，并显示它们有很强的依赖于电子密度，不能解释的传统理论的基础上的随机相位近似。
英文摘要：
Fluid dynamics is one of the cornerstones of modern physics and has recently found applications in the transport of electrons in solids. In most solids electron transport is dominated by extrinsic factors, such as sample geometry and scattering from impurities. However in the hydrodynamic regime Coulomb interactions transform the electron motion from independent particles to the collective motion of a viscous `electron fluid'. The fluid viscosity is an intrinsic property of the electron system, determined solely by the electron-electron interactions. Resolving the universal intrinsic viscosity is challenging, as it only affects the resistance through interactions with the sample boundaries, whose roughness is not only unknown but also varies from device to device. Here we eliminate all unknown parameters by fabricating samples with smooth sidewalls to achieve the perfect slip boundary condition, which has been elusive both in molecular fluids and electronic systems. We engineer the device geometry to create viscous dissipation and reveal the true intrinsic hydrodynamic properties of a 2D system. We observe a clear transition from ballistic to hydrodynamic electron motion, driven by both temperature and magnetic field. We directly measure the viscosity and electron-electron scattering lifetime (the Fermi quasiparticle lifetime) over a wide temperature range without fitting parameters, and show they have a strong dependence on electron density that cannot be explained by conventional theories based on the Random Phase Approximation.
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