Saha Tushar K, Lucero Joseph N E, Ehrich Jannik, Sivak David A, Bechhoefer John
Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
Proc Natl Acad Sci U S A. 2021 May 18;118(20). doi: 10.1073/pnas.2023356118.
Information-driven engines that rectify thermal fluctuations are a modern realization of the Maxwell-demon thought experiment. We introduce a simple design based on a heavy colloidal particle, held by an optical trap and immersed in water. Using a carefully designed feedback loop, our experimental realization of an "information ratchet" takes advantage of favorable "up" fluctuations to lift a weight against gravity, storing potential energy without doing external work. By optimizing the ratchet design for performance via a simple theory, we find that the rate of work storage and velocity of directed motion are limited only by the physical parameters of the engine: the size of the particle, stiffness of the ratchet spring, friction produced by the motion, and temperature of the surrounding medium. Notably, because performance saturates with increasing frequency of observations, the measurement process is not a limiting factor. The extracted power and velocity are at least an order of magnitude higher than in previously reported engines.
能纠正热涨落的信息驱动引擎是麦克斯韦妖思想实验的现代实现方式。我们基于一个重胶体粒子引入了一种简单设计，该粒子由光阱捕获并浸没在水中。通过精心设计的反馈回路，我们对“信息棘轮”的实验实现利用了有利的“向上”涨落来克服重力提升重物，在不做外部功的情况下存储势能。通过一个简单理论优化棘轮设计以提高性能，我们发现功存储速率和定向运动速度仅受引擎的物理参数限制：粒子大小、棘轮弹簧刚度、运动产生的摩擦力以及周围介质的温度。值得注意的是，由于性能随着观测频率增加而饱和，测量过程不是一个限制因素。提取的功率和速度比之前报道的引擎至少高一个数量级。
