论文标题

最大化信息引擎的功率和速度

Maximizing power and velocity of an information engine

论文作者

Saha, Tushar K., Lucero, Joseph N. E., Ehrich, Jannik, Sivak, David A., Bechhoefer, John

论文摘要

纠正热波动的信息驱动引擎是麦克斯韦 - 恶魔思想实验的现代实现。我们引入了一个基于重型胶体颗粒的简单设计,该设计由光学陷阱固定并浸入水中。使用经过精心设计的反馈循环,我们对“信息棘轮”的实验实现,利用了有利的“上升”波动来提高重量的重量,在不进行外部工作的情况下存储势能。通过通过简单理论优化棘轮设计以表现性能,我们发现工作存储的速率和定向运动的速度仅受到发动机的物理参数的限制:粒子的大小,棘轮弹簧的刚度,动作产生的摩擦,以及周围介质的温度。值得注意的是,由于性能随观察频率的增加而饱和,因此测量过程不是限制因素。提取的功率和速度至少比先前报道的发动机高的数量级。

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 is 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.

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