论文标题
从主动循环布朗信息引擎中提取巨大的功率
Colossal power extraction from active cyclic Brownian information engines
论文作者
论文摘要
布朗信息引擎可以利用信息从热波动中提取工作。到目前为止，有关布朗信息引擎的研究考虑了在热浴中的系统。但是，自然界中的许多过程都发生在非平衡环境中，例如自propelled微生物的悬浮液或称为活动浴的细胞环境。在这里，我们介绍了一种原型模型，用于在高斯相关的活动浴中运行的Maxwell-Demon类型循环布朗信息引擎。主动发动机可以提取比热对应物的更多作品，超过了第二种信息热力学定律设定的结合。我们为活动发动机获得了一般的积分波动定理，其中包括从有效温度独特的活动浴中获得的其他共同信息。此有效描述修改了第二定律，并为提取的工作提供了新的上限。与在热浴中运行的被动信息引擎不同，主动信息引擎提取了在有限周期期间达到高峰的巨大功率。我们的研究提供了对在测量和反馈控制下活跃浴中合成和生物亚微米电动机的设计和功能的基本见解。
Brownian information engines can extract work from thermal fluctuations by utilizing information. So far, the studies on Brownian information engines consider the system in a thermal bath; however, many processes in nature occur in a nonequilibrium setting, such as the suspensions of self-propelled microorganisms or cellular environments called an active bath. Here, we introduce an archetypal model for Maxwell-demon type cyclic Brownian information engine operating in a Gaussian correlated active bath. The active engine can extract more work than its thermal counterpart, exceeding the bound set by the second law of information thermodynamics. We obtain a general integral fluctuation theorem for the active engine that includes additional mutual information gained from the active bath with a unique effective temperature. This effective description modifies the second law and provides a new upper bound for the extracted work. Unlike the passive information engine operating in a thermal bath, the active information engine extracts colossal power that peaks at the finite cycle period. Our study provides fundamental insights into the design and functioning of synthetic and biological submicron motors in active baths under measurement and feedback control.
