自催化反应网络中的动态滞后现象
Dynamic hysteresis in an autocatalytic reaction network
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中文总结 AI 辅助
研究自催化反应网络中的动态滞后现象，以周期性驱动的施洛格模型为代表，通过滞后环面积研究其受驱动协议、固有涨落和系统大小的影响，揭示控制因素作用，还将其与信息论和随机热力学联系，为化学逻辑门和计算机提供启示。
中文摘要 AI 辅助
我们发现自催化反应网络由于其固有弛豫时间尺度与周期性驱动时间尺度之间的竞争可呈现动态滞后。自催化反应步骤使自催化物种浓度产生双稳态，产物物种的周期性注入提供外部驱动。滞后源于自催化物种浓度响应与外部周期性驱动之间的滞后。用周期性驱动的施洛格模型作为代表性双稳态化学系统，我们用滞后环面积确定滞后响应大小如何受驱动协议、固有涨落和系统大小控制。改变驱动频率和涨落强度会使滞后环面积翻转，而面积随驱动幅度和系统大小单调变化。与通常与弱周期强迫和噪声辅助放大相关的随机共振不同，动态滞后可表征更广泛外部控制条件下的反应系统响应。我们还表明延迟的浓度响应反映在香农熵和总熵产生率中，将动态滞后与信息论和随机热力学的不可逆性度量联系起来。总体而言，我们识别并解释了化学动态滞后中控制因素的作用，并对高效化学逻辑门及化学计算机提出了启示。
英文摘要
Here we show that an autocatalytic reaction network can exhibit dynamic hysteresis as a result of the competition between its intrinsic relaxation time scale and that of the periodic drive. The autocatalytic reaction steps generate bistability in the concentration of the autocatalytic species, and periodic pumping of the product species provides the external drive. Hysteresis arises from the lag between the concentration response of the autocatalytic species and the external periodic drive. The resulting hysteresis-loop area quantifies the extent of the system's hysteretic response. Using the periodically driven Schlögl model as a representative bistable chemical system, we use this loop area to determine how the magnitude of the hysteretic response is controlled by the driving protocol, intrinsic fluctuations, and the size of the system. Varying the driving frequency and the strength of the fluctuations causes a turnover of the hysteresis loop area, whereas the area changes monotonically with driving amplitude and system size. These trends identify the driving protocol and fluctuation strength as primary controls on the magnitude of dynamic hysteresis. In contrast to stochastic resonance, which is typically associated with weak periodic forcing and noise-assisted amplification, dynamic hysteresis can characterize the reaction-system response over a wider range of external control conditions. We further show that the delayed concentration response is mirrored in Shannon entropy and in the total entropy production rate, connecting dynamic hysteresis to information-theoretic and stochastic-thermodynamic measures of irreversibility. Overall, we identify and interpret the role of the controlling factors in chemical dynamic hysteresis and suggest implications for efficient chemical logic gates and eventually, chemical computers.
