Nonequilibrium Statistical Mechanics of Nanoscale Systems

纳米系统的非平衡统计力学

• 批准号: 1206971
• 负责人: Christopher Jarzynski
• 金额: $ 43.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1206971&HistoricalAwards=false
• 关键词: Nonequilibrium; Statistical; Mechanics; Nanoscale; Systems

TECHNICAL SUMMARYThis award is funded by the Division of Materials Research and the Chemistry Division. It supports theoretical research and education related to the nonequilibrium behavior of small systems, from biomolecular complexes to artificial molecular machines. The primary aim of the research is to advance basic understanding of the laws of thermodynamics and how they apply to nanoscale systems, where fluctuations are important and unavoidable. The PI's approach will include exact analysis of tractable models, theoretical and numerical modeling of systems studied experimentally, and the formulation of general principles. Two central questions to be addressed are:1) What fundamental constraints does statistical thermodynamics impose on the processing of information by molecular-level systems?2) What are the principles by which the directed motion of artificial molecular machines can be generated and controlled by the variation of macroscopic external parameters?The first question is motivated both by the reality that biomolecular systems do perform information-processing tasks, and artificial molecular machines are beginning to show this capability. The PI will develop models that capture essential features of information processing by autonomous molecular systems, while remaining accessible to exact analysis or numerical simulation. By exposing explicit, transparent mechanisms of operation, such models will offer simple paradigms for investigating the thermodynamics of information processing in situations where thermal fluctuations dominate.Research in the second question was originally motivated by experiments involving the manipulation of ring-like molecules, catenanes, by the variation of external stimuli. The aim is a systematic control theory for artificial molecular machines in the presence of substantial thermal noise. The PI will approach this problem within the general theoretical framework of stochastic pumps, in which a system makes random transitions among a network of discrete states, with time-dependent transition rates. Here the topology of the network plays an important role in the analysis.This award supports graduate student and postdoctoral level training in the methods of theoretical and computational statistical mechanics.NON-TECHNICAL SUMMARYThis award is funded by the Division of Materials Research and the Chemistry Division. It supports theoretical research and education on biomolecular systems and materials far from the steady state of equilibrium.Nature teaches us that small systems such as individual molecules and molecular complexes are capable of exhibiting a rich diversity of dynamical behavior. Prime evidence of this diversity is found in the biomolecular machines that perform the numerous and often sophisticated tasks required to maintain life: they harness energy, convert it from one form to another, and faithfully copy and translate genetic information. In recent years there has been growing interest and progress not only in understanding the details of how biological molecules accomplish these tasks, but also in synthesizing artificial molecular machines that mimic such capabilities. This award supports theoretical research and teaching that aims to uncover and clarify the basic physical laws that govern these phenomena.The PI will develop a better understanding of the means by which nanoscale systems are able to perform tasks that involve the processing of information, as occurs in the replication and repair of DNA. A deeper understanding of the thermodynamics of information processing will suggest strategies for the synthesis and improvement of programmable artificial molecular machines. The PI will also analyze how microscopic systems respond to time-dependent variations in temperature, chemical conditions and laser light. This research aims to advance the ability to manipulate events on the scale of molecules in a controlled manner.The activities funded by this award will prepare graduate students and postdocs for the challenges of research in rapidly evolving topics at the intersection of physics, chemistry and biology.

技术概述：该奖项由材料研究部和化学部资助。它支持与小系统的非平衡行为有关的理论研究和教育，从生物分子复合物到人工分子机器。这项研究的主要目的是促进对热力学定律的基本理解，以及它们如何应用于纳米级系统，在纳米级系统中，波动是重要的和不可避免的。PI的方法将包括对可处理模型的精确分析，实验研究系统的理论和数值建模，以及一般原则的制定。需要解决的两个核心问题是：1)统计热力学对分子级系统的信息处理施加了哪些基本约束？2)通过宏观外部参数的变化，产生和控制人工分子机器定向运动的原理是什么？第一个问题的原因是，生物分子系统确实执行信息处理任务，而人工分子机器也开始显示出这种能力。PI将开发模型，捕捉自主分子系统信息处理的基本特征，同时保持对精确分析或数值模拟的可访问性。通过揭示明确的、透明的操作机制，这些模型将为在热波动占主导地位的情况下研究信息处理的热力学提供简单的范例。第二个问题的研究最初是由一些实验激发的，这些实验涉及到通过外部刺激的变化来操纵环状分子，即链链烷。目的是为存在大量热噪声的人工分子机器建立系统的控制理论。PI将在随机泵的一般理论框架内解决这个问题，在随机泵中，系统在离散状态网络中进行随机转换，具有随时间变化的转换速率。这里，网络的拓扑结构在分析中起着重要的作用。该奖项支持理论和计算统计力学方法的研究生和博士后水平的培训。该奖项由材料研究部和化学部资助。它支持远离稳定平衡状态的生物分子系统和材料的理论研究和教育。大自然告诉我们，像单个分子和分子复合物这样的小系统能够表现出丰富多样的动力学行为。这种多样性的主要证据是在生物分子机器中发现的，这些生物分子机器执行维持生命所需的大量且往往复杂的任务：它们利用能量，将能量从一种形式转化为另一种形式，忠实地复制和翻译遗传信息。近年来，不仅在了解生物分子如何完成这些任务的细节方面，而且在合成模拟这些能力的人工分子机器方面，人们的兴趣和进展越来越大。该奖项支持旨在揭示和阐明支配这些现象的基本物理定律的理论研究和教学。PI将更好地理解纳米级系统如何能够执行涉及信息处理的任务，就像DNA的复制和修复一样。对信息处理热力学的深入理解将为合成和改进可编程人工分子机器提供策略。PI还将分析微观系统对温度、化学条件和激光随时间变化的反应。这项研究旨在以可控的方式提高在分子尺度上操纵事件的能力。由该奖项资助的活动将为研究生和博士后准备在物理、化学和生物学交叉领域快速发展的课题的研究挑战。