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Dissipation and Control in Microscopic Nonequilibrium Systems

  • Book
  • © 2021

Overview

  • Nominated as an outstanding PhD thesis by Simon Fraser University
  • Includes an accessible introduction to the mathematics that describes microscopic, fluctuating systems
  • Advances theory and simulation of nonequilibrium systems, and experimental design and data analysis of DNA hairpins

Part of the book series: Springer Theses (Springer Theses)

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Table of contents (11 chapters)

  1. Experimental Tests of Nonequilibrium Theory

  2. Dissipation in Nonequilibrium Systems Through the Lens of Control Theory

  3. The Nonequilibrium Physics of Autonomous Machines

Keywords

About this book

This thesis establishes a multifaceted extension of the deterministic control framework that has been a workhorse of nonequilibrium statistical mechanics, to stochastic, discrete, and autonomous control mechanisms. This facilitates the application of ideas from stochastic thermodynamics to the understanding of molecular machines in nanotechnology and in living things. It also gives a scale on which to evaluate the nonequilibrium energetic efficiency of molecular machines, guidelines for designing effective synthetic machines, and a perspective on the engineering principles that govern efficient microscopic energy transduction far from equilibrium. The thesis also documents the author’s design, analysis, and interpretation of the first experimental demonstration of the utility of this generally applicable method for designing energetically-efficient control in biomolecules. Protocols designed using this framework systematically reduced dissipation, when compared to naive protocols, in DNA hairpins across a wide range of experimental unfolding speeds and between sequences with wildly different physical characteristics.

Authors and Affiliations

  • Viewpoint Investment Partners, Calgary, Canada

    Steven J. Large

About the author

Dr. Steven Large grew up in Victoria, Canada, and received his undergraduate honours degree in Nanoscience in 2015 from the University of Guelph in Ontario, Canada. He then completed his PhD in Physics at Simon Fraser University in Vancouver, Canada, defending his thesis in December 2020 under the supervision of Prof. David Sivak. Currently, Dr. Large works as a Data Scientist with Viewpoint Investment Partners, in Calgary, Alberta, using quantitative analysis methods and machine learning techniques to develop robust long-term financial investment strategies.

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