Date of Award

8-30-2022

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Chemistry/Physical/Analytical Chemistry

First Advisor

Jason R. Green

Second Advisor

Jason Evans

Third Advisor

Michelle Foster

Abstract

Living systems function dynamically. They generate work with molecular motors, assemble dynamic structures like microtubules, keep time with circadian clocks, and catalyze the replication of RNA. Implementing these functions in synthetic nanoscale systems requires a quantitative understanding of how to optimize their performance, despite the dissipative losses associated with operating in a fluctuating environment on finite timescales. Near equilibrium, thermodynamic intuition suggests that fast, irreversible processes will dissipate more energy and entropy than slow, quasistatic processes connecting the same initial and final states. For small systems away from equilibrium, recently discovered thermodynamic speed limits suggest that processes that are fast will dissipate more than those that are slow. Here, the consequences of this time constraint to speed, dissipation, and system function are analyzed. To apply this time constraint, an exact expression for the path probabilities of continuous-time Markov chains is derived from the path summation solution to the master equation. Using this formula, it is demonstrated that faster processes can dissipate less far from equilibrium. Further, a system classification is proposed for these systems based on the correlation of speed with dissipation and with performance metrics of system function. Models presented herein serve as minimal prototypes for designing kinetics to sculpt the nonequilibrium path space to optimize performance, despite ever-present dissipation when there is a need for speed.

Comments

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