دانلود کتاب اتلاف و کنترل در سیستم های غیر تعادلی میکروسکوپی

Encompassing several independent research thrusts, Dr. Steven Large’s thesis tackles a wide range of important open questions in nonequilibrium statistical mechanics, a subfield of physics that promises insights into the design principles of molecular-scale biological systems. Steve’s thesis work establishes a multifaceted extension of the deterministic control framework that has been the workhorse of the field for the past 20 years to systems that are strongly fluctuating and autonomous, thereby facilitating the application of stochastic thermodynamics ideas to understand molecular machines in nanotechnology and in living things. This thesis contains several distinct types of contributions: purely theoretical derivations, numerical simulations to systematically characterize model systems, experimental design, and analysis of experimental data. In his examination of the control of strongly fluctuating microscopic systems, Steve derived simple, tractable, and surprisingly general conclusions (that upon reflection are intuitive). He subsequently generalized this work to autonomous settings, establishing connections to the chemical kinetics literature. As part of this project, Steve uncovered a conceptual blind spot in recent stochastic thermodynamics research, establishing the rigorous generalization of fundamental thermodynamics concepts to autonomous complex systems. On the applied side, Steve put these theoretical developments to work by designing, analyzing, and interpreting the first experimental demonstration (by collaborators Sara Tafoya and Carlos Bustamante, UC Berkeley) of the utility of this generally applicable method (that some naturally evolved machines show evidence of using) for designing energetically efficient control in biomolecules: drive slowly where friction is large and rapidly where friction is small. He analyzed raw data from equilibrium single-molecule experiments on small DNA molecules and designed subsequent nonequilibrium experiments demonstrating that protocols optimized using this framework systematically and statistically significantly reduced energy loss compared to naive protocols, across a wide range of experimental unfolding speeds and wildly different DNA molecules.