Stochastic Energetics by now commonly designates the emerging field that bridges the gap between stochastic dynamical processes and thermodynamics. Triggered by the vast improvements in spatio-temporal resolution in nanotechnology, stochastic energetics develops a framework for quantifying individual realizations of a stochastic process on the mesoscopic scale of thermal fluctuations. This is needed to answer such novel questions as: Can one cool a drop of water by agitating an immersed nano-particle? How does heat flow if a Brownian particle pulls a polymer chain? Can one measure the free-energy of a system through a single realization of the associated stochastic process? This book will take the reader gradually from the basics to the applications: Part I provides the necessary background from stochastic dynamics (Langevin, master equation), Part II introduces how stochastic energetics describes such basic notions as heat and work on the mesoscopic scale, Part III details several applications, such as control and detection processes, as well as free-energy transducers. It aims in particular at researchers and graduate students working in the fields of nanoscience and technology.

"Statistical Mechanics in a Nutshell offers a concise, self-contained advanced undergraduate to graduate level introduction to this rapidly developing field, requiring a background in elementary calculus and elementary mechanics. It starts with the basics, introduces the most important developments in classical statistical mechanics over the last thirty years, and guides readers to the very threshold of today's cutting-edge research. The author has revised the first 5 chapters (harmonizing the notation, improving the proofs, checking all exercises and adding a few additional interesting ones). He has also added a new chapter on stochastic thermodynamics, which finds its place after the 9th chapter. The appendices will also be completely rewritten, emphasizing the role of convexity and the Jensen inequality. Chapter 8 will be improved to include some important topics: namely, thermostats and fast algorithms. Chapter 9 will also be rewritten to modernize it and to transition to the new chapter on stochastic thermodynamics. Chapter 10 will be split in two, to focus on "disordered systems" and "complex systems," to emphasize applications (including neural networks and optimization algorithms), and to introduce some fundamental techniques (like the cavity method and message passing) at an elementary level. The goal of the new edition is to help the reader find her/his way into and through the vast, recent literature concerning statistical mechanics and to build a sense of the many fields in which the discipline has recently been applied"--

This book is a printed edition of the Special Issue "Thermodynamics and Statistical Mechanics of Small Systems" that was published in Entropy

This book presents collaborative research works carried out by experimentalists and theorists around the world in the field of nonlinear dynamical systems. It provides a forum for applications of nonlinear systems while solving practical problems in science and engineering. Topics include: Applied Nonlinear Optics, Sensor, Radar & Communication Signal Processing, Nano Devices, Nonlinear Biomedical Applications, Circuits & Systems, Coupled Nonlinear Oscillator, Precision Timing Devices, Networks, and other contemporary topics in the general field of Nonlinear Science. This book provides a comprehensive report of the various research projects presented at the International Conference on Applications in Nonlinear Dynamics (ICAND 2016) held in Denver, Colorado, 2016. It can be a valuable tool for scientists and engineering interested in connecting ideas and methods in nonlinear dynamics with actual design, fabrication and implementation of engineering applications or devices.>

After an insightful introductory part on recent developments in the thermodynamics of small systems, the author presents his contribution to a long-standing problem, namely the connection between irreversibility and dissipation. He develops a method based on recent results on fluctuation theorems that is able to estimate dissipation using only information acquired in a single, sufficiently long, trajectory of a stationary nonequilibrium process. This part ends with a remarkable application of the method to the analysis of biological data, in this case, the fluctuations of a hair bundle. The third part studies the energetics of systems that undergo symmetry breaking transitions. These theoretical ideas lead to, among other things, an experimental realization of a Szilard engine using manipulated colloids. This work has the potential for important applications ranging from the analysis of biological media to the design of novel artificial nano-machines.

The author investigates athermal fluctuation from the viewpoints of statistical mechanics in this thesis. Stochastic methods are theoretically very powerful in describing fluctuation of thermodynamic quantities in small systems on the level of a single trajectory and have been recently developed on the basis of stochastic thermodynamics. This thesis proposes, for the first time, a systematic framework to describe athermal fluctuation, developing stochastic thermodynamics for non-Gaussian processes, while thermal fluctuations are mainly addressed from the viewpoint of Gaussian stochastic processes in most of the conventional studies. First, the book provides an elementary introduction to the stochastic processes and stochastic thermodynamics. The author derives a Langevin-like equation with non-Gaussian noise as a minimal stochastic model for athermal systems, and its analytical solution by developing systematic expansions is shown as the main result. Furthermore, the a uthor shows a thermodynamic framework for such non-Gaussian fluctuations, and studies some thermodynamics phenomena, i.e. heat conduction and energy pumping, which shows distinct characteristics from conventional thermodynamics. The theory introduced in the book would be a systematic foundation to describe dynamics of athermal fluctuation quantitatively and to analyze their thermodynamic properties on the basis of stochastic methods.

Molecular motors convert chemical energy (typically from ATP hydrolysis) to directed motion and mechanical work. Biomolecular motors are proteins able of converting chemical energy into mechanical motion and force. Because of their dimension, the many small parts that make up molecular motors must operate at energies only a few times greater than those of the thermal baths. The description of molecular motors must be stochastic in nature. Their actions are often described in terms of Brownian Ratchets mechanisms. In order to describe the principles used in their movement, we need to use the tools that theoretical physics give us. In this book we centralize on the some physical mechanisms of molecular motors.

This book provides an introduction to the emerging field of quantum thermodynamics, with particular focus on its relation to quantum information and its implications for quantum computers and next generation quantum technologies. The text, aimed at graduate level physics students with a working knowledge of quantum mechanics and statistical physics, provides a brief overview of the development of classical thermodynamics and its quantum formulation in Chapter 1. Chapter 2 then explores typical thermodynamic settings, such as cycles and work extraction protocols, when the working material is genuinely quantum. Finally, Chapter 3 explores the thermodynamics of quantum information processing and introduces the reader to some more state of-the-art topics in this exciting and rapidly developing research field.

In this book the author presents a general formalism of nonequilibrium thermodynamics with complex information flows induced by interactions among multiple fluctuating systems. The author has generalized stochastic thermodynamics with information by using a graphical theory. Characterizing nonequilibrium dynamics by causal networks, he has obtained a novel generalization of the second law of thermodynamics with information that is applicable to quite a broad class of stochastic dynamics such as information transfer between multiple Brownian particles, an autonomous biochemical reaction, and complex dynamics with a time-delayed feedback control. This study can produce further progress in the study of Maxwell’s demon for special cases. As an application to these results, information transmission and thermodynamic dissipation in biochemical signal transduction are discussed. The findings presented here can open up a novel biophysical approach to understanding information processing in living systems.

This thesis reveals how the feedback trap technique, developed to trap small objects for biophysical measurement, could be adapted for the quantitative study of the thermodynamic properties of small systems. The experiments in this thesis are related to Maxwell’s demon, a hypothetical intelligent, “neat fingered” being that uses information to extract work from heat, apparently creating a perpetual-motion machine. The second law of thermodynamics should make that impossible, but how? That question has stymied physicists and provoked debate for a century and a half. The experiments in this thesis confirm a hypothesis proposed by Rolf Landauer over fifty years ago: that Maxwell’s demon would need to erase information, and that erasing information—resetting the measuring device to a standard starting state—requires dissipating as much energy as is gained. For his thesis work, the author used a “feedback trap” to study the motion of colloidal particles in “v irtual potentials” that may be manipulated arbitrarily. The feedback trap confines a freely diffusing particle in liquid by periodically measuring its position and applying an electric field to move it back to the origin.