A New Thermodynamic Theory for Small Fluctuating Systems: From Nanodevices to Cellular Biology


CALL: 2011

DOMAIN: MS - Materials, Physics and Engineering

FIRST NAME: Massimiliano

LAST NAME: Esposito



HOST INSTITUTION: University of Luxembourg

KEYWORDS: Nonequilibrium Thermodynamics, Statistical Mechanics, Small systems, Stochastic Processes, Nonequilibrium Fluctuations, Quantum Transport, Complex Systems, Information processing.

START: 2012-01-01

END: 2017-06-30

WEBSITE: https://www.uni.lu

Submitted Abstract

Recent developments in experimental techniques significantly improved our ability to build and manipulate systems at the nanoscale. The driving force behind these achievements is the enormous impact of artificial nanodevices on modern technologies. They are used, for example, to convert and transfer energy, to perform logical operations, and to store memory in an efficient and compact way. Current strategies to design new nanodevices or enhance their current performance remain largely system-specific and often empirical. This lack of general (i.e. system-independent) guiding principles largely results from the inability of traditional thermodynamics to deal with the effects of strong fluctuations, which are ubiquitous in nanodevices, and with the fact that these systems often operate far from equilibrium. The goal of this project is to develop a new thermodynamic theory, called stochastic thermodynamics, which incorporates these characteristic features of nanodevices. I have already significantly contributed to establish the basis of this new theory which shows how the stochastic nonequilibrium dynamics of a system can be connected to its thermodynamic behaviour. In this project, I intend to further develop stochastic thermodynamics along three main directions. First, I want to incorporate notions of information, computation, feedback and control in the theory of stochastic thermodynamics. This will allow me to address issues related to the performance of information processing (such as copying and erasing information) in finite time. It is known that in order to minimize losses (dissipation) and maximize accuracy, the information processing has to be done infinitely slowly. I want to propose ways to optimize these features in the regimes of much greater practical interest where the information processing is performed at finite speed. This new theory will also allow me to study small devices (such as energy converters) controlled by time-dependent feedback mechanisms. I want to quantify the gains in performance that these feedbacks can achieve by understanding how they affect the thermodynamic description of the devices. Second, I want to extend stochastic thermodynamics to properly describe the quantum effects which arise when low temperatures and small system sizes are considered. To do so, key thermodynamic concepts such as entropy, entropy production, and detailed balance need to be appropriately defined in terms of the central quantities describing transport in nonequilibrium quantum systems. This will allow me to propose realistic ways to identify and study quantum effects in the electronic current fluctuations through quantum dots and molecular junctions. It will also allow me to assess the extent in which these quantum effects can affect the efficiency of small quantum devices (e.g. energy converters). Finally, I want to demonstrate that stochastic thermodynamics is a suitable tool to study the properties of the natural “nanodevices” fuelling the activity of biological cells. I want to use this theory to study, from a physics standpoint, the basic mechanisms by which the molecular machinery of the cell (e.g. enzymes, molecular motors) operates. A better understanding of the strategies that natural selection has found to operate efficiently and reliably far from equilibrium and in the highly fluctuating environment of the cell could prove very useful to engineer more efficient artificial nanodevices.My research strategy consists in proposing model systems that can be studied using analytical as well as numerical mathematical tools. These models range from simple analytically solvable models, which are used to elaborate new theories, to more realistic models, which can be studied using computer simulations and confronted to experimental results.

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