Publication
Entropy in Spin Relaxation, Spintronics, and Magnetic Resonance
Résumé
The entropy change during spin relaxation for a realistic model system is studied, whose spin dynamics can be handled with the Boltzmann equation. The time evolution of the von Neumann entropy is monitored during the process and is compared with the recently introduced concept of the Loschmidt echo envelope. The time evolution of the two quantities is remarkably similar which helps to distinguish reversible and irreversible changes to the ensemble spin state. The method is also demonstrated for a toy model of nuclear magnetic resonance, where the usual pi spin echo is performed numerically, and the echo envelope also follows the time evolution of the von Neumann entropy. The numerical approach highlights the utility of the entropy concept in analyzing various processes which occur during spin relaxation.
Source officielle
À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Proximité ontologique
Concepts associés (32)
Entropie (thermodynamique)
L'entropie est une grandeur physique qui caractérise le degré de désorganisation d'un système. Introduite en 1865 par Rudolf Clausius, elle est nommée à partir du grec , littéralement « action de se retourner » pris au sens de « action de se transformer ». En thermodynamique, l'entropie est une fonction d'état extensive (c'est-à-dire, proportionnelle à la quantité de matière dans le système considéré). Elle est généralement notée , et dans le Système international d'unités elle s'exprime en joules par kelvin ().
Entropie de Shannon
En théorie de l'information, l'entropie de Shannon, ou plus simplement entropie, est une fonction mathématique qui, intuitivement, correspond à la quantité d'information contenue ou délivrée par une source d'information. Cette source peut être un texte écrit dans une langue donnée, un signal électrique ou encore un fichier informatique quelconque (suite d'octets). Elle a été introduite par Claude Shannon. Du point de vue d'un récepteur, plus la source émet d'informations différentes, plus l'entropie (ou incertitude sur ce que la source émet) est grande.
Entropy (statistical thermodynamics)
The concept entropy was first developed by German physicist Rudolf Clausius in the mid-nineteenth century as a thermodynamic property that predicts that certain spontaneous processes are irreversible or impossible. In statistical mechanics, entropy is formulated as a statistical property using probability theory. The statistical entropy perspective was introduced in 1870 by Austrian physicist Ludwig Boltzmann, who established a new field of physics that provided the descriptive linkage between the macroscopic observation of nature and the microscopic view based on the rigorous treatment of large ensembles of microstates that constitute thermodynamic systems.
Publications associées (55)
Pressure-freezing of dodecane: exploring the crystal structures, formation kinetics and phase diagrams for colossal barocaloric effects in n-alkanes
Barocaloric (BC) materials provide cheaper and more energy efficient alternatives to traditional refrigerants. Some liquid alkanes were recently shown to exhibit a colossal BC effect, matching the entropy changes in commercial vapour-liquid refrigerants. D ...
Cambridge2023Tree-AMP: Compositional Inference with Tree Approximate Message Passing
Florent Gérard Krzakala, Lenka Zdeborová
We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms ...
2023Coarse-grained entropy production with multiple reservoirs: Unraveling the role of time scales and detailed balance in biology-inspired systems
Daniel Maria Busiello, Amos Maritan, Deepak Gupta
A general framework to describe a vast majority of biology-inspired systems is to model them as stochastic processes in which multiple couplings are in play at the same time. Molecular motors, chemical reaction networks, catalytic enzymes, and particles ex ...
2020MOOCs associés (14)
Basic Steps in Magnetic Resonance
A MOOC to discover basic concepts and a wide range of intriguing applications of magnetic resonance to physics, chemistry, and biology
Fundamentals of Biomedical Imaging: Magnetic Resonance Imaging (MRI)
Learn about magnetic resonance, from the physical principles of Nuclear Magnetic Resonance (NMR) to the basic concepts of image reconstruction (MRI).
Fundamentals of Biomedical Imaging: Magnetic Resonance Imaging (MRI)
Learn about magnetic resonance, from the physical principles of Nuclear Magnetic Resonance (NMR) to the basic concepts of image reconstruction (MRI).