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Entropy

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...

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Related Experiment Video

Updated: May 29, 2025

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

Inference of entropy production for periodically driven systems.

Pedro E Harunari¹, Carlos E Fiore², Andre C Barato³

¹University of Luxembourg, Complex Systems and Statistical Mechanics, Department of Physics and Materials Science, L-1511 Luxembourg City, Luxembourg.

Physical Review. E

|February 7, 2025

Summary

This summary is machine-generated.

Estimating entropy production in periodically driven systems is crucial. This study develops a new method using transition statistics and waiting times, independent of initial conditions, for accurate entropy production estimation.

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Area of Science:

Stochastic thermodynamics
Non-equilibrium systems

Background:

Estimating entropy production is vital for understanding non-equilibrium systems.
Existing methods often focus on steady states with fixed forces.
Periodically driven systems present unique challenges for entropy estimation.

Purpose of the Study:

To develop a method for estimating entropy production in periodically driven systems.
To provide an estimate independent of initial conditions and protocol tracking.
To analyze the method's performance using a molecular pump model.

Main Methods:

Utilizing statistics of visible transitions and waiting times.
Adapting a method from non-equilibrium steady states to time-dependent systems.
Analyzing a molecular pump model with varying energies and barriers.

Main Results:

An entropy production estimate dependent on inter-transition times but not the first transition time.
An inequality relating the rate of entropy production and its estimate, including an extra term.
Net motion (probability current) is necessary for a relevant entropy production estimate.

Conclusions:

The proposed method offers a practical approach to entropy production estimation in complex, time-dependent systems.
The findings highlight the importance of system dynamics and probability currents in thermodynamic assessments.
This work advances the experimental and theoretical understanding of entropy in non-equilibrium thermodynamics.