The Gibbs Paradox: Lessons from Thermodynamics

The Gibbs paradox in statistical mechanics is often taken to indicate that already in the classical domain particles should be treated as fundamentally indistinguishable. This paper shows, on the contrary, how one can recover the thermodynamical account of the entropy of mixing, while treating state...

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Main Author: Janneke van Lith
Format: Article
Language:English
Published: MDPI AG 2018-04-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/20/5/328
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spelling doaj-297d8cab50e0426192880bfaaa491a902020-11-24T23:54:16ZengMDPI AGEntropy1099-43002018-04-0120532810.3390/e20050328e20050328The Gibbs Paradox: Lessons from ThermodynamicsJanneke van Lith0Department of Philosophy and Religious Studies, Utrecht University, Janskerkhof 13, 3512 BL Utrecht, The NetherlandsThe Gibbs paradox in statistical mechanics is often taken to indicate that already in the classical domain particles should be treated as fundamentally indistinguishable. This paper shows, on the contrary, how one can recover the thermodynamical account of the entropy of mixing, while treating states that only differ by permutations of similar particles as distinct. By reference to the orthodox theory of thermodynamics, it is argued that entropy differences are only meaningful if they are related to reversible processes connecting the initial and final state. For mixing processes, this means that processes should be considered in which particle number is allowed to vary. Within the context of statistical mechanics, the Gibbsian grandcanonical ensemble is a suitable device for describing such processes. It is shown how the grandcanonical entropy relates in the appropriate way to changes of other thermodynamical quantities in reversible processes, and how the thermodynamical account of the entropy of mixing is recovered even when treating the particles as distinguishable.http://www.mdpi.com/1099-4300/20/5/328Gibbs paradoxthermodynamicsextensivityfoundations of statistical mechanicsindistinghuishabilityentropy of mixing
collection DOAJ
language English
format Article
sources DOAJ
author Janneke van Lith
spellingShingle Janneke van Lith
The Gibbs Paradox: Lessons from Thermodynamics
Entropy
Gibbs paradox
thermodynamics
extensivity
foundations of statistical mechanics
indistinghuishability
entropy of mixing
author_facet Janneke van Lith
author_sort Janneke van Lith
title The Gibbs Paradox: Lessons from Thermodynamics
title_short The Gibbs Paradox: Lessons from Thermodynamics
title_full The Gibbs Paradox: Lessons from Thermodynamics
title_fullStr The Gibbs Paradox: Lessons from Thermodynamics
title_full_unstemmed The Gibbs Paradox: Lessons from Thermodynamics
title_sort gibbs paradox: lessons from thermodynamics
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2018-04-01
description The Gibbs paradox in statistical mechanics is often taken to indicate that already in the classical domain particles should be treated as fundamentally indistinguishable. This paper shows, on the contrary, how one can recover the thermodynamical account of the entropy of mixing, while treating states that only differ by permutations of similar particles as distinct. By reference to the orthodox theory of thermodynamics, it is argued that entropy differences are only meaningful if they are related to reversible processes connecting the initial and final state. For mixing processes, this means that processes should be considered in which particle number is allowed to vary. Within the context of statistical mechanics, the Gibbsian grandcanonical ensemble is a suitable device for describing such processes. It is shown how the grandcanonical entropy relates in the appropriate way to changes of other thermodynamical quantities in reversible processes, and how the thermodynamical account of the entropy of mixing is recovered even when treating the particles as distinguishable.
topic Gibbs paradox
thermodynamics
extensivity
foundations of statistical mechanics
indistinghuishability
entropy of mixing
url http://www.mdpi.com/1099-4300/20/5/328
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