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Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Entropy01:18

Entropy

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.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
The Entropy as a State Function01:14

The Entropy as a State Function

Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
The Phase Rule01:20

The Phase Rule

The phase rule describes the relationship between the variance (degrees of freedom), the number of components, and the number of phases in a system at equilibrium.Variance is a concept that denotes the number of independent intensive properties (properties are those that do not depend on the amount of material in the system), such as temperature, pressure, and composition, that can be altered without impacting the number of phases in equilibrium.In a single-component system, such as pure water,...

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Related Experiment Video

Updated: May 18, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Entropy production in full phase space for continuous stochastic dynamics.

Richard E Spinney1, Ian J Ford

  • 1Department of Physics and Astronomy, UCL, Gower Street, London WC1E 6BT, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
PubMed
Summary

This study analyzes total entropy production in continuous Markovian dynamics. Two components are always positive on average, but the third is not uniquely linked to irreversibility or nonequilibrium constraints.

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Last Updated: May 18, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Thermodynamics
  • Physical Chemistry

Background:

  • Entropy production quantifies irreversibility in dynamic systems.
  • Understanding entropy production is crucial for non-equilibrium processes.
  • Fluctuation theorems provide insights into the statistical behavior of entropy production.

Purpose of the Study:

  • To analyze total entropy production and its components in continuous Markovian dynamics.
  • To investigate the properties of constituent quantities of entropy production, particularly their positivity and relation to irreversibility.
  • To explore these concepts using examples of heat conduction and drift diffusion.

Main Methods:

  • Stochastic differential equations with multiplicative noise were used to model system dynamics.
  • Analysis was performed in the context of continuous Markovian dynamics in full phase space.
  • Integral fluctuation theorems were applied to constituent quantities of entropy production.

Main Results:

  • Two constituent quantities of total entropy production were shown to be rigorously positive on average.
  • The third constituent quantity does not consistently obey integral fluctuation theorems.
  • The third quantity's association with irreversibility and detailed balance breakage is not unique.

Conclusions:

  • The study clarifies the behavior of different components of entropy production.
  • It highlights limitations in uniquely associating certain entropy production components with irreversibility.
  • The findings are illustrated through models of heat conduction and Brownian particle dynamics.