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Entropy Change in Reversible Processes

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.

Entropy

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...

Entropy and the Second Law of Thermodynamics

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...

Second Law of Thermodynamics

Second Law of Thermodynamics

In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic...

The Second Law of Thermodynamics

The Second Law of Thermodynamics

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...

Entropy and Solvation

Entropy and Solvation

The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...

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Related Experiment Video

Updated: Jul 30, 2025

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

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Published on: December 4, 2017

Conditional Entropic Approach to Nonequilibrium Complex Systems with Weak Fluctuation Correlation.

Yuichi Itto^1,2

¹Science Division, Center for General Education, Aichi Institute of Technology, Toyota 470-0392, Aichi, Japan.

Entropy (Basel, Switzerland)

|May 16, 2023

Summary

This summary is machine-generated.

This study introduces a conditional entropic approach for complex systems. It reveals that weak correlations in fluctuations maximize entropy, explaining diffusion processes in biological systems.

Keywords:

conditional entropy diffusion in living cells weak correlation

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Area of Science:

Non-equilibrium thermodynamics
Statistical mechanics
Complex systems analysis

Background:

Understanding the behavior of complex systems far from equilibrium is challenging.
Spatiotemporal fluctuations in these systems often exhibit weak correlations over large time scales.
Characterizing these fluctuations is crucial for modeling dynamic processes.

Purpose of the Study:

To develop a conditional entropic approach for analyzing non-equilibrium complex systems.
To investigate the role of weak correlations in fluctuation distributions.
To provide a framework for understanding diffusion phenomena in biological contexts.

Main Methods:

Application of conditional entropy principles.
Analysis of fluctuation distributions in systems with weak correlations.
Modeling of diffusion processes using the developed entropic approach.

Main Results:

Identified that weak correlations in spatiotemporal fluctuations maximize conditional entropy.
Demonstrated the approach's efficacy in explaining protein diffusion within bacteria.
Showcased potential applications in modeling membraneless organelles and cellular components.

Conclusions:

The conditional entropic approach offers a novel perspective on non-equilibrium systems.
Weak correlations play a key role in maximizing entropy and governing diffusion.
The framework is applicable to diverse biological diffusion phenomena.