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Related Concept Videos

Entropy02:39

Entropy

34.6K
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...
34.6K
Entropy01:18

Entropy

3.4K
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...
3.4K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

6.5K
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...
6.5K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.6K
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...
4.6K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

23.8K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
23.8K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.1K
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.
3.1K

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Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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Inferring Entropy Production from Short Experiments.

Sreekanth K Manikandan1, Deepak Gupta2, Supriya Krishnamurthy1

  • 1Department of Physics, Stockholm University, SE-10691 Stockholm, Sweden.

Physical Review Letters
|April 14, 2020
PubMed
Summary
This summary is machine-generated.

We present a new method to precisely measure entropy production in steady-state systems using current fluctuations. This approach requires minimal experimental data, simplifying the analysis of nonequilibrium thermodynamics.

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Area of Science:

  • Statistical mechanics
  • Non-equilibrium thermodynamics
  • Physical chemistry

Background:

  • Understanding entropy production is crucial for characterizing the efficiency and behavior of thermodynamic systems operating out of equilibrium.
  • Current methods for measuring entropy production often require extensive data or complex experimental setups.

Purpose of the Study:

  • To develop a strategy for the exact inference of average entropy production and its fluctuations in nonequilibrium steady states.
  • To enable these measurements using arbitrary current fluctuations and minimal experimental data.

Main Methods:

  • Utilizing a finite-time generalization of the thermodynamic uncertainty relation.
  • Analyzing arbitrary current fluctuations from experimental measurements.
  • Applying the method to exact and numerical solutions for colloidal heat engines.

Main Results:

  • A strategy for exact inference of entropy production (average and fluctuations) in nonequilibrium steady states.
  • The method's requirement for only short time series data, reducing experimental burden.
  • Successful illustration using colloidal heat engine models.

Conclusions:

  • The proposed strategy offers a powerful and efficient tool for analyzing nonequilibrium systems.
  • This approach significantly simplifies the experimental determination of entropy production.
  • The findings have broad implications for the study of thermodynamics in small systems and beyond.