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Related Concept Videos

Entropy02:39

Entropy

34.7K
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...
34.7K
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
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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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...
4.7K
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
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

6.6K
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...
6.6K
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

21.4K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
21.4K

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Universal and Accessible Entropy Estimation Using a Compression Algorithm.

Ram Avinery1, Micha Kornreich1, Roy Beck1

  • 1The Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.

Physical Review Letters
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

A new universal method uses lossless compression to accurately calculate entropy in complex systems. This computationally effective approach enhances the study of molecular dynamics and free-energy calculations.

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Area of Science:

  • Thermodynamics and statistical mechanics
  • Computational chemistry and physics
  • Biophysics and structural biology

Background:

  • Entropy and free-energy estimation are crucial for characterizing diverse simulated systems.
  • Existing methods are often model-specific, computationally expensive, and require simulations under non-ideal conditions.

Purpose of the Study:

  • To develop a universal and computationally efficient scheme for entropy calculation.
  • To validate the new method across systems of increasing complexity.
  • To demonstrate its application in detecting subtle dynamics in protein folding.

Main Methods:

  • Utilizing lossless-compression algorithms for entropy estimation.
  • Applying the method to various simulated systems, including spin models, polymers, and colloids.
  • Validating results against benchmark calculations and analyzing molecular dynamics simulations.

Main Results:

  • The proposed scheme accurately calculates entropy, comparable to established methods.
  • It proves computationally effective, reducing resource requirements.
  • Unprecedented detection of entropy fluctuations in protein folding simulations was achieved, revealing folded states.

Conclusions:

  • This universal entropy calculation scheme offers a powerful and efficient tool for thermodynamic characterization.
  • It provides new insights into the dynamics of complex systems, particularly in molecular simulations.
  • The method facilitates more efficient free-energy calculations, advancing fields like drug design.