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Related Concept Videos

Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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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.
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Entropy02:39

Entropy

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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...
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Gibbs Free Energy02:39

Gibbs Free Energy

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One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

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The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
 
where R is the gas constant (8.314 J/K·mol), T is the absolute temperature in kelvin, and Q is the reaction quotient. This equation may be used to predict the spontaneity of a process under any given set of conditions.
Reaction Quotient...
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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...
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Updated: May 17, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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Maximum entropy inference of reaction-diffusion models.

Olga Movilla Miangolarra1, Asmaa Eldesoukey1, Ander Movilla Miangolarra2

  • 1Department of Mechanical and Aerospace Engineering, University of California, Irvine, California 92697, USA.

The Journal of Chemical Physics
|May 16, 2025
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Summary
This summary is machine-generated.

We developed a new maximum entropy method for reaction-diffusion models. This approach integrates diverse experimental data, improving predictions for biological and chemical systems.

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Area of Science:

  • Complex Systems Modeling
  • Theoretical Physics
  • Mathematical Biology

Background:

  • Reaction-diffusion equations are widely used for modeling complex systems in biology, chemistry, and physics.
  • Current models are often phenomenological, necessitating parameter fitting to experimental data.
  • There is a need for more robust and data-integrative modeling frameworks.

Purpose of the Study:

  • To introduce a novel formalism for constructing reaction-diffusion models based on the principle of maximum entropy.
  • To develop a method capable of incorporating diverse experimental data, including ensemble currents and temporal distributions.
  • To extend the Schrödinger bridges and maximum caliber frameworks to nonlinear interacting systems.

Main Methods:

  • Developed a maximum entropy-based formalism for reaction-diffusion models.
  • Extended the Schrödinger bridges and maximum caliber problem frameworks.
  • Applied the formalism to nonlinear interacting systems.

Main Results:

  • Successfully modeled morphogen evolution in zebrafish fins.
  • Accurately predicted the population dynamics of two toad species in Poland.
  • Demonstrated the ability to incorporate various experimental data types.

Conclusions:

  • The maximum entropy formalism provides a powerful, data-driven approach to reaction-diffusion modeling.
  • This novel method enhances the predictive accuracy of complex system models.
  • The framework is applicable to diverse scientific domains requiring quantitative modeling.