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Related Concept Videos

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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The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
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Energy Conservation and Bernoulli's Equation01:16

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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
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Converting work to heat is an irreversible process, and the purpose of a heat engine is to reverse the effect partially. Heat engines aim to increase the efficiency of the reversal, that is, maximize the work retrieved from heat. If the efficiency of a heat engine were 100%, it would imply reversing the process completely without introducing any other effect. Thus, it would violate the second law of thermodynamics.
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Related Experiment Videos

Thermodynamically consistent machine learning model for excess Gibbs energy.

Marco Hoffmann1, Thomas Specht1, Quirin Göttl2

  • 1Laboratory of Engineering Thermodynamics, RPTU University Kaiserslautern-Landau, Kaiserslautern, Germany.

Nature Communications
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

HANNA, a machine learning model, accurately predicts excess Gibbs energy for liquid mixtures by integrating physical laws. This thermodynamically consistent approach expands applicability beyond current methods.

Related Experiment Videos

Area of Science:

  • Thermodynamics
  • Chemical Engineering
  • Machine Learning

Background:

  • Excess Gibbs energy is crucial for modeling thermodynamic properties of liquid mixtures.
  • Predicting this property for multi-component mixtures from molecular structures remains a significant challenge.

Purpose of the Study:

  • To develop a machine learning model, HANNA, for predicting excess Gibbs energy.
  • To ensure thermodynamically consistent predictions by integrating physical laws as hard constraints.

Main Methods:

  • Developed HANNA, a flexible machine learning model.
  • Trained HANNA on experimental data for vapor-liquid equilibria, liquid-liquid equilibria, activity coefficients at infinite dilution, and excess enthalpies.
  • Utilized a surrogate solver for end-to-end training on liquid-liquid equilibrium data.
  • Employed a geometric projection method for extrapolation to multi-component mixtures.

Main Results:

  • HANNA provides accurate predictions of excess Gibbs energy.
  • The model demonstrates a substantially broader domain of applicability compared to state-of-the-art methods.
  • Thermodynamically consistent predictions are guaranteed.

Conclusions:

  • HANNA successfully addresses the challenge of predicting excess Gibbs energy for multi-component mixtures.
  • The model offers improved accuracy and applicability in thermodynamic property prediction.
  • Open-source code and an interactive interface are available for broader use.