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Formal Determination of the Nucleation Probabilities in Binary Alloys: A Hyperfinite Approach and Thermodynamic Justification of the Lattice Models.

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Title: Formal Determination of the Nucleation Probabilities in Binary Alloys: A Hyperfinite Approach and Thermodynamic Justification of the Lattice Models.
Authors: Krasnyuk, I. B., Yurchenko, V. M.
Source: Journal of Experimental & Theoretical Physics. Mar2002, Vol. 94 Issue 3, p545. 7p.
Subjects: Binary metallic systems, Burgers' equation
Abstract: We obtained an equation of the Burgers type modeling the glass transition process in binary alloys with inhomogeneous inclusions. The proposed equation is thermodynamically justified; conditions are indicated under which this equation converts into the classical Cahn-Hillard equation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental & Theoretical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We obtained an equation of the Burgers type modeling the glass transition process in binary alloys with inhomogeneous inclusions. The proposed equation is thermodynamically justified; conditions are indicated under which this equation converts into the classical Cahn-Hillard equation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental & Theoretical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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