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Trends in Mineralogy   Volumes    Volume 1 
Abstract
A new method of prediction of enthalpy of formation of minerals based on crystal structure refinements
Ph. Vieillard
Pages: 281 - 294
Number of pages: 14
Trends in Mineralogy
Volume 1 

Copyright © 1992 Research Trends. All rights reserved

ABSTRACTThe method of prediction of enthalpy of formation is based on the parameter ΔHO2- cation (comp) (Vieillard 1982; Vieillard and Tardy ,1988a). The calculation of this parameter needs the knowledge of cristallographical and optical properties of minerals (refractive index , molecular volume, average distances and shortest bond lengths of cation- oxygens in all sites involved in the compounds). This method has been tested on a great number of minerals and artificial compounds belonging to the system :Li2O-Na2O-K2O-BeO-MgO-CaO-MnO-FeO-Fe2O3-Al2O3-SiO2-H2O. From the initial works on the two oxides minerals (Vieillard & Tardy, 1988a) and extended to three oxides and three sites bearing compounds (Vieillard and Tardy, 1993), it appears that the generalisation of the method of prediction to a great number of inorganic compounds (186) have been conducted by proposing some new improvements in the calculations of ΔH°f,oxides which are the following:

-Distinction of all sites occupied by one or different cations;

-New formulation of the parameter ΔHO2- cation (comp) of a site occupied by two or more than three different cations;

-Detection of non oxygen bridges between two nearest neighbor polyhedra, necessary to the calculation of the enthalpy of formation from the oxides;

-Detection of hydrogen bondings.

178 minerals and artificial compounds belonging to different families such as hydroxides, metasilicates, orthosilicates, pyroxenes, olivines, feldspars, melilites, garnets, aluminates, ferrites, spinels, pyllosilicates, hydrosilicates, amphiboles and zeolites whose crystal refinements and enthalpies of formation are available, have been tested. Their predicted enthalpies of formation are given and compared to the experimental data.

Several explanations are proposed in order to show why the method of prediction works so well.

 

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