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Unraveling the Schneeberg garnet puzzle: a numerical model of multiple nucleation and coalescence.

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Title: Unraveling the Schneeberg garnet puzzle: a numerical model of multiple nucleation and coalescence.
Authors: Dobbs, Harvey T.1, Peruzzo, Luca2 luca@dmp.unipd.it, Seno, Flavio1, Spiess, Richard2,3, Prior, David J.4
Source: Contributions to Mineralogy & Petrology. Oct2003, Vol. 146 Issue 1, p1-9. 9p.
Subjects: Chemical kinetics, Micromechanics, Microstructure, Semiconductor doping, Diffusion, Chemical affinity
Abstract: A quantitative kinetic model for the growth of the different garnet porphyroblast microstructures (type 1 and type 2) of the Western Schneeberg Complex (WSC) is presented. These porphyroblasts formed by a multiple nucleation and coalescence mechanism. Our numerical simulation shows that at constant diffusion rates: (1) low interface reaction rates result in a fully amalgamated porphyroblast (type 2); (2) intermediate reaction rates result in a porphyroblast, where coalescence of grains closer to the margin prevented amalgamation of those in the centre (similar to type 1 porphyroblasts); and (3) high interface reaction rates result in a porphyroblast microstructure with an atoll form. All three microstructures are characterised by distinctive cluster size distributions. A 2-D cluster size distribution analysis of type 1 porphyroblasts of WSC shows that these did not form because of intermediate interface reaction rates, but because the diffusion rate of nutrients was too low to keep pace with the interface reaction rate. [ABSTRACT FROM AUTHOR]
Copyright of Contributions to Mineralogy & Petrology 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: <searchLink fieldCode="DE" term="%22Chemical+kinetics%22">Chemical kinetics</searchLink><br /><searchLink fieldCode="DE" term="%22Micromechanics%22">Micromechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Microstructure%22">Microstructure</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+doping%22">Semiconductor doping</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion%22">Diffusion</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+affinity%22">Chemical affinity</searchLink>
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  Data: A quantitative kinetic model for the growth of the different garnet porphyroblast microstructures (type 1 and type 2) of the Western Schneeberg Complex (WSC) is presented. These porphyroblasts formed by a multiple nucleation and coalescence mechanism. Our numerical simulation shows that at constant diffusion rates: (1) low interface reaction rates result in a fully amalgamated porphyroblast (type 2); (2) intermediate reaction rates result in a porphyroblast, where coalescence of grains closer to the margin prevented amalgamation of those in the centre (similar to type 1 porphyroblasts); and (3) high interface reaction rates result in a porphyroblast microstructure with an atoll form. All three microstructures are characterised by distinctive cluster size distributions. A 2-D cluster size distribution analysis of type 1 porphyroblasts of WSC shows that these did not form because of intermediate interface reaction rates, but because the diffusion rate of nutrients was too low to keep pace with the interface reaction rate. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Contributions to Mineralogy & Petrology 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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