The structure and composition of olivine grain boundaries: 40 years of studies, status and current developments
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269_2017_935_ReferencePDF.pdf
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14.94 MB
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a086020cf0c2ed360bf3ecb68de3dc42
Author(s)
Marquardt, K.
Faul, U. H
Date Issued
January 30, 2018
Publisher
Springer Berlin Heidelberg
Version
Author's final manuscript
Abstract
Abstract
Interfaces in rocks, especially grain boundaries in olivine dominated rocks, have been subject to about 40 years of studies. The grain boundary structure to property relation is fundamental to understand the diverging properties of polycrystalline samples compared to those of single crystals. The number of direct structural observations is small, i.e. in range of 100 micrographs, and the number of measurements of properties directly linked to structural observations is even smaller. Bulk aggregate properties, such as seismic attenuation, rheology and electrical conductivity, are sensitive to grain size, and seem to show influences by grain boundary character distributions. In this context we review previous studies on grain boundary structure and composition and plausible relations to bulk properties. The grain boundary geometry is described using five independent parameters; generally, their structural width ranges between 0.4–1.2 nm and the commonly used 1 nm seems a good approximation. This region of enhanced disorder is often enriched in elements that are incompatible in the perfect crystal lattice. The chemical composition of grain boundaries depends on the bulk rock composition. We determined the 5 parameter grain boundary character distribution (GBCD) for polycrystaline Fo
$$_{90}$$
90
and studied structure and chemistry at the nm-scale to extend previous measurements. We find that grain boundary planes close to perpendicular to the crystallographic c-direction dominate the grain boundary network. We conclude that linking grain boundary structure in its full geometric parameter space to variations of bulk rock properties is now possible by GBCD determination using EBSD mapping and statistical analyses.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1007/s00269-017-0935-9
