Multicomponent diffusion in a basaltic melt: Temperature dependence

Abstract

Eighteen successful diffusion couple experiments in 8-component SiO2–TiO2–Al2O3–FeO–MgO–CaO–Na2O–K2O basaltic melts were conducted at 1260 °C and 0.5 GPa and at 1500 °C and 1.0 GPa. These experiments are combined with previous data at 1350 °C and 1.0 GPa (Guo and Zhang, 2018) to study the temperature dependence of multicomponent diffusion in basaltic melts. Effective binary diffusion coefficients of components with monotonic diffusion profiles were extracted and show a strong dependence on their counter-diffusing component even though the average (or interface) compositions are the same. The diffusion matrix at 1260 °C was obtained by simultaneously fitting diffusion profiles of all diffusion couple experiments as well as appropriate data from the literature. All features of concentration profiles in both diffusion couples and mineral dissolution are well reproduced by this new diffusion matrix. At 1500 °C, only diffusion couple experiments are used to obtain the diffusion matrix. Eigenvectors of the diffusion matrix are used to discuss the diffusion (exchange) mechanism, and eigenvalues characterize the diffusion rate. Diffusion mechanisms at both 1260 and 1500 °C are inferred from eigenvectors of diffusion matrices and compared with those at 1350 °C reported in Guo and Zhang (2018). There is indication that diffusion eigenvectors in basaltic melts do not depend much on temperature, but complexity is present for some eigenvectors. The two slowest eigenvectors involve the exchange of SiO2 and/or Al2O3 with nonalkalis. The third slowest eigenvector is due to the exchange of divalent oxides with other oxides. The fastest eigenvector is due to the exchange of Na2O with other oxide components. Some eigenvalues differ from each other by <1/3, and their eigenvectors are less well defined. We define small difference in eigenvalues as near degeneracy. In strict mathematical degeneracy when two eigenvalues are identical, eigenvectors are not uniquely defined because any linear combination of two eigenvectors is also an eigenvector. In the case of near degeneracy, more constraints either in terms of higher data quality or more experiments are needed to resolve the eigenvectors. We made a trial effort to generate a set of average eigenvectors, which are assumed to be constant as temperature varies. The corresponding eigenvalues are roughly Arrhenian. Thus, the temperature-dependent diffusion matrix can be roughly predicted. The method is applied to predict experimental diffusion profiles in basaltic melts during olivine and anorthite dissolution at ~1400 °C with preliminary success. We further applied our diffusion matrix to investigate multicomponent diffusion during magma mixing in the Bushveld Complex and found such diffusion may result in an increased likelihood of sulfide and FeTi oxide mineralization.