The substitution mechanism of Au in In-, Fe- and In-Fe-bearing synthetic crystals of sphalerite, based on the data from EPMA and LA-ICP-MS study

Research subject.Sphalerite (ZnS) is a widespread mineral that can be found in various depositional environments. During formation, this mineral can accumulate minor and trace impurities, with gold being one of the most valuable component. The issue of the chemical state of Au in sphalerite has been much discussed recently.Methods.Samples of In-, Fe- and In-Febearing sphalerite with a composition ranging from 0 to 2.5 mol.% In2S3 and 0 – 40 mol.% FeS were synthesized in an Ausaturated system using gas transport and salt flux techniques. The resulting products were subsequently investigated using EPMA and LA-ICP-MS.Results.All the elements under investigation are found to be homogeneously distributed within the sphalerite matrix. After quenching, sphalerite is shown to retain Au. Our data indicates that the observed increase in Au concentration is caused by the presence of In (up to 1.02 wt % Au) and, to a lesser extent, by that of Fe (up to ≈600 ppm Au). These elements substitute Zn in the crystal structure of sphalerite following the scheme Au+ + In3+(Fe3+) ↔ 2Zn2+, which is in good agreement with previous data obtained using the XAS method.Conclusions.A higher sulphur fugacity in the system leads to a more significant accumulation of Au in sphalerite. The concentration of Au in pure sphalerite does not exceed 10 ppm under our experimental conditions and does not depend on the activity of sulphur in the system.

The geometric blueprint of perovskites

Perovskite minerals form an essential component of the Earth’s mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the “no-rattling” principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.