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Collaborative Research: Refining Geothermobarometry in Pyroxenes using In Situ Measurements of Fe3+

$172,000FY2018GEONSF

Mount Holyoke College, South Hadley MA

Investigators

Abstract

This project promotes the progress of science by providing calibrations for use at a national facility, the Advanced Photon Source at Argonne National Laboratory; these results will also be useful for users of other synchrotron facilities worldwide. The calibrations address the scientific question of how much oxygen was present when a mineral crystallized by analyzing a proxy, which is the oxidation state of the multivalent element iron. This understanding has vital importance for understanding how oxygen controls the crystallization path and composition of cooling magmas, also providing insight into processes that may have operated on and in the magma as it moved to the surface. Graduate and undergraduate students at Mount Holyoke College, the University of Tennessee at Knoxville, and the University of Idaho will be supported by this project, including at least three women. The ability to measure redox states at sub-nm and pm scales is a formidable analytical challenges due to the inherent anisotropy of most rock-forming minerals, which causes their crystal structures to interact with photons differently according to crystal orientation. This project studies oriented single crystals to explore the effects of crystallographic orientation on x-ray absorption spectroscopy measurements, focusing on the pyroxene mineral group, one of the most common phases in igneous rocks. The team will use these data to build a universal calibration appropriate for quantifying the partial pressure of oxygen (oxygen fugacity) in pyroxene-bearing samples. This work has the potential to advance knowledge in multiple geological disciplines. It will also enable synchrotrons around the world that use the Athena software package to quickly and easily determine Fe3+ contents of pyroxene minerals with known precision and accuracy. Finally, this project applies machine learning techniques to the study of spectroscopic data. Development of this methodology is exportable to other fields, such as medical, biological, and forensic uses of spectroscopy, where it has the potential for societal impact. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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