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EEWR Brown Bag Seminar with Julie Kim and Nathan Li, Graduate Students

Speaker: Julie Kim and Nathan Li, Graduate Students
Series: EEWR Brown Bag Seminars
Location: Engineering Quad E225
Date/Time: Friday, March 15, 2019, 12:00:00 p.m. - 01:00:00 p.m.

Abstract:

Julie Kim: Machine Learning Applications for Mineral Mapping Using Coupled Micro X-ray Diffraction and Fluorescence Data

Julie KimIn the context of subsurface activities such as geologic CO2 sequestration, there is a need for detailed characterization of the mineral heterogeneity. The only method of mineral identification is X-ray Diffraction (XRD), which has limitations in spatial characterization of samples at micron resolutions in a reasonable time. To overcome this challenge, a machine learning approach using Artificial Neural Network (ANN) is developed. We utilize coupled datasets of elemental intensities (from X-ray Fluorescence (XRF) and diffraction patterns (from XRD) to extract and learn from relationships that exist between elemental and mineralogical presence. The data was obtained from a synchrotron source beamline 13-IDE at Advanced Photon Source in Argonne National Laboratory, equipped with coupled XRF-XRD detectors. The ANN trained on the dataset of a heterogeneous shale rock yields low misclassification rates, and minimizes time required for analyses. Finally, additional uses of the coupled dataset such as solid-solution chemistry determination will also be addressed.

Nathan Li: Spectroscopic Parameters for a Laser-based Water Vapor Sensor for UAVs and Climate Change Research

Nathan LiWater vapor is the most important greenhouse gas in the atmosphere. It  has many effects on our climate, one of which is cloud formation. Clouds  reflect sunlight and significantly affect the earth’s radiative balance. Water  vapor has been difficult to measure at the precision necessary to study  clouds because it has a very large dynamic range, 1-40,000 ppm, and  easily adsorbs to instrument surfaces. NASA and other organizations  have been able to deal with these problems using high time-resolution  laser-based sensors. However, many of these instruments were initially  designed to be operated on manned aircraft and are heavy and power-  hungry. I will be presenting the specifications of a lightweight, low-power  laser hygrometer designed to be flown on UAVs. My research focus has  been to measure certain spectroscopic parameters, the air and self-  broadening coefficients and the power-law dependence of temperature,  which are necessary to calibrate our novel laser hygrometer.