380137 Integrating PVD Modeling, High-Throughput Experimentation and Big Data into the Discovery of Novel High-Temperature Materials

Monday, November 17, 2014
Galleria Exhibit Hall (Hilton Atlanta)
Jason Hattrick-Simpers, Chemical Engineering, University of South Carolina, Columbia, SC, Kenneth Bunn, University of South Carolina, Colu, SC and Christopher Metting, University of South Carolina, Columbia, SC

Integrating PVD Modeling, High-throughput Experimentation and Big Data into the Discovery of New High-Temperature Materials

Jason Hattrick-Simpers, University of South Carolina, Columbia, SC

Jonathan Kenneth Bunn, University of South Carolina, Columbia, SC
Christopher J. Metting, University of South Carolina, Columbia, SC

The identification of new materials and their engineering optimization is a time-intensive process involving the passage of up to 20 years from serendipitous discovery to engineered product. Although the development of high-throughput experimental methods in the early 1990’s originally offered the possibility for revolutionizing novel material exploration, researchers encountered several new problems. High-throughput characterization techniques needed to be developed. Such techniques generated terabytes of diffraction and spectroscopy data, which need to be analyzed and then compared systematically. This problem is amplified in the field of high-temperature materials where precise compositional control is required and material properties must be evaluated during prolonged oxidation cycles.

Here we use the Ni-Al binary alloy system to demonstrate a comprehensive modeling-experimental-data minimization methodology for the combinatorial exploration of novel high-temperature alloys. Modeling of the physical vapor deposition process is used to minimize the trial-and-error efforts associated with the identification of composition regions that maximize effective usage of sample real-estate, avoiding low temperature liquidus regions and undesirable phases. A suite of diffraction and spectroscopy techniques are then implemented to monitor the initial phase of the base metal and oxide and their phase evolution in time during oxidation treatments at 1373 K. The resulting data sets are mined individually using a modified version of the CombiView platform and compared to determine prevailing trends. Finally, time dependent phase diagrams of metal oxidation are created, permitting compositional trends to be evaluated and new materials to be identified.  


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See more of this Session: Interactive Session: Systems and Process Control
See more of this Group/Topical: Computing and Systems Technology Division