Shahani receives NSF CAREER Award

Assistant Professor Ashwin Shahani wins NSF CAREER Award for research in eutectic solidification.
Shahani receives NSF CAREER Award

CONGRATULATIONS to Assistant Professor Ashwin Shahani and his research team for recently securing an NSF CAREER Award from the Division of Materials Research (DMR). Shahani received the award for his proposal: “Microstructure Formation in Chemically-Modified Eutectics: Bridging Real-Time Imaging, Machine Learning, and Problem-Based Instruction.”

“The applicant pool is becoming stronger and stronger,” Shahani said, “so I am very grateful that the NSF has deemed my research worthy of funding.”

Shahani’s research, detailed below, is in eutectic solidification. As Shahani explains: “Most of our theories for eutectic solidification rely on simple two-component systems, such as aluminum (Al)-silicon (Si) alloys. How do we generalize microstructure formation to multicomponent eutectics with more than two components? The goal of our research is to bring us closer to understanding alloys of technological importance which involve a cocktail of metallic elements besides Al and Si.” 

With the NSF CAREER Award, Shahani can now hire more students at the Ph.D. level to carry out the research. The award also supports the development of an outreach solidification science program dedicated to engaging underrepresented middle school students, in partnership with the Detroit Area Pre-College Engineering Program (DAPCEP).


Non-Technical Research Summary 

Patterns in nature typically form when a system changes from one phase of matter to another - for example, from a liquid phase to a geometrically patterned solid phase during solidification. The resulting patterns resemble a labyrinth of streets in a metropolitan area, although they are approximately one-hundred million times smaller and in three dimensions, called crystals. The structures of the as solidified crystals arranged in the pattern closely relate to the properties of the material, even after subsequent processing steps. To our advantage, the shapes and sizes of these crystals can be tailored to meet technological demands by manipulating the chemical composition of the parent liquid phase. For instance, trace metal impurities dissolved in the liquid are known to transform solid silicon (Si) from coarse, blocky particles into fine, web-like fibers. This results in a dramatic enhancement of the strength and ductility of the Si-based alloy and expands its potential for new applications, including space-frames and electric vehicles. The objective of this CAREER award is to understand how and why such transformations occur in the presence of trace metal impurities, by harnessing one of the brightest sources of hard X rays in the world at Argonne National Laboratory. The incident X-radiation can penetrate through an otherwise opaque metal, allowing one to capture the details of solidification in real time. Following the experiments, the PI and his team will extract information from the time lapse videos of solidification using state-of-the-art machine learning algorithms. It is anticipated that their new vision will advance the field of alloy solidification from metallurgical alchemy to predictive science. Ultimately, understanding the evolution of solid patterns during synthesis is the key to controlling the manufacture of advanced materials from the bottom-up. The new discoveries generated by this program will be integrated into problem-based learning units for underrepresented middle school students, in partnership with the Detroit Area Pre-College Engineering Program (DAPCEP). The PI will assess the impact of these activities using annual and multi-year surveys distributed through the DAPCEP organization.