Date/Time
Date(s) - 03/07/2023
3:00 pm - 4:00 pm
Location
Rhines Hall 125
Categories
Kerri-lee Chintersingh, Ph.D.
Assistant Professor, Chemical and Materials Engineering
New Jersey Institute of Technology
Dr. Kerri-lee Chintersingh is an Assistant Professor within the Otto H. York Department of Chemical and Materials Engineering at the New Jersey Institute of Technology. After completing her doctoral degree in Chemical Engineering at NJIT, Dr. Chintersingh joined the Hopkins Extreme Materials Institute and Department of Materials Science and Engineering at the Johns Hopkins University (JHU) as a Postdoctoral Research Fellow in the Weihs group. She is chemical engineering undergraduate alum of the University of Technology, Jamaica, and has served as Process Control Engineer at a alumina refinery.
Currently, her research focuses on tuning nanostructured metals, alloys and composites as powders or varied architecture for combustion, energy, and biomedical applications. She is also interested in utilizing machine learning to extract data from complex and extreme environments and to optimize reaction processes and material design. This effort has led to several peer- reviewed publications and multiple research awards. Outside of research, Dr. Chintersingh is a member of the Diversity, Equity and Inclusion Committee within her department and participates in outreach activities to stimulate STEM-based careers, particularly in middle and high school girls. She is also a member of the Society of Women Engineers and the American Institute of Chemical Engineers.
Abstract
Metals powders like aluminum and boron are attractive potential fuel additives for pyrotechnics, propellants and explosives due to their high energy release upon oxidation. However, they tend to agglomerate, have lengthy ignition delays, and low combustion rates/efficiencies. This study therefore aims to design and test metal powders with tuned microstructure or chemistries to mitigate these challenges and favor the formation of desired products; without jeopardizing thermochemical performance, safety, and stability.
One approach used is to incorporate elements that can form exothermic intermetallics (like Zr for Al). Ball-milling these elements form nanocomposite powders with lower ignition thresholds, improved combustion efficiencies and lead to dual- phase combustion. Novel experimental and diagnostic tools like x-ray phase contrast imaging (XPCI) and snapshot hyper-spectral imager for emissions and reactions (SHEAR) have been coupled to capture condensed phase/internal particle features and external optical emissions, temperatures, and gas phase species, respectively.
Machine learning is used to obtain quantitative data: identify trends, detect anomalies and classify particle events from the videos produced of combustion scenes. Other approaches for tuning metals include modifying boron particle surface by washing with hydrocarbon solvents or introducing transition metals like Fe as an oxygen “shuttle catalysts” to accelerate boron’s heterogeneous surface reactions. Results show that washing boron reduces ignition delays by reducing the oxide layer present and doping boron with as low as 1wt% Fe improves surface reaction rates.