Rebecca K. Lindsey

Carbon nanoparticles (CNPs) are of tremendous interest for clean-energy technology due to the manifold of chemical, mechanical, electronic, and optoelectronic properties they can exhibit. However, practical application of these materials is hampered by current synthesis strategies. Low-pressure techniques such as chemical vapor deposition and flame pyrolysis are well understood but yield material with relatively low throughput. Conversely, high-pressure methods like ultrasound cavitation and detonation can greatly enhance throughput (e.g., enabling rates of up to kgs/μs), but the underlying phenomena are not well understood due to the highly dynamic nature of these processes, hampering tunability.

In this presentation, we discuss recent efforts to bridge this gap through development of a new shockwave-based technique that enables rapid CNP synthesis and enhanced tunability over pre-existing high-pressure synthesis methods. A robust understanding of phenomena underlying material synthesis is critical for enabling tunability. Hence, we also discuss how we deploy ChIMES, our unique machine-learning-enhanced atomic-resolution simulation technique to simulate this synthesis process on scales overlapping with experiments. Ultimately, we show that our simulations provide a previously missing means of elucidating the complicated condensed-phase reaction driven phase separation and transformation processes that underly CNP shockwave synthesis.

Dr. Lindsey is an Assistant Professor of Chemical Engineering and by courtesy, of Applied Physics, Nuclear Engineering and Radiological Sciences, and Materials Science at the University of Michigan (UM). Prior to joining UM, Dr. Lindsey earned her B.S. in Chemical Engineering from Wayne State University and her M.S. and Ph.D. in Chemical Physics from the University of Minnesota, Twin Cities. Following, she worked as a postdoctoral scholar at Lawrence Livermore National Laboratory (LLNL), where she later converted to staff, leading a variety of research teams within the LLNL Energetic Materials Center. Her work in computational chemistry, for which applications have spanned sorption in soft materials, possible mechanisms for the origins of life, detonation synthesis of unusual carbon nanoparticles, and more, has been underpinned by a strong interest in developing tools enabling work in previously inaccessible problem spaces. In addition to her work in computational chemistry, she leverages data science and machine learning to aid in interpretation of large experimental datasets and to develop material performance models from them. Her research has been recognized by a number of awards, the most recent of which include the 2025 American Physical Society Neil Ashcroft Early Career Award for Studies of Matter at Extreme High Pressure Conditions and the 2024 American Institute of Chemical Engineering Computational Molecular Science and Engineering Forum Young Investigator Award.

• Time: 11:00 AM
• Location: 206 Furnas Hall