Date: Wednesday, April 10, 2024
Time: 9:30-10:30AM
Location: Health Science Technology Building (HST), Forum Room 101
This event features Antonia Statt, who will talk about "Self-Assembly of Non-Spherical Colloids using Coarse-grained Simulations and Machine Learning Approaches", as part of the Lehigh University Chemical and Biomolecular Engineering's Spring 2024 Colloquium Seminar Series.
Abstract
Modern synthesis methods have enabled the fabrication of colloids with various different shapes. In addition, plastic waste degrades to nano- and microplastics particles in the environment, also leading to irregular shaped colloidal particles. These particles, polluting our habitats have unknown implications for our health and habitats. Therefore, understanding self-assembly of these non-spherical particles
crucial not only for many applications, like bottom-upfabrication of large ordered systems, but also for understanding the fate of microplastic particles in the aquaticenvironment.
We utilize efficient, large-scale molecular dynamics simulations of coarse-grained models to investigate hownon-spherical colloids aggregate. We find that in heterogaggregation of microplastics with other organic matter, smoothround shapes formed compact structures with large number of neighbors with weak connection between adjacent shapes.Microplastics with sharper edges and corners aggregated into more fractal structures with fewer neighbors, but withstronger connections. When investigating their behavior under shear, the critical shear rate at which the aggregates breakup is much larger for spherical and rounded cube microplastics, and surprisingly, aggregates made of roundedcubes exhibited unexpectedly high stability under shear.
To simulate the non-shperical colloids, we utilized the commonly used composite bead approach to represent thedifferent shapes. We find that this approach is not only expensive as the complexity of the colloidal shape increases, dueto inter-body distance calculations, but also cumbersome as it is not always obvious how the composite beads shouldinteract with each other. Here, I will highlight the use of data-driven methods to accelerate our simulations. We trainedneural networks to directly predict energy, forces, and torques between rigid non-spherical particles based on theirconfiguration, bypassing the need for inter-bead distance calculations. This approach can yield significant computationalspeedup and can be applied to irregular shapes with any pair interaction, given sufficient training data.
About the Speaker
Antonia Statt is an Assistant Professor in the Materials Science and Engineering Department at the University of Illinois in Urbana-Champaign since November 2019. Prior to that, she was a postdoctoral fellow at the Princeton Center for Complex Materials, where she worked in the lab of Prof. Athanassios Z. Panagiotopoulos in the CBE Department. Sheobtained her PhD in the lab of Prof. Kurt Binder in Physics at the University of Mainz in Germany. Prof. Statt has receivedrecognition for her research, including the AIChE CoMSEF Young Investigator award, the ACS PRF Doctoral New Investigator Award, and the NSF CAREER award. Her research is focused on utilizing coarse-grained computationalmodels to further the fundamental understanding of soft matter for applications in energy and medicine. Of special interestare non-equilibrium phenomena like deformation, evaporation, and flow, as well as self-assembly and phase transitions.
Department/Program:
