University of Tennessee at Chattanooga UTC Scholar Student Research, Creative Works, and Honors Theses Publications 5-2021 Understanding shear thinning using Brownian dynamics simulation Mackenzie Nicole Wall University of Tennessee at Chattanooga,
[email protected] Follow this and additional works at: https://scholar.utc.edu/honors-theses Part of the Physical Chemistry Commons Recommended Citation Wall, Mackenzie Nicole, "Understanding shear thinning using Brownian dynamics simulation" (2021). Honors Theses. This Theses is brought to you for free and open access by the Student Research, Creative Works, and Publications at UTC Scholar. It has been accepted for inclusion in Honors Theses by an authorized administrator of UTC Scholar. For more information, please contact
[email protected]. 1 Understanding Shear Thinning Using Brownian Dynamics Simulation Mackenzie Nicole Wall Departmental Honors Thesis University of Tennessee at Chattanooga Department of Chemistry and Physics Examination Date: 23 March 2021 Dr. Luis Sanchez-Diaz Dr. John Lee Lecturer of Physics Associate Professor of Chemistry Project Director Department Examiner 2 Abstract: In this work, we study the changes in structure during the shear thinning regime using Brownian Dynamics with a simple steady-state shear flow of binary charged colloidal suspension. Previous research has analyzed the viscosity, radial distribution, elasticity and plasticity of materials with rheo-SANS experimentation; however, less research has been conducted to replicate the experiment through computer simulations. With Brownian Dynamic Simulation, this study was able to reproduce the results obtained in a recent rheo-SANS experiment and it also explored the viscosity, radial distribution, elastic and plastic behavior of a system under different parameters. The comparison of simulated data with experimental data revealed the computer simulation to successfully generate results indicative of shear thinning behavior in both the viscosity versus shear data as well as the radial distribution data.