Lilly Schroer

Advisor: Prof. Christopher Muhlstein 

will defend a master’s 

Experimental Framework for Characterizing the Network Structure of Thermosetting Polymers using Strain Field Mining

On

Thursday, July 11th at 2:30 p.m.
MRDC Room 4404

and

 Virtually via MS Teams 

Link

Committee
            Prof. Christopher Muhlstein – School of Materials Science and Engineering (advisor)

            Prof. Rick Neu – School of Materials Science and Engineering 
            Prof. Donggang Yao – School of Materials Science and Engineering 

Abstract

Thermosetting polymers are heterogeneous, cross-linked, network structured materials, with mechanical properties that depend on processing conditions and final microstructure. The final microstructure is dependent on the evolution of the network during cure, and its innate heterogeneity is directly correlated to non-affine deformation of the network. Defining a constitutive relationship for cross-linked networks has remained a challenge because structural heterogeneity has not been observed on large length scales (> 100 μm) by current characterization techniques. Thus, it is assumed that characteristic length scales of the network are on the order of hundreds of nanometers, and a large enough length scale exists for it to be assumed that the network is ideal and the bulk mechanical properties are uniform. The limitations of current characterization techniques present a critical barrier to establishing structure-property relationships of thermosets and advancing the engineering of their mechanical performance. In this thesis, I present an approach for understanding the processing-structure-property relationships of thermosets using reaction kinetics models and digital image correlation (DIC) with strain field mining. The structure of this thesis follows a general experimental method that I propose can be used to engineer and characterize the network architecture for any thermosetting material.