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Computational Design of Photopolymerized Materials


The Biomaterials Group develops advanced measurement capabilities and measurement assurance strategies for characterizing biomaterials, biological systems, and their interactions. Our work aims to provide quantitative measurement and prediction of chemical, physical, structural, and mechanical properties on multiple length and time scales. This particular project develops new experimental and computational methods to gain unprecedented quantitative insight into photopolymerization processes and the properties of resultant materials. Our efforts include: (1) Simulation of network growth in crosslinked photopolymers, (2) Multiparametric analysis of network growth process via combined experimental and computational approaches, and (3) Modeling photon absorption in composite resin systems.



Photopolymerization represents a versatile method that affords rapid and robust manufacturing of polymers and composites. The properties of polymerized materials can be controlled by the chemistry and processing parameters, as well as novel molecular mechanisms. As such, photopolymerization has found wide usage in structural and coating applications, dental and biomedical restoratives, lithography, and more recently in additive manufacturing such as 3D printing. The chemical composition, reaction kinetics, and other processing parameters interact in complex ways to affect the properties of the polymerized materials.  Methods to understand and predict material properties would greatly advance the design and function of new materials. This program combines experiments and simulation to gain unprecedented quantitative insight into photopolymerization processes and the properties of resultant materials. The methods developed in this program will benefit both the industrial and the academic sectors seeking to innovate manufacturing using photopolymerization.


Created October 24, 2017, Updated September 25, 2018