Photopolymerization of multicomponent mixtures is widely used in industry to create a wide range of material properties for a variety of applications including microelectronics, contact lenses, dental restorations, adhesives and coatings. Under typical photopolymerization conditions, crosslinking polymerization exhibit non-classical phenomena such as autoacceleration, autodeceleration, incomplete conversion, and reaction diffusion controlled termination. These non-classical behaviors present difficult challenges to predicting network properties over various polymerization conditions. However, the complex photopolymerization reaction that generates these properties is modulated by the variety of conditions that these materials are subjected to both during and after the photopolymerization.

A design of experiments approach limits the number of systems that are required for analysis, but a significant fraction of photopolymer analysis techniques are time consuming and the conclusions are limited to the region analyzed. This process creates a large bottleneck in the ability to optimize formulations and understand the effects that develop such advantageous properties. The wide spectrum of monomer chemistries add to this complexity and hinder the efficient optimization of various control parameters to produce a resulting polymer with the suitable material properties.

In this work, high-throughput techniques for analyzing photopolymer conversion and modeling copolymerizations were developed. Controllable gradients of significant properties in photopolymerization were employed to produce a two factor system with all available analysis conditions on a single substrate. Gradients of composition, light intensity, exposure time, and temperature were all developed to produce combinatorial samples that were subsequently analyzed for their conversion. This technique allows for the rapid assessment of conversion over the entire substrate, generating a large set of data in a relatively short period of time.

A copolymerization model was also developed to leverage these large data sets and estimate fundamental kinetic constants of the monomers analyzed. These kinetic constants model conversion at a specified composition and exposure time, which is then combined with estimations of other material properties to produce a large data set of polymer properties as a function of composition and conversion. With this data set, the compositions which meet the specified properties are limited into more manageable regions, reducing the potential experiments required for further analysis.