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It was once thought either impossible or inefficient to photopolymerize a thickness greater than a thin film because of the optical attenuation of light into the depth of the sample. However, if several considerations are allowed, it is indeed possible. Three particular modifications are essential to enhance light penetration into the depth of the system. An initiator that absorbs in a region of the spectrum where no other components absorb maximizes the incident light intensity for photolysis of the initiator. Concentration and/or molar absorptivity of the initiator lower than typically used in thin films enhance light penetration. Finally, photobleaching initiators exhibit decreased absorbance upon photolysis and thus allow light to penetrate more deeply into the system with time.
A need to model these systems is born out of the desirability to use light to initiate polymerizations of all sorts, including thicker systems. In this project, a set of differential equations describing the spatial and temporal evolution of the light intensity gradient, photoinitiator concentration gradient, and the photoinitiation rate profile are developed for a thick polymer system. The generalized model accounts for the consumption of initiator, evolution of the products of photolysis, diffusion of the initiator and photolysis products, and absorbance by all system components. The purpose of these studies was to characterize further these systems so that results accurately capture the photoinitiation process. Several key objectives have been accomplished, including the effects of illumination with polychromatic incident light, various illumination schemes, and verification of the predicative ability of the model.
The ultimate goal of this project was two fold; first, to build a tool that models photopolymerization systems well, and second, to develop a means for choosing reaction components for photopolymerization applications. To understand and predict how these systems work contributes significantly to the photopolymerization field because it allows the user to predict system behavior accurately and to choose system components appropriate for a particular application.