In-situ Monitoring of Photopolymerization Using Microrheology

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Slopek, Ryan Patrick
Breedveld, Victor
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Photopolymerization is the basis of several multi-million dollar industries including films and coating, inks, adhesives, fiber optics, and biomaterials. The fundamentals of the photopolymerization process, however, are not well understood. As a result, spatial variations of photopolymerization impose significant limitations on applications in which a high spatial resolution is required. To address these issues, microrheology was implemented to study the spatial and temporal effects of free-radical photopolymerization. In this work a photosensitive, acrylate resin was exposed to ultraviolet light, while the Brownian motion of micron sized, inert fluorescent tracer particles was tracked using optical videomicroscopy. Statistical analysis of particle motion yielded data that could then be used to extract rheological information about the embedding medium as a function of time and space, thereby relating UV exposure to the polymerization and gelation of monomeric resins. The effects of varying depth, initiator concentration, inhibitor concentration, composition of the monomer, and light intensity on the gelation process were studied. The most striking result is the measured difference in gelation time observed as a function of UV penetration depth. The observed trend was found to be independent of UV light intensity and monomer composition. The intensity results were used to test the accuracy of energy threshold model, which is used to empirically predict photo-induced polymerization. The results of this research affirm the ability of microrheology to provide the high spatial and temporal resolution necessary to accurately monitor the photopolymerization process. The experimental data provide a better understanding of the photo-induced polymerization, which could lead to expanded use and improved industrial process optimization. The use of microrheology to monitor photopolymerization can also aid in the development of predictive models and offer the ability to perform in-situ quality control of the process.
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