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Optimization of multicomponent photopolymer formulations using high-throughput analysis and kinetic modeling

Authors

  • Peter M. Johnson,

    1. University of Colorado at Boulder, Dept. of Chemical and Biological Engineering, Boulder, CO 80309
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  • Christopher N. Bowman,

    Corresponding author
    1. University of Colorado at Boulder, Dept. of Chemical and Biological Engineering, Boulder, CO 80309
    • University of Colorado at Boulder, Department of Chemical and Biological Engineering, Boulder, CO 80309
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  • Jeffrey W. Stansbury

    1. University of Colorado at Boulder, Dept. of Chemical and Biological Engineering, Boulder, CO 80309
    2. University of Colorado Denver, Dept. of Craniofacial Biology, Aurora, CO 80045
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Abstract

While high throughput and combinatorial techniques have played an instrumental role in materials development and implementation, numerous problems in materials science and engineering are too complex and necessitate a prohibitive number of experiments, even when considering high throughput and combinatorial approaches, for a comprehensive approach to materials design. Here, we propose a unique combination of high throughput experiments focused on binary formulations that, in combination with advanced modeling, has the potential to facilitate true materials design and optimization in ternary and more complex systems for which experiments are never required. Extensive research on the development of photopolymerizable monomer formulations has produced a vast array of potential monomer/comonomer, initiator and additive combinations. This array dramatically expands the range of material properties that are achievable; however, the vast number of potential formulations has eliminated any possibility of comprehensive materials design or optimization. This limitation is addressed by maximizing the benefits and unique capabilities of high throughput experimentation coupled with predictive models for material behavior and properties. The high throughput experimentation-model combination is useful to collect a limited amount of data from as few as 11 experiments on binary combinations of 10 analyzed monomers, and then use this limited data set to predict and optimize formulation properties in ternary resins that would have necessitated at least 1000 high throughput experiments and several orders of magnitude greater numbers of traditional experiments. A data analysis approach is demonstrated, and the model development and implementation for one model application in which a range of material properties are prescribed, and an optimal formulation that meets those properties is predicted and evaluated. © 2009 American Institute of Chemical Engineers AIChE J, 2010

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