Intelligently Synthesized In Situ Suspension Carbon Black/Styrene/Butylacrylate Composites: Using Artificial Neural Networks towards Printing Inks with Well-Controlled Properties

In this work, an intelligent approach is applied for the first time in modeling and optimization of in situ polymerized black printing toner composites which enables fine tuning of toner properties, including physical, color, and thermal characteristics. A genetic algorithm-based computer code was developed to optimize the architecture of artificial neural network, by which an accurate model was developed for the prediction of toner properties. Styrene-acrylate copolymer was synthesized in the presence of carbon black through suspension polymerization varying monomer ratio, carbon black content and surfactant content. The developed code learned nonlinear behavior of polymerization, generalized it to recipes never been experienced, and made the decision according to the pattern learned. Interestingly enough, the polymerization recipe by which printing toners with acceptable characteristics (particle size, particle size distribution, and colorimetric properties) could be produced was identified and reported.