The
performance of waterborne polymer–polymer composite
materials strongly depends on the particle morphology, but the precise
quantitative characterization of complex morphologies is a long-standing
problem. In this work, high angle annular dark-field scanning transmission
electron microscopy (HAADF-STEM) has been used to obtain a 3D quantitative
characterization of the morphology of polymer–polymer particles.
The potential of this technique was demonstrated in the study of the
effect of process variables (T
g of the
seed, type of initiator, and instantaneous conversion of the monomer)
on particle morphology during the semicontinuous emulsion copolymerization
of styrene and butyl acrylate on methyl methacrylate-rich seeds. This
analysis unveiled new mechanisms of morphology development.
Polymer-polymer composite nanoparticles allow both the improvement of the performance in stablished applications of waterborne polymer dispersions and targeting new applications that are out of reach of currently available products. The performance of these materials is determined by the particle morphology. To open the way to process optimization and on-line control of the particle morphology, the capability of the recently developed model to predict the evolution of the particle morphology during seeded semibatch emulsion polymerization process was evaluated. Structured polymer particles were synthesized by copolymerization of styrene and butyl acrylate (St-BA) on methyl methacrylate and butyl acrylate (MMA-BA) copolymer seeds of different Tgs. The model captured well the effect of process variables on the evolution of the particle morphology, opening the way to the design and implementation of optimal strategies.
An event‐driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot‐plant reactors are presented.
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