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A systematic experimental and modeling study of several emulsion copolymerization systems has been performed, and will be reported in a series of papers. Ten binary and three ternary copolymerizations involving styrene, methyl methacrylate, butyl acrylate, butadiene, vinyl acetate, acrylic acid, and ethylene were studied varying polymerization temperature, monomer composition, water to monomer ratio, initiator and emulsifier concentrations. Conversion, particle size, copolymer composition, and gel content were measured at several reaction times. The goal of this series of papers is to assess our quantitative understanding of emulsion copolymerization expressed in the form of a comprehensive mathematical model applied to monomers widely used in industry. In this first paper of the series, a global comparison of the experimental results is made. It is observed that the gel content is higher in systems containing butyl acrylate and butadiene, and smaller in systems containing methyl methacrylate. Larger particle numbers are obtained for lattices containing acrylic acid and butadiene. It is also shown that, for most of the systems, integration of the simple Mayo–Lewis equation is adequate to explain the drift in copolymer composition observed experimentally. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 79: 2360–2379, 2001

ABSTRACT:Using a previously published model and continuing the series of papers started with styrenic copolymers, predictions for evolution of conversion and average particle diameter in batch experiments are compared against experimental data for four emulsion copolymerizations involving at least one acrylic monomer: (1) methyl methacrylate/butyl acrylate, (2) methyl methacrylate/butadiene, (3) methyl methacrylate-vinyl acetate, and (4) butyl acrylate/vinyl acetate. For each system a fraction of factorial experiments were run covering simultaneous variations in five variables: initiator [I] and surfactant [E] concentrations, water to monomer ratio (W/M), monomer composition, and temperature. Data fitting is performed to represent the experimental data as several parameters are not available from independent experimental sources. The model is able to explain the effects of simultaneous changes in emulsifier concentration, initiator concentration, and water to monomer ratio on conversion and average particle size histories, although in some cases only qualitatively. An assessment of the degree in which a general emulsion copolymerization model is useful for practical applications is made. Physical insight is also gained by observing the trends of adjusted parameters with temperature and copolymer composition.

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Using a model previously published, predictions for evolution of conversion and average particle diameter in batch experiments are compared against experimental data for four emulsion copolymerizations of styrene with the following monomers: (1) methyl methacrylate, (2) butyl acrylate, (3) butadiene, and (4) acrylic acid. For each copolymerization system the experiments covered simultaneous variations in five variables: initiator and surfactant concentrations, water to monomer ratio, monomer composition, and temperature. It is shown that after data fitting for unknown or uncertain parameters, the model is capable of explaining quantitatively the experimental observations for conversion evolution and only qualitatively the particle size evolution data. This points out to the possible contribution of particle nucleation mechanisms other than the micellar one, which is the only mechanism included in the model. Some of the adjustable parameter values were found to depend on the copolymer composition. The only case in which the model does not perform well is in the prediction of the effect of initiator concentration on the copolymerization rate for butadiene‐rich formulations. It is also found that the model predictions are very sensitive to the value of the diffusion coefficients of monomeric radicals in the copolymer particle, which are not readily available in the literature. It is concluded that it is important to independently measure these parameters in order to enhance the predictive power of models. It is also concluded that the model can be useful for practical applications. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 79: 2380–2397, 2001