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Factorial design optimization of polystyrene microspheres obtained by aqueous dispersion polymerization in the presence of poly(2‐ethyl‐2‐oxazoline) reactive stabilizer
Author(s) -
Iordache TantaVerona,
Banu Nicoleta D,
Giol Elena D,
Vuluga Dumitru M,
Jerca Florica A,
Jerca Valentin V
Publication year - 2020
Publication title -
polymer international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.592
H-Index - 105
eISSN - 1097-0126
pISSN - 0959-8103
DOI - 10.1002/pi.5974
Subject(s) - dispersion polymerization , polystyrene , particle size , factorial experiment , materials science , dispersion (optics) , polymerization , particle size distribution , chemical engineering , yield (engineering) , polymer chemistry , dispersity , aqueous solution , polymer , chemistry , mathematics , organic chemistry , composite material , physics , statistics , optics , engineering
The present study highlights the use of statistical design to establish an effective model for the synthesis of polystyrene microspheres by aqueous dispersion polymerization using poly(2‐ethyl‐2‐oxazoline) reactive stabilizer. The significant parameters (e.g. solvent polarity, stabilizer and initiator concentration) influencing the characteristic responses of the process such as yield, particle size and size distribution, as well as the interactions between the variables, were identified. The macromonomer concentration and solvent polarity influence both particle size and size distribution, whereas initiator concentration influences the yield. Analysis of the variance of process variables indicates that the models can be successfully used to describe the dispersion polymerization process. Moreover, the factorial design allows the development of microspheres with optimal properties with respect to size and size distribution. The experimental data regarding yield, particle size and size distribution of the optimized dispersion polymerization shows less than 7% difference compared with the predicted responses. In view of these results, the implementation of statistical design models represents an efficient solution for optimizing microparticle synthesis while aiming for industrial applications. © 2020 Society of Chemical Industry