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Development of Cellulose Acetate Spherical Microparticles by Means of Melt Extrusion of Incompatible Polymer Blend

Polymers 2025 Score: 38 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Masaya Omura, Keiko Kobayashi, Kanji Nagai, Shu Shimamoto

Summary

Researchers developed cellulose acetate (CA) spherical microparticles using melt extrusion of incompatible polymer blends comprising CA with triacetin plasticizer and polyvinyl alcohol (PVA), followed by selective removal of the secondary polymers. Drawing on Wu's equation, particle size was controlled by adjusting shear rate and viscosity ratios, offering a biodegradable alternative to synthetic microplastic particles.

Cellulose acetate (CA), commercially produced from natural cellulose, is one of the promising candidates to solve the microplastic issue. In this study, attempts were made to prepare CA microparticles by means of melt extrusion of incompatible polymer blends comprising CA with plasticizer (triacetin (TA)) and polyvinyl alcohol (PVA) followed by selective removable of TA and PVA. As implied by semi-theoretical equation previously established by Wu (Wu's equation), particle size decreased with increasing shear rate or decreasing viscosity ratio of polymers. CA microparticles with a controlled size of 2-8 μm, narrow particle size distribution, and smooth surface were successfully obtained. Efforts were made to determine the numerical solution of Wu's equation to compare them with observed particle size. To this end, interfacial tension between dispersed and matrix phases to be incorporated in the equation was determined by group contribution methods. The root mean squared error (RMSE) between the observed and calculated particle size was unsatisfactorily large, 4.46 μm. It was found that one of the possible reasons for the limited prediction accuracy was migration of TA from the dispersed to matrix phase affecting the viscosity ratio. Further efforts will be required to achieve a better prediction.

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