ArticlePolymers2026
Impeller Configuration and Agitation Speed Govern Lignin Dispersion and Resultant Properties of Natural Rubber Composites in Wet Mixing: A CFD Experimental Study.
Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Alkali lignin is an abundant renewable biopolymer that can both reinforce natural rubber (NR) and protect it against thermo-oxidative aging, but its strong polarity drives self-aggregation and poor dispersion in the NR matrix. Wet mixing pre-disperses lignin in the aqueous phase before compounding and is widely used to improve the dispersion of lignin relative to dry mixing; however, the initial latex-lignin mixing stage that ultimately controls the dispersion state has received little systematic attention. Here, a Eulerian-Eulerian two-fluid CFD model was established for the alkali lignin (AL)/natural rubber latex (NRL) wet-mixing system, and four impeller configurations (single- and two-stage straight-blade and 45° pitched-blade impellers) were compared at agitation speeds of 400-800 rpm. The simulations show that dispersion homogeneity is associated with the synergistic matching of macroscopic axial circulation, turbulent-fluctuation distribution, and the openness of the circulation structure. The two-stage 45° pitched-blade impeller achieved the best dispersion at all speeds, whereas the two-stage straight-blade impeller dispersed lignin worse than its single-stage counterpart because flow-region compartmentalization hindered axial particle exchange. Dispersion improved monotonically with speed, increasing rapidly from 400 to 600 rpm and showing an apparent plateau between 600 and 800 rpm. Payne effect, microstructural, vulcanization, mechanical, and thermo-oxidative aging characterizations all reproduced the dispersion ranking predicted by the model. These results clarify the role of the initial mixing stage in AL/NRL wet processing and provide a basis for impeller selection and process-parameter optimization.
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