4.7 Article

Rheology of PLA/regenerated cellulose nanocomposites prepared by the pickering emulsion process: Network formation and modeling

期刊

MATERIALS & DESIGN
卷 206, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2021.109774

关键词

Melt rheology; Oscillatory measurements; Cellulose nanofibers; Aggregation; Carreau-Yasuda model; Cross model

资金

  1. BME Nanotechnology and Materials Science TKP2020 IE grant from NKFIH Hungary [BME IE-NAT TKP2020]

向作者/读者索取更多资源

PLA/regenerated cellulose (RC) nanocomposites with varying cellulose content were prepared using the Pickering emulsion process, demonstrating RC as a suitable reinforcement for PLA with good dispersion and network structure formation. Model analysis aided in quantitative analysis of experimental results, offering additional insights into the structure and properties of the composites.
PLA/regenerated cellulose (RC) nanocomposites containing 0.5, 1.0, 1.5, 2.0, 3.0 and 5.0 wt% cellulose were prepared by the Pickering emulsion process. The structure and the rheological properties of the composites were characterized by microscopy, the measurement of melt flow rate and oscillatory rheom-etry. Viscosity was modelled with the modified Carreau-Yasuda and the modified Cross models, and a new model was proposed as well. The results show that RC is a suitable reinforcement for PLA and the Pickering emulsion process produces homogeneous material. Good dispersion and the relatively large aspect ratio of the cellulose nanofiller leads to the formation of a network structure already at small cel-lulose content. Viscosity increases rapidly with increasing cellulose concentration and a yield stress appears already at 0.5 wt% RC content. Modeling helps the quantitative analysis of the experimental results and offers additional information about the structure and properties of the composites studied. All three models predict yield stress well, but only the new model gives reasonable values for the power law exponent. Other parameters and their composition dependence are difficult to interpret. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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