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Stereocomplex-type polylactide with remarkably enhanced melt-processability and electrical performance via incorporating multifunctional carbon black

As a popular “green” engineering plastic, stereocomplex-type polylactide (SC-PLA) exhibits great application potential in various fields owing to its outstanding physicochemical performance and durability. However, the applications of SC-PLA still face formidable challenges mostly associated with its inferior melt-processability (i.e., the weak melt memory effect to motivate exclusive SC crystallization and extremely low melt viscosity) and the lack of necessary functional features (e.g., electrical conductivity) in some cases. Herein, we devise a facile and robust strategy to overcome these obstacles by incorporating carbon black (CB) into equimolar poly(L-lactide)/poly(D-lactide) (PLLA/PDLA) blend. It is interesting to find that the CB particles can adsorb many PLLA/PDLA chain segments on their surface and such strongly adsorbed PLA segments could interact with PLA chains outside the surface to form physical junctions capable of stabilizing the PLLA/PDLA chain assemblies in the melt, finally inducing the exclusive SC formation during subsequent crystallization. Meanwhile, the CB particles can substantially enhance the melt viscosity of the blend (from 3.9 Pa s to 844.1 Pa s when measuring at 250 °C and 50 Hz). Because of the greatly improved melt-processability, the PLLA/PDLA/CB composites have been successfully processed into highly crystalline products with exclusive SC crystallites and excellent thermomechanical performance by injection molding. Additionally, the CB particles can endow the composite products with fascinating electrical conductivity (19.0 S/m) and electromagnetic interference shielding effectiveness (26.6 dB). This work could open up a promising avenue towards high-performance and multifunctional PLA engineering Bioplastic.

Publication date: 03/02/2020

Author: Zhenwei Liu, Fangwei Ling, Xingyuan Diao, Meirui Fu, Hongwei Bai, Qin Zhang, Qiang Fu



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870292.