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Polymerization compounding composites of nylon‐6,6/short glass fiber
Author(s) -
Feng Wei,
AitKadi Abdellatif,
Brisson Josee,
Riedl Bernard
Publication year - 2003
Publication title -
polymer composites
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 82
eISSN - 1548-0569
pISSN - 0272-8397
DOI - 10.1002/pc.10049
Subject(s) - materials science , composite material , ultimate tensile strength , glass fiber , nylon 6 , glass transition , dynamic mechanical analysis , fiber , scanning electron microscope , crystallization , young's modulus , compounding , nylon 66 , polymer , polyamide , chemical engineering , engineering
Nylon‐6,6 was grafted onto the surface of short glass fibers through the sequential reaction of adipoyl chloride and hexamethylenediamine onto the fiber surface. Grafted and unsized short glass fibers (USGF) were used to prepare composites with nylon‐6,6 via melt blending. The glass fibers were found to act as nucleating agents for the nylon‐6,6 matrix. Grafted glass fiber composites have higher crystallization temperatures than USGF composites, indicating that grafted nylon‐6,6 molecules further increase crystallization rate of composites. Grafted glass fiber composites were also found to have higher tensile strength, tensile modulus, dynamic storage modulus, and melt viscosity than USGF composites. Property enhancement is attributed to improved wetting and interactions between the nylon‐6,6 matrix and the modified surface of glass fibers, which is supported by scanning electron microscopy (SEM) analysis. The glass transition (tan δ) temperatures extracted from dynamic mechanical analysis (DMA) are found to be unchanged for USGF, while in the case of grafted glass fiber, tan δ increases with increasing glass fiber contents. Moreover, the peak values (i.e., intensity) of tan δ are slightly lower for grafted glass fiber composites than for USGF composites, further indicating improved interactions between the grafted glass fibers and nylon‐6,6 matrix. The Halpin‐Tsai and modified Kelly‐Tyson models were used to predict the tensile modulus and tensile strength, respectively.