journal article·2021-02

Different-structured nanoclays incorporated composites: Computational and experimental analysis on mechanical properties

Zixuan Chen, Tianyu Yu, Yun-Hae Kim, Zetian Yang, Yan Li, Tao Yu

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Abstract

Abstract The objective of this study is to investigate the mechanical properties of basalt fiber reinforced polymers (BFRPs) with different structured nanoclays incorporation through experimental and computational approaches. To solve the inherent weak interfacial adhesion problem of inorganic basalt fiber is also one purpose of this study. Halloysite nanotube (HNT) and nano Kaolin, which both derived from Kaolin's sub group, were employed to conduct the study due to their distinctive structures. The HNTs were conducted to heat and silane treatments change the structure and enhance the interfacial bonding. A modified Eshelby-Mori-Tanaka micromechanic model was implemented to predict the effective modulus of BFRPs regarding the structures of incorporated nanoclays. The computational and experimental results demonstrate the identical tendency that a flattened nanoclay structure, or larger sectional aspect ratio, is more effective for the modulus improvement. The lower experimental results comparing to computational results are due to the imperfect nanoclay-resin interfacial bonding. Incorporation of different structured nanoclays also improved the interlaminar strength and toughness of BFRPs by experimental approach. The relative dense and uniform distribution of nanoclays was observed on the interlaminar surface of fractured double cantilever beam (DCB) specimens. The bridging effect of tubular structured HNTs which impeded the crack propagation was determined as the mechanism of the improvement of interlaminar properties.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Clay minerals and soil interactions · Polymer Nanocomposites and Properties · Concrete and Cement Materials Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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