Effect of filler functionalization on thermo-mechanical properties of polyamide-12/carbon nanofibers composites: a study of filler–matrix molecular interactions
Source abstract
Abstract
The effect of carbon nanofiber (CNF) functionalization on the thermo-mechanical properties of polyamide-12/CNF nanocomposites was investigated. Three main different surface treatments were performed to obtain CNF-OH (OH rich), CNF-Silane (C6H5Si-O-), and CNF-peroxide. CNF modified with poly-(tert-butyl acrylate) chains grown from the surface via ATRP (atom transfer radical polymerization) were also prepared and tested. The modified CNFs and neat CNFs were used as fillers in polyamide-12 nanocomposites and the properties of the ensuing materials were characterized and compared. Universal tensile tests demonstrated a substantial increase (up to 20 %) of the yield strength, without reduction of the final elongation, for all functionalized samples tested within 1 wt% filler content. Further evidences of mechanical properties improvement were given by dynamic mechanical thermal analyses. CNFs functionalized with poly-(tert-butyl acrylate) and silane exhibited the best performance with stiffening and strengthening at low (≤1 wt%) filler loadings, via a partial decrease of the intensity of β-transitions attributed to favorable interactions between the functional groups on the surface of functionalized CNFs and polyamide-12. CNFs treated with peroxide proved to be the most simple preparation technique and the ensuing nanocomposites exhibited the highest storage modulus at high (5 wt%) filler content. Theoretical simulations using the micro-mechanics model were used to predict the Young modulus of the composites and compare them with experimental data. The results obtained suggest a synergistic effect between the matrix and the filler enhanced by surface functionalization.
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Research context
OpenAlex topics: Polymer Nanocomposites and Properties · Carbon Nanotubes in Composites · Fiber-reinforced polymer composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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