Scalable Production and Thermoelectrical Modeling of Infusible Functional Graphene/Epoxy Nanomaterials for Engineering Applications

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH(2022)

引用 2|浏览5
暂无评分
摘要
: The growing market for the application of carbon fiber reinforced polymers (CFRP) as structural components has necessitated the need to develop CFRPs with improved matrix properties to avoid the challenges associated with heat dissipation and electric current flow paths within the composites. In this work, we have developed infusible graphene nanoplatelets (GnP)-modified epoxy polymer nanocomposites containing 0.5-5.0 wt % GnP using two different processing techniques, (1) high-shear mechanical mixing (HMM) and (2) ultrasonication. Compared to HMM, ultrasonication enabled the proper exfoliation of graphene sheets within an epoxy matrix without damaging the sheet morphology of the nanoplatelets. The resulting ultrasonicated nanocomposite with the addition of 2 wt % GnP delivers a maximum electrical conductivity of 4.75 x 10-5 S/m. Unlike electrical conductivity, thermal conductivity properties of nanocomposites were found to be less dependent on the choice of processing method or restacking of graphene sheets at higher filler loadings. Slight improvements to thermal conductivity values were achieved for samples prepared through HMM compared to the ultrasonication method with a maximum value of 0.45 W m-1 K-1 at 5 wt % GnP. New effective thermal and electrical conductivity models were defined for randomly oriented two-phase heterogeneous nanocomposites so that the functional characteristics of GnP-epoxy nanocomposites could be predicted at different loadings. Ultimately, the optimized GnP-modified matrix containing 2 wt % GnP was successfully embedded into a carbon fiber laminate composite component using a customized vacuum infusion molding method to enhance the multiscale properties of FRPs. This solvent-free and industrially scalable process may pave the way to a future generation of smart self-sensing reinforced composites
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要