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In order to investigate the effects of different functional groups on graphene on the mechanical behavior of carbon fiber reinforced epoxy composites were fabricated by hand layup technique. In this study, three different variety of functionalized graphene (–NH2, –COOH, and –OH) with varying weight percent (i.e. 0.1, 0.3, and 0.5 wt%) was considered. Tensile, interlaminar, and three-point flexural tests were conducted to evaluate the effects of graphene concentration and fiber stacking sequence (i.e. 0/90° and 0/90/ ± 45°) on composites. The microstructures of the composites were characterized by a scanning electron microscope. Experimental results showed that pristine and functionalized graphene significantly improved the mechanical properties of carbon fiber epoxy composites. Compared to neat carbon fiber epoxy composites, the maximum tensile strength of 33.34% and 26.35% were noticed for 0/90°–COOH–0.5 and 0/90/ ± 45°–NH2–0.3 stacked functionalized graphene modified carbon fiber epoxy composites, respectively. Moreover, 48.44% and 59.1% flexural strength were recorded for 0/90°–COOH–0.3 and 0/90/ ± 45°–OH–0.5 stacked functionalized graphene modified carbon fiber epoxy composites, respectively. Furthermore, maximum interlaminar shear strength (ILSS) of 120.25% was observed for 0/90/ ± 45° stacked carbon fiber epoxy composites. Finally, the results obtained with graphene as reinforcement filler can cause a significant improvement in the mechanical performance of fiber composites. Henceforth, they can also be considered as a promising candidate for designing composites for structural applications. [ABSTRACT FROM AUTHOR]

In this article, we investigated the effects of different functional groups in graphene on the mechanical performance of epoxy nanocomposites fabricated using the in-situ polymerization method. This study considered pristine graphene and three different varieties of functionalized graphene (–NH2, –COOH, and –OH) with varying weight percent (i.e., 0.25, 0.5, and 1.0 wt%). The functional groups grafted of graphene have been confirmed through Fourier transforms infrared (FTIR) spectra. Microstructural features and fracture surfaces were also examined by field-emission scanning electron microscopy with energy-dispersive X-ray spectroscopy. In comparison to pure epoxy, –OH functionalized graphene/epoxy nanocomposites showed a maximum improvement of 37.24% in tensile strength and 38.05% in flexural strength. The improved mechanical properties of epoxy nanocomposites were attributed to homogeneous dispersion, graphene bridging, and better interfacial contact between epoxy and graphene. The effect of pristine and functionalized graphene on the mechanical properties of epoxy nanocomposites was studied. Nanocomposites were fabricated for three different wt% (0.25, 0.5, and 1.0) of graphene nanofillers. Field emission scanning electron microscopy (FE-SEM) with EDX was used to examine the microstructural features of fractured surfaces. Graphene enhanced the mechanical properties of epoxy nanocomposites. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine Learning models were developed to predict the output of the supercapacitor. • Artificial neural network model had achieved the highest performance. • Machine Learning provides optimum material composition for better output. Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (C sp), electrical conductivity (σ) and sheet resistance (R s) for MXene/Graphene Nanoplatelets (GNPs) based energy storage devices. These output features are modeled as a function of wt.% in different weight ratios of GNPs in MXene, optimum potential window and scan rates. The datasets are obtained by the real time output measurements through the experimental runs of these composites in different weight ratios. Among these models, ANN had achieved the highest performance followed by MLR, SVR and RF. From both the experimental and ANN results, the electrode with 20 wt% of GNPs in MXene (MG-80) exhibited the highest C sp of 226.6F/g at 5 mV/s with a long cycle life having 84.2 % retention even after 5000 cycles of charging-discharging. ANN model is further utilized to predict the cyclic stability of MG-80 electrode upto 10,000 cycles and the results show the accuracy of the ML model to fabricate storage devices. Furthermore, the structure of MXene/GNPs composites are investigated by different characterization techniques. XRD spectra confirmed the successful synthesis of MXene and the successful intercalation of GNPs into MXene sheets. The morphology of the embedded GNPs in layered MXenes are determined through FE-SEM/EDX and HR-TEM analysis. The increase in the surface area and pore volume in the MXene/GNPs composite are revealed by BET measurements. XPS is utilized to find out the chemical element states of the bare MXene as well as its composite with GNPs. [ABSTRACT FROM AUTHOR]

Simulation of molecular dynamics (MD) allows mimicking the systematic variation of mechanical properties beyond the capacity of experimental methods. In this review, the status of the molecular modelling, simulation and mechanical and thermal characterisation of graphene and the properties of bulk nanocomposites using MD are extensively examined. The molecular models of graphene (pristine and functionalised), and the most common epoxy matrix with preeminent MD energy minimisation tools are thoroughly examined. The presence of MD's simulation setup and the computational procedure for different styles of mixing the polymer matrix with graphene as nanofiller are exhaustively reviewed. In particular, in the analysis of several papers on graphene and their composites, it has been noticed that mechanical, thermal and interfacial properties are ubiquitous to most of the engineering applications. In this review, advances in the molecular modelling and simulation of graphene and their composites has been discussed in detail keeping graphene structures, aspect ratio, weight fraction and density effect as major factors. Finally, the research gaps and future scope are discussed, which indeed helps the researchers in scaling up their researches in graphene and their composites, which may further lead to their experimental findings. [ABSTRACT FROM AUTHOR]

In the present review, the recent progress in describing the intricacies of mechanical and thermal properties of all types of graphene- and modified graphene-based polymer nanocomposites has been comprehensively examined. The effectiveness of microscopy bouquet for the intrinsic characterization of graphene family and their composites was clearly demonstrated in this research. Furthermore, the utility of the dynamic mechanical analysis and thermo-gravimetric analysis employed for thermal characterization that has been reported by various researchers was exhaustively analyzed in this paper. This research primarily focused on the analyses of several good articles concerned with hybrid graphene composites and the synergetic effect of graphene with other nanofiller to assess its effect on the mechanical properties of its corresponding composites. Such systematic analysis of previous literatures imparted a direction to the researchers about the solution of improved interfacial properties as well as the enhanced dispersion into the vicinity of the matrix. This current research has suggested that the presence of the graphene filler even at very low loadings has shown considerable improvement in the overall mechanical properties of graphene. Further studies to optimize the value of the filler need to be addressed in order to gain complete understanding of the properties of graphene. The potential applications, current challenges, and future perspectives pertaining to these nanocomposites were elaborately discussed in the current study with regard to the multi-scale capabilities and promising developments of the graphene-family-based nanocomposites materials. [ABSTRACT FROM AUTHOR]

Present work investigates the effect of alumina/graphene (GnP) hybrid nanoparticle additives on tribological properties of lubricant as cutting fluid in turning of AISI 304 steel. The hybrid nano-lubricant has been developed in different volumetric concentrations (0.25, 0.75 and 1.25 vol%). The tribology and wettability testing of lubricants confirm that wear and friction coefficient reduces with an increase of nanoparticle concentration. The hybrid nanofluid has shown the lowest pin wear and friction coefficient. The performance of hybrid nano-lubricant have been compared with the alumina nanofluid. The results establish that mixing of GnP with alumina enhanced its tribological properties. The developed hybrid nano-lubricant significantly reduced the tool flank wear and nodal temperature by 12.29% and 5.79%, respectively compare to alumina based lubricant. [ABSTRACT FROM AUTHOR]

A hybrid nanofluid (NF) with better thermal and tribological properties has been developed in this investigation by mixing alumina-based nanofluid with graphene nanoplatelets (GnP) in the volumetric concentrations of 0.25, 0.75 and 1.25 vol %. It was noted that an increase of nanoparticle concentration enhances both, the thermal conductivity and viscosity, though the hybrid nanofluid has lower thermal conductivity compare to its constituents and the viscosity lies in between its constituents. The tribological test confirms that the wear decreases with the increase of nanoparticle concentration and the hybrid nanofluid generated least amount of wear. Hybrid nanofluid shows better wettability results as compared to alumina-based nanofluid as well as base fluid. The turning of AISI 304 steel under minimum quantity lubrication (MQL) technique clearly establishes that application of hybrid nanofluid performs better as compared to alumina nanoparticle mixed cutting fluid. The study reveals that blending of GnP with alumina enhances the performance of hybrid nanofluids. The application of hybrid nanofluid with MQL significantly reduces the surface roughness by 20.28% and cutting force, thrust force and feed force by 9.94%, 17.38% and 7.25%, respectively. [ABSTRACT FROM AUTHOR]