Advances in graphene reinforced metal matrix nanocomposites:Mechanisms,processing,modelling,properties and applications

Graphene has been extensively explored to enhance functional and mechanical properties of metalmatrix nanocomposites for wide-range applications due to their superior mechanical,electrical and thermal properties.This article discusses recent advances of key mechanisms,synthesis,manufacture,modelling and applications of graphene metal matrix nanocomposites.The main strengthening mechanisms include load transfer,Orowan cycle,thermal mismatch,and refinement strengthening.Synthesis technologies are discussed including some conventional methods(such as liquid metallurgy,powdermetallurgy,thermal spraying and deposition technology)and some advanced processing methods(such as molecular-level mixing and friction stir processing).Analytical modelling(including phenomenological models,semi-empirical models,homogenization models,and self-consistent model)and numerical simulations(including finite elements method,finite difference method,and boundary element method)have been discussed for understanding the interface bonding and performance characteristics between graphene and different metal matrices(Al,Cu,Mg,Ni).Key challenges in applying graphene as a reinforcing component for the metal matrix composites and the potential solutions as well as prospectives of future development and opportunities are highlighted.

Rapid fatty acids detection of vegetable oils by Raman spectroscopy based on competitive adaptive reweighted sampling coupled with support vector regression

Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.