Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach

Abstract

This article presents a method for the electrochemical preparation of a coating of nickel-silica nanocomposites on a carbon steel substrate. The incorporation of hydrophilic silica particles into the Ni composite coating during co-electrodeposition is so difficult due to the small size and the hydrophilicity of SiO 2 particle, generally less than 2 v% of silica is incorporated into the composite at different current densities, agitation speeds and silica concentrations. The effect of the presence of four surfactants, namely cocamidopropyl betaine (CAPB), decylglycoside (DG), cetyltrimethyl ammonium chloride (CTAC) and ammonium lauryl ether sulfate (ALES), on overcoming this problem was investigated in this research, and the surfactants were found to greatly influence the surface charge of silica, silica incorporation percentage and the microstructure of the composite. In fact, upon increasing the internal stresses, the products prepared in the presence of CAPB and DG were found to crack to some degree. CTAC was found to lead to entrapment mode silica co-deposition in the Ni coating. Furthermore, the addition of ALES into an electrolyte bath negatively supercharged silica surfaces and increased silica dispersion, which led to a dramatic increase in the silica incorporation percentages to around 14 v%. The results showed that Ni-SiO 2 composites prepared in the presence of ALES had better corrosion resistance, hardness and wear properties.

Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet

“…Since the absolute value of engineering moduli of carbon based nanomaterial depends largely on the deployed inter-atomic potential function, one can expect a variation should different potential function be deployed. Future work is to compare the CI-MD approach to those of other methods such as neural network and support vector regression [54][55][56][57] and evaluate any differences from the current study.…”

A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material

“…The optimized REBO potential is obtained by determining optimum parameter constants for the original REBO [37] that can effectively describe the in-plane phonondispersion data of graphite. The REBO potential is also ideal for simulating a system consisting of large number of hydrocarbon atoms while maintaining the accuracies of semi-empirical and ab initio techniques [38][39][40][41][42][43][44]. The REBO potential is described mathematically as,…”

Manufacturing Graphene and Graphene-based Nanocomposite for Piezoelectric Pressure Sensor Application: A Review

“…Compound graphene was previously supported on substrates such as minerals and silicon carbide (SiC), but its inability to form two-dimensional structures led to uncertainty of graphene as an independent list until Kostya Novoselvo and Andre Geim made a stunning discovery in which they proposed precise mechanical division (Scotch tape strategy) To break the graphene into a single layer of graphite [37].…”