Nanocomposite Coating Based on Thermoplastic Acrylic Resin and Montmorillonite Clay: Preparation and Corrosion Prevention Properties

Abstract Different amounts of nanoclay were incorporated into the acrylic resin matrix at 0, 1, 3, and 5 wt.% loadings. The coatings were applied on low carbon steel plates. Optical microscopy, sedimentation test, transmission electron microscopy, and X-ray diffraction were employed to investigate the dispersion of nanoclay in matrix. The corrosion resistance of coatings was evaluated by electrochemical impedance spectroscopy, polarization measurement, and salt spray test. In addition, pull-off and cross-cut tests were used for the assessment of coating adhesion to the substrate. The results indicated that the anti-corrosive properties of the acrylic resin were obviously increased by the addition of nanoclay. The nanocomposite coatings containing 3 wt.% clay showed the best corrosion resistance. Finally, the nanocomposites containing 1 and 3 wt.% showed the highest adhesion to the substrate

An Electrochemical Investigation of Nano Cerium Oxide/Graphene as an Electrode Material for Supercapacitors

In this paper, the effect of cationic and anionic ion sizes on the charge storage capability of graphene nanosheets is investigated. The electrochemical properties of the produced electrode are studied using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques in 3M NaCl, NaOH, and KOH electrolytes. Scanning electron microscopy (SEM) is used to characterize the microstructure and nature of the prepared electrode. The SEM images and X-ray diffraction (XRD) patterns confirm the layered structure (12 nm thickness) of the used graphene with an interlayer distance of 3.36 Å. The electrochemical results and the ratio of  confirm good charge storage and charge delivering capability of the prepared electrode in the 3M NaCl electrolyte. Charge/discharge cycling tests show a good reversibility and confirm that the solution resistance will increase after 500 cycles. In this paper, the effect of cationic and anionic ion sizes on the charge storage capability of graphene nanosheets, is investigated. Electrochemical properties of produced electrode are studied using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques, in 3M NaCl, NaOH and KOH electrolytes. Scanning electron microscopy (SEM) is used to characterize the microstructure and nature of prepared electrode. SEM images and XRD patterns confirm the layered structure (12 nm thickness) of the used graphene with an interlayer distance of 3.36 (Å). The electrochemical results and the ratio of q*O/q*T confirm a good charge storage and charge delivering capability of prepared electrode in 3M NaCl electrolyte. Charge/discharge cycling test shows a good reversibility and confirms that solution resistance will increase after 500 cycles

Preparation and Characterization of Structure and Corrosion Resistivity of Polyurethane /Montmorillonite/Cerium Nitrate Nanocomposites

In this study, nanocomposite coatings based on polyurethane cerium nitrate montmorillonite (MMT) were prepared, applied on carbon steel substrates, and investigated. The nanocomposite coatings were successfully prepared by the effective dispersing of nanoparticles in polyurethane resin by mechanical and sonication processes. The state of dispersion, dissolution, and incorporation were characterized by optical microscopy, sedimentation tests, and transmission electron microscopy. The structure and properties of the nanocomposite coatings were investigated by X-ray diffraction and anticorrosive properties of the nanocomposites were studied by Tafel polarization measurements. The experimental results showed that the PU/MMT/Cerium nitrate nanocomposite coatings were superior to the neat PU in corrosion protection. In addition, it was observed that the corrosion protection of the nanocomposite coatings was improved as the clay and cerium nitrate loadings were increased to 4 wt.% to 2 wt.% respectively

Nanocomposite Coating Based on Thermoplastic Acrylic Resin and Montmorillonite Clay: Preparation and Corrosion Prevention Properties

Different amounts of nanoclay were incorporated into the acrylic resin matrix at 0, 1, 3, and 5 wt.% loadings. The coatings were applied on low carbon steel plates. Optical microscopy, sedimentation test, transmission electron microscopy, and X-ray diffraction were employed to investigate the dispersion of nanoclay in matrix. The corrosion resistance of coatings was evaluated by electrochemical impedance spectroscopy, polarization measurement, and salt spray test. In addition, pull-off and cross-cut tests were used for the assessment of coating adhesion to the substrate. The results indicated that the anti-corrosive properties of the acrylic resin were obviously increased by the addition of nanoclay. The nanocomposite coatings containing 3 wt.% clay showed the best corrosion resistance. Finally, the nanocomposites containing 1 and 3 wt.% showed the highest adhesion to the substrate

Development of machine learning models to evaluate the toughness of OPH alloys

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion- Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets