Prediction of Exchange-Correlation Energy of Graphene Sheets from Reverse Degree-Based Molecular Descriptors with Applications

Over the past few years, the popularity of graphene as a potential 2D material has increased since graphene-based materials have applications in a variety of fields, including medicine, engineering, energy, and the environment. A large number of graphene sheets as well as an understanding of graphene’s structural hierarchy are critical to the development of graphene-based materials. For a variety of purposes, it is essential to understand the fundamental structural properties of graphene. Molecular descriptors were used in this study to investigate graphene sheets’ structural behaviour. Based on our findings, reverse degree-based molecular descriptors can significantly affect the exchange-correlation energy prediction. For the exchange-correlation energy of graphene sheets, a linear regression analysis was conducted using the reverse general inverse sum indeg descriptor, RGISI(p,q). From RGISI(p,q), a set of reverse topological descriptors can be obtained all at once as a special case, resulting in a model with a high correlation coefficient (R between 0.896 and 0.998). Used together, these reverse descriptors are graphed in relation to their response to graphene. Based on this study’s findings, it is possible to predict the exchange correlation energy as well as the geometric structures of graphene sheets with very little computational cost

Experimental Investigation and Optimization of Turning Polymers Using RSM, GA, Hybrid FFD-GA, and MOGA Methods

The machining of polymers has become widely common in several components of industry 4.0 technology, i.e., mechanical and structural components and chemical and medical instruments, due to their unique characteristics such as: being strong and light-weight with high stiffness, chemical resistance, and heat and electricity insolation. Along with their properties, there is a need to attain a higher quality surface finish of machined parts. Therefore, this research concerns an experimental and analytical study dealing with the effect of process parameters on process performance during the turning two different types of polymers: high-density polyethylene (HDPE) and unreinforced polyamide (PA6). Firstly, the machining output responses (surface roughness (Ra), material removal rate (MRR), and chip formation (λc)) are experimentally investigated by varying cutting speed (vc), feed rate (f), and depth of cut (d) using the full factorial design of experiments (FFD). The second step concerns the statistical analysis of the input parameters’ effect on the output responses based on the analysis of variance and 3D response surface plots. The last step is the application of the RSM desirability function, genetic algorithm (GA), and hybrid FFD-GA techniques to determine the optimum cutting conditions of each output response. The lowest surface roughness for HDPE was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1.47 mm and for PA6 it was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1 mm. The highest material removal rate was obtained at vc = 150 m/min, f = 0.01 mm/rev, and d = 1.5 mm for both materials. At f = 0.01 mm/rev, d = 1.5 mm, and vc = 100 for HDPE, and vc = 77 m/min for PA6, the largest chip thickness ratios were obtained. Finally, the multi-objective genetic algorithm (MOGA) methodology was used and compared

Influence of Ultrafine-Grained Microstructure and Texture Evolution of ECAPed ZK30 Magnesium Alloy on the Corrosion Behavior in Different Corrosive Agents

Magnesium-Zinc-Zirconium (Mg-Zn-Zr) alloys have caught considerable attention in medical applications where biodegradability is critical. The combination of their good biocompatibility, improved strength, and low cytotoxicity makes them great candidates for medical implants. This research investigation is focused on providing further insight into the effects of equal channel angular processing (ECAP) on the corrosion behavior, microstructure evolution, and mechanical properties of a biodegradable ZK30 alloy. Billets of Mg-3Zn-0.6 Zr (ZK30) alloy were processed through ECAP up to 4 passes of route Bc (rotating the billets 90° in the same direction between the subsequent passes) at 250 °C. Electron back-scatter diffraction (EBSD) was utilized to investigate the microstructural evolution as well as the crystallographic texture. Several electrochemical measurements were carried out on both a simulated body fluid and a 3.5% sodium chloride (NaCl) solution. Mechanical properties such as Vicker’s hardness and tensile properties were also assessed. The as-annealed (AA) microstructure was dominated by equiaxed coarse recrystallized grains with an average grain size of 26.69 µm. After processing, a geometric grain subdivision took place due to the severe plastic deformation. Processed samples were characterized by grain refinement and high density of substructures. The 4-passes sample experienced a reduction in the grain size by 92.8% compared with its AA counterpart. The fraction of high-angle grain boundaries increased significantly after 4-passes compared to the 1-pass processed sample. With regards to the crystallographic texture, the AA condition had its {0001} basal planes mostly oriented parallel to the transversal direction. On the other hand, ECAP processing resulted in crystallographic texture changes, such as the shifting of the ZK30 shear plane to be aligned at 45° relative to the extrusion direction (ED). Furthermore, the maximum texture intensity was reduced from 14 times random (AA billets) to 8 times random after ECAP processing through 4-passes. The corrosion rate of the 4-passes sample was tremendously reduced by 99% and 45.25% compared with its AA counterpart in the simulated body fluid and the NaCl solution, respectively. The pitting corrosion resistance of ZK30 showed notable improvements in the simulated body fluid by 471.66% and 352% during processing through 1-pass and 4-passes, respectively, compared with the 3.5% NaCl findings. Finally, significant improvements in the tensile strength, hardness, and ductility were also achieved

Optimizing the ECAP Parameters of Biodegradable Mg-Zn-Zr Alloy Based on Experimental, Mathematical Empirical, and Response Surface Methodology

Experimental investigations were conducted on Mg-3Zn-0.6Zr alloy under different ECAP conditions of number of passes, die angles, and processing route types, aimed at investigating the impact of the ECAP parameters on the microstructure evolution, corrosion behavior, and mechanical properties to reach optimum performance characteristics. To that end, the response surface methodology (RSM), analysis of variance, second-order regression models, genetic algorithm (GA), and a hybrid RSM-GA were utilized in the experimental study to determine the optimum ECAP processing parameters. All of the anticipated outcomes were within a very small margin of the actual experimental findings, indicating that the regression model was adequate and could be used to predict the optimization of ECAP parameters. According to the results of the experiments, route Bc is the most efficient method for refining grains. The electrochemical impedance spectroscopy results showed that the 4-passes of route Bc via the 120°-die exhibited higher corrosion resistance. Still, the potentiodynamic polarization results showed that the 4-passes of route Bc via the 90°-die demonstrated a better corrosion rate. Furthermore, the highest Vicker’s microhardness, yield strength, and tensile strength were also disclosed by four passes of route Bc, whereas the best ductility at fracture was demonstrated by two passes of route C

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

Copper and its related alloys are frequently adopted in contemporary industry due to their outstanding properties, which include mechanical, electrical, and electronic applications. Equal channel angular pressing (ECAP) is a novel method for producing ultrafine-grained or nanomaterials. Modeling material design processes provides exceptionally efficient techniques for minimizing the efforts and time spent on experimental work to manufacture Cu or its associated alloys through the ECAP process. Although there have been various physical-based models, they are frequently coupled with several restrictions and still require significant time and effort to calibrate and enhance their accuracies. Machine learning (ML) techniques that rely primarily on data-driven models are a viable alternative modeling approach that has recently achieved breakthrough achievements. Several ML algorithms were used in the modeling training and testing phases of this work to imitate the influence of ECAP processing parameters on the mechanical and electrical characteristics of pure Cu, including the number of passes (N), ECAP die angle (φ), processing temperature, and route type. Several experiments were conducted on pure commercial Cu while altering the ECAP processing parameters settings. Linear regression, regression trees, ensembles of regression trees, the Gaussian process, support vector regression, and artificial neural networks are the ML algorithms used in this study. Model predictive performance was assessed using metrics such as root-mean-squared errors and R2 scores. The methodologies presented here demonstrated that they could be effectively used to reduce experimental effort and time by reducing the number of experiments runs required to optimize the material attributes aimed at modeling the ECAP conditions for the following performance characteristics: impact toughness (IT), electrical conductivity (EC), hardness, and tensile characteristics of yield strength (σy), ultimate tensile strength (σu), and ductility (Du

Electrochemical Behavior of SiC-coated AA2014 Alloy Through Plasma Electrolytic Oxidation

The file attached to this record is the Publisher's final version. Open access article.In this study, AA2014 aluminum alloys were coated with a layer containing silica (SiC) formed by the plasma electrolytic oxidation (PEO) process. The PEO process was performed with different electrical parameters (frequency, current mode, and duty ratio) and with and without SiC to investigate the microstructural and electrochemical differences in the coated samples produced from the process. The microstructure and composition of the PEO coatings were studied by X-ray diffraction (XRD) and a scanning electron microscope (SEM) equipped with an energy dispersive spectroscope (EDS). A potentiodynamic polarization test and electrochemical impedance spectroscopy (EIS) were used to investigate the electrochemical behavior of the AA2014 PEO coated samples. The potentiodynamic polarization shows that the SiC PEO-coated samples have a significantly decreased corrosion rate (99.8 %) compared to the un-coated AA2014 Al alloy. Results showed that the coats containing SiC possess much higher corrosion resistance than the un-coated AA2014 Al alloy (8344673%), and the SiC-free coatings – which possess low corrosion resistance – because of their higher chemical stability and more compact microstructure