An aluminum/cobalt/iron/nickel alloy as a precatalyst for water oxidation

Among different strategies, water splitting toward hydrogen production is a promising process to store energy from intermittent sources. However, the anodic water oxidation is a bottleneck for water splitting. In this paper, we report an aluminum/cobalt/iron/nickel alloy as a precatalyst for the electrochemical water oxidation. The alloy electrode contains different metal ions including cobalt, iron, and nickel which all are efficient for water oxidation is tested. We characterized this electrode using scanning electron microscopy, transmission electron microscopy, diffuse reflectance infrared Fourier transform spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy and electrochemical methods. After stabilization, the electrode shows an onset overpotential of 200.0 mV and affords a current density of 3.5 mA cm^?2 at an overpotential of 600.0 mV in KOH solution at pH 13.     

Study of hydrogen storage performance of ZnO–CeO2 ceramic nanocomposite and the effect of various parameters to reach the optimum product

ZnOCeO₂ nanocomposites have been successfully prepared by a simple sol-gel approach via employing fructose as a green capping agent. The effect of various parameters including the different precursors of zinc, calcination time and temperature on the morphology and size of as-synthesized products were investigated to reach the optimum conditions. Different analysis to study the synthesized products was utilized. We used X-ray diffraction (XRD) patterns to investigate the crystal structure of the products. The chemical composition of the nanocomposite was characterized by energy-dispersive X-ray analysis (EDX) and Fourier transform infrared (FT-IR) analyses. To study the size and morphology of nanocomposites were employed scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images. The structural properties (porosity and specific surface area) of nanocomposite were evaluated by BET analysis. The performance of metal oxides as a host for hydrogen storage has not been widely studied. Albeit the technology of hydrogen energy enhanced gradually, the performance of metal oxides as a host for hydrogen adsorption has not been widely studied. According to our knowledge, the electrochemical hydrogen storage of prepared ZnOCeO₂ nanocomposite was investigated via chronopotentiometry method in KOH (6 M) electrolyte, for the first time. The maximum discharge capacity of the optimized product (S6) was observed at 2400 mAh/g after 20 cycles. The results showed the synthesized ZnOCeO₂ nanocomposites can be used as a suitable candidate for storage of energy in future.     

Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models

Finding construction components in cluttered point clouds is a critical pre‐processing task that requires intensive and manual operations. Accurate isolation of an object from point clouds is a key for further processing steps such as positive identification, scan‐to‐building information modeling (BIM), and robotic manipulation. Manual isolaton is tedious, time consuming, and disconnected from the automated tasks involved in the process. This article adapts and examines a method for finding objects within 3D point clouds robustly, quickly, and automatically. A local feature on a pair of points is employed for representing 3D shapes. The method has three steps: (1) offline model library generation, (2) online searching and matching, and (3) match refinement and isolation. Experimental tests are carried out for finding industrial (curvilinear) and structural (rectilinear) elements. The method is verified under various circumstances in order to measure its performance toward addressing the major challenges involved in 3D object finding. Results show that the method is sufficiently quick and robust to be integrated with automated process control frameworks.     

A finite fracture mechanics model for the prediction of the open-hole strength of composite laminates

A new model based on finite fracture mechanics is proposed to predict the open-hole tensile strength of composite laminates. Failure is predicted when both stress-based and energy-based criteria are satisfied. The material properties required by the model are the ply elastic properties, and the laminate unnotched strength and fracture toughness. No empirical adjusting parameters are required. Using experimental data obtained in quasi-isotropic carbon–epoxy laminates it is concluded that the model predictions are very accurate, resulting in improvements over the traditional strength prediction methods. It also is shown that the proposed finite fracture mechanics model can be used to predict the brittleness of different combinations of materials and geometries.     

A sensitive biosensor for cyanide detection using modified glassy carbon electrode with reduced graphene oxide and immobilization of horseradish peroxidase

Cyanide is a highly poisonous and hazardous substance which may release into the environment from natural sources or industrial effluent; therefore, cyanide detection is a fundamental step to prevent environmental pollution and secure health and safety. In this study, we prepared a sensitive amperometric inhibition biosensor for cyanide detection by immobilization of horseradish peroxidase (HRP) enzyme and reduced graphene oxide (rGO) on the surface of glassy carbon electrode (GCE). To do so, we performed the amperometric measurement by modified GCE to test its efficiency in detecting cyanide. The optimum conditions of pH equal to 7.5, −100 mV applied potential, 0.7 μM mediator concentration, and 0.5 mM substrate concentration were found. Then, experiments were performed at different boundary conditions in a range of 0.1 to 10 μM cyanide concentration at optimal conditions and a low detection limit of 0.01 μM was obtained. Also, the possible mechanism of inhibition was analyzed based on the Michalis–Menten equation and non-competitive inhibition was observed. Due to high sensitivity, low detection limit, and low cost, this biosensor is proposed as a useful method for cyanide determination in real samples.     

Molecular dynamics simulation study of proton diffusion in polymer electrolyte membranes based on sulfonated poly (ether ether ketone)

Molecular dynamics (MD) simulation technique was employed to investigate the effect of degree of sulfonation (DS) on structural and dynamical characteristics of sulfonated poly (ether ether ketone) (SPEEK) membranes at different temperatures. MD Simulations were performed for the cell containing SPEEK chains, hydronium ions and water molecules under NVT and NPT conditions. By evaluating the pair correlation functions, it was observed that with increasing the DS of SPEEK, the distance between sulfur atoms increases, more water molecules solvate the sulfur atoms and hydronium ions, the average sulfur–hydronium ion separation distance increases and larger water clusters are formed. It was also found that with increasing DS and temperature, the diffusion coefficient and conductivity of hydronium ions enhance. It was also understood, the simulated ionic conductivities qualitatively follow the experimental data.     

Study of flow field,heat transfer,and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method

In this study, the turbulent natural convection of Ag‐water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics‐artificial neural network (CFD‐ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag‐water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF‐ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e^?022 and 1.552e^?015 mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF‐ANN.     

Synthesis of mesoporous nanocrystalline zirconia with tetragonal crystallite phase by using ethylene diamine as precipitation agent

Mesoporous nanocrystalline zirconia with high surface area and pure tetragonal crystallite phase has been prepared by bifunctional ethylene diamine as both precipitating agent for ZrO(NO₃)₂ to ZrO(OH)₂ and colloidal protecting agent for the ZrO(OH)₂ nanoparticles. The effect of refluxing time and temperature on the structural properties of the zirconia were investigated. The obtained results showed that the increasing in refluxing time and temperature improved the thermal stability and increased the tetragonal content of the zirconia. The results also showed that the addition of Pluronic P123 block copolymer surfactant acted as a cosurfactant and increased the specific surface area of the zirconia.