A review on preparation and characterization of silver/nickel oxide nanostructures and their applications

Nickel oxide and silver oxide nanoparticles have wonderful properties that could be employed in numerous applications. Thus, synthesis of nickel silver oxide nanostructures with different characteristics is of great interest. In this review, many synthesis methods were reported such as: electrodeposition, electrochemical method, simple immersion process and subsequent RFsputtering deposition, chemical oxidative polymerization, followed by acidic sol–gel process, flame-based process, liquidphase reduction technique, sol–gel, hydrothermal method, co-precipitation method, simple precipitation method, thermal decomposition, chemical wet synthesis, low and high-temperature reduction, high-pressure autoclave, thermal treatment method, and laser-liquid–solid interaction technique. Reporting all methods employed for the fabrication of NiO and Ag2O nanostructures is useful to produce and develop novel nanomaterials with enhanced properties and applications. Studying the factors that tuned their properties: particle size, shape, and capping agents as well as solution pH is highly recommended in future works. Also, further research studies should be conducted for finding another/other facile and effective synthesis method/methods

Binary nickel and silver oxides by thermal route: preparation and characterization

Many studies have concentrated on exploring behaviors of nickel silver oxide nanoparticles using various routes of fabrication. Thermal treatment technique has never been utilized to fabricate nickel oxide silver oxide nanoparticles. In this research, binary (NiO)0.4 (Ag2O)0.6 nanoparticles were synthesized using the thermal treatment method due to its attractive advantages such as low cost, eco-friendly, and purity of nanoparticles. The structural, morphological, and optical behaviors of these nanoparticles were investigated at different calcined temperatures. X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), ultraviolet–visible spectroscopy (UV–Vis), and photoluminescence (PL) were the techniques used to characterize the synthesized nanoparticles. XRD was conducted at different calcined temperatures. The crystallite size was increased from 25.4 nm to 37.0 nm as the calcined temperature increased from 500 °C to 800 °C. Also, TEM results verified that the mean particle size was enlarged as the calcined temperatures increased. Two band gaps were found for each temperature, which were decreased from (3.05, 2.45) to (2.70, 1.95) eV as the temperature varied from 500 to 800 °C, respectively. Broadbands were observed by PL spectra, and the intensity of two emission peaks was also increased at higher temperatures. The results approved the successful formation of binary (NiO)0.4 (Ag2O)0.6 nanoparticles by a novel facile synthesis route. These nanoparticles are likely to have various applications, especially optical applications due to the formation of two band gaps

The effect of precursor concentration on the particle size, crystal size, and optical energy gap of CexSn1â’xO2 nanofabrication

In the present work, a thermal treatment technique is applied for the synthesis of CexSn1−xO2 nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of CexSn1−xO2 nanoparticles. CexSn1−xO2 nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the CexSn1−xO2 nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of CexSn1−xO2 nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready CexSn1−xO2 nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO3)3·6H2O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa

Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction

This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the same conditions. The process control factors were the power of the laser beam (80–140 W) and cutting speed (1–6 mm/s), while sheet thickness, standoff distance, and gas pressure were kept constant during experiments. KOD between the upper and lower opens of the kerf was the process response. KOD was predicted using three machine learning models, namely a conventional artificial neural network (ANN), a hybrid neural network–humpback whale optimizer (HWO-ANN), and a hybrid neural network–particle swarm optimizer (PSO-ANN). Experimental data for all polymeric materials were employed to train and test all models. The hybrid neural network–humpback whale optimizer model outperformed other models to predict KOD for all cut materials. The root-mean-square error between predicted and experimental data was 0.349–0.627 µm, 0.085–0.242 µm, and 0.023–0.079 µm for conventional neural network, neural network–particle swarm model, and neural network–humpback whale model, respectively

A virtual laboratory for radiotracer and sealed-source applications in industry

Radioactive sealed sources and radiotracer techniques are used to diagnose industrial process units. This work introduces a workspace to simulate four sealed sources and radiotracer applications, namely, gamma scanning of distillation columns, gamma scanning of pipes, gamma transmission tomography, and radiotracer flow rate measurements. The workspace was created in Geant4 Application for Tomographic Emission (GATE) simulation toolkit and was called Industrial Radioisotope Applications Virtual Laboratory. The flexibility of GATE and the fact that it is an open-source software render it advantageous to radioisotope technology practitioners, educators, and students. The comparison of the simulation results with experimental results that are available in the literature showed the effectiveness of the virtual laboratory

Radiological characterization of the phosphate deposit in Al-Jalamid phosphate mining area, Saudi Arabia

It is a known fact that phosphate rocks have high levels of natural radioactivity due to the presence of large concentrations of radionuclides. This work aims to estimate radiation exposure and dose levels at Al-Jalamid site in northern Saudi Arabia. Al-Jalamid area is one of the largest reserves of phosphate worldwide. Ma’aden, a Saudi Government public company, owns the mine and is responsible for all mining activities. Phosphate and soil samples collected from Al-Jalamid phosphate mining area have been analysed for their uranium and thorium content by an α-spectrometer using radiochemical techniques. The quantity of radon gas was measured both in groundwater and in the atmosphere (indoor and outdoor) at the site using a portable radiation survey instrument. Groundwater samples collected from wells surrounding the mining area were analysed using a liquid scintillation counter in addition to an α-spectrometer. Finally, it is found that phosphate rock concentrate products cannot be utilized economically based on the standards set by the International Atomic Energy Agency (IAEA), since the average activity concentration does not reach the limit set by IAEA and hence are not commercially feasible

Radiological characterization of the phosphate deposit in Al-Jalamid phosphate mining area, Saudi Arabia

It is a known fact that phosphate rocks have high levels of natural radioactivity due to the presence of large concentrations of radionuclides. This work aims to estimate radiation exposure and dose levels at Al-Jalamid site in northern Saudi Arabia. Al-Jalamid area is one of the largest reserves of phosphate worldwide. Ma’aden, a Saudi Government public company, owns the mine and is responsible for all mining activities. Phosphate and soil samples collected from Al-Jalamid phosphate mining area have been analysed for their uranium and thorium content by an α-spectrometer using radiochemical techniques. The quantity of radon gas was measured both in groundwater and in the atmosphere (indoor and outdoor) at the site using a portable radiation survey instrument. Groundwater samples collected from wells surrounding the mining area were analysed using a liquid scintillation counter in addition to an α-spectrometer. Finally, it is found that phosphate rock concentrate products cannot be utilized economically based on the standards set by the International Atomic Energy Agency (IAEA), since the average activity concentration does not reach the limit set by IAEA and hence are not commercially feasible