Room temperature dilute magnetic semiconductor response in (Gd, Co) co-doped ZnO for efficient spintronics applications.

The co-precipitation approach was utilized to experimentally synthesize ZnO, Zn0.96Gd0.04O and Zn0.96-x Gd0.04Co x O (Co = 0, 0.01, 0.03, 0.04) diluted magnetic semiconductor nanotubes. The influence of gadolinium and cobalt doping on the microstructure, morphology, and optical characteristics of ZnO was investigated, and the Gd doping and Co co-doping of the host ZnO was verified by XRD and EDX. The structural investigation revealed that the addition of gadolinium and cobalt to ZnO reduced crystallinity while maintaining the preferred orientation. The SEM study uncovered that the gadolinium and cobalt dopants did not affect the morphology of the produced nanotubes, which is further confirmed through TEM. In the UV-vis spectra, no defect-related absorption peaks were found. By raising the co-doping content, the crystalline phase of the doped samples was enhanced. It was discovered that the dielectric response and the a.c. electrical conductivity display a significant dependent relationship. With the decreasing frequency and increasing Co co-dopant concentration, the ε r and ε'' values decreased. It was also discovered that the ε r, ε'', and a.c. electrical conductivity increased when doping was present. Above room temperature, co-doped ZnO nanotubes exhibited ferromagnetic properties. The ferromagnetic behaviour increased as Gd (0.03) doping increased. Increasing the Co content decreased the ferromagnetic behaviour. It was observed that Zn0.96-x Gd0.04Co x O (x = 0.03) nanotubes exhibit superior electrical conductivity, magnetic and dielectric characteristics compared to pure ZnO. This high ferromagnetism is typically a result of a magnetic semiconductor that has been diluted. In addition, these nanoparticles are utilized to design spintronic-based applications in the form of thin-films.

Studying Different Operating Conditions on Reverse Osmosis Performance in the Treatment of Wastewater Containing Nickel (II) Ions.

The reverse osmosis performance in removing nickel ions from artificial wastewater was experimentally and mathematically assessed. The impact of temperature, pressure, feed concentration, and feed flow rate on the permeate flux and Ni (II) rejection % were studied. Experiments were conducted using a SEPA CF042 Membrane Test Skid-TFC BW30XFR with applied pressures of 10, 20, 30, and 40 bar and feed concentrations of 25, 50, 100, and 150 ppm with varying operating temperatures of 25, 35, and 45 °C, while the feed flow rate was changed between 2, 3.2, and 4.4 L/min. The permeate flux and the Ni (II) removal % were directly proportional to the feed temperature and operating pressure, but inversely proportional to the feed concentration, where the permeate flux increased by 49% when the temperature was raised from 25 to 45 °C, while the Ni (II) removal % slightly increased by 4%. In addition, the permeate flux increased by 188% and the Ni (II) removal % increased to 95.19% when the pressure was raised from 10 to 40 bar. The feed flow rate, on the other hand, had a negligible influence on the permeate flux and Ni (II) removal %. The temperature correction factor (TCF) was determined to be directly proportional to the feed temperature, but inversely proportional to the operating pressure; nevertheless, the TCF was unaffected either by the feed flow rate or the feed concentration. Based on the experimental data, mathematical models were generated for both the permeate flux and nickel removal %. The results showed that both models matched the experimental data well.

Ni(II) removal using date seed powder biosorbent: Process parameters classification and RSM modeling.

The effects of pH, particle size, adsorbent mass and stirring time on the adsorption efficiency were investigated. The univariate linear regression algorithm was applied on experimental data to rank the most effective parameters on the Ni(II) removal percentage. Response surface method (RSM) was then applied to model and optimize the operating conditions of the removal process. Results revealed that the most effective operation parameters on Ni(II) removal is the solution's pH. It has been concluded that the highest removal of 94.13% is obtained with stirring time of 29.15 min, particle size 137.81 µm, added mass absorbent of 0.346 g and pH of 12.04. Experimental verification showed removal percentage of 93.5% concluding agreement with that obtained by model prediction.Implications: The removal of Ni(II) ions from wastewater utilizing the agricultural waste of date seed powder is dominated by many parameters; solution pH, initial Ni(II) concentration, adsorbent mass, particle size, operational temperature and contact time. This research classifies these parameters to define the ones that significantly impacts the removal process. Modeling of these parameters was then conducted to study the impact of every set on the removal efficiency thus defining the optimum operating conditions. The findings of this study can be used to create optimal operating conditions that are capable of achieving higher removal percentages than are currently available.

Conductive Polymers and Their Nanocomposites as Adsorbents in Environmental Applications.

Proper treatment and disposal of industrial pollutants of all kinds are a global issue that presents significant techno-economical challenges. The presence of pollutants such as heavy metal ions (HMIs) and organic dyes (ODs) in wastewater is considered a significant problem owing to their carcinogenic and toxic nature. Additionally, industrial gaseous pollutants (GPs) are considered to be harmful to human health and may cause various environmental issues such as global warming, acid rain, smog and air pollution, etc. Conductive polymer-based nanomaterials have gained significant interest in recent years, compared with ceramics and metal-based nanomaterials. The objective of this review is to provide detailed insights into different conductive polymers (CPs) and their nanocomposites that are used as adsorbents for environmental remediation applications. The dominant types of CPs that are being used as adsorbent materials include polyaniline (PANI), polypyrrole (Ppy), and polythiophene (PTh). The various adsorption mechanisms proposed for the removal of ODs, HMIs, and other GPs by the different CPs are presented, together with their maximum adsorption capacities, experimental conditions, adsorption, and kinetic models reported.

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents.

This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents-hexane, chloroform, and acetone-with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10-3 to 3.70 × 10-7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.