In this study, a low-cost biosorbent, dead mushroom biomass (DMB) granules, was used for investigating the optimum conditions of Pb(II), Cu(II), and Ni(II) biosorption from aqueous solutions. Various physicochemical parameters, such as initial metal ion concentration, equilibrium time, pH value, agitation speed, particles diameter, and adsorbent dosage, were studied. Five mathematical models describing the biosorption equilibrium and isotherm constants were tested to find the maximum uptake capacities: Langmuir, Freundlich, Redlich-Peterson, Sips, and Khan models. The best fit to the Pb(II) and Ni(II) biosorption results was obtained by Langmuir model with maximum uptake capacities of 44.67 and 29.17 mg/g for these two ions, respectively, w

This work deals with the effect of adding aluminum nanoparticles on the mechanical properties, micro-hardness and porosity of memory-shape alloys (Cu-Al-Ni). These alloys have wide applications in various industrial fields such as (high damping compounds and self-lubricating applications). The samples are manufactured using the powder metallurgy method, which involved pressing in only one direction and sintered in a furnace surrounded by an inert gas. Four percentages (0%, 5%, 10%, and 15%) of aluminum nanoparticles were fabricated, which depended on the weight of aluminum powder (13%) in the sample under study. To find out which phase is responsible for the reliability of the formation of this type of alloy and its porosity, X-ray diffr

Carbon dioxide (CO2) capture and storage is a critical issue for mitigating climate change. Porous aromatic Schiff base complexes have emerged as a promising class of materials for CO2 capture due to their high surface area, porosity, and stability. In this study, we investigate the potential of Schiff base complexes as an effective media for CO2 storage. We review the synthesis and characterization of porous aromatic Schiff bases materials complexes and examine their CO2 sorption properties. We find that Schiff base complexes exhibit high CO2 adsorption capacity and selectivity, making them a promising candidate for use in carbon capture applications. Moreover, we investigate the effect of various parameters such as temperature, and pressu

Gelatin is a valuable substance that is known to have a complex 3D structure, but it has limited adsorption efficiency, which limit its application at physiological temperature. In this paper, gelatin was improved by the addition of glutaraldehyde (GTA) to enhance its ability to adsorb dye in aqueous solution. The adsorption experiments were conducted under different conditions, such as the amount of adsorbent, the concentration of the dye, and the temperature. The study found that the qe by GTA-GE (4.978 to 23.056 mg/g) and GE (4.8 to 21.333 mg/g) increased with the increase of initial dye concentrations but decreased with the amount of adsorbent. The parameters at equilibrium were at a pH of 4 and a dose of adsorbent of 100 mg in

The involvement of maxillofacial tissues in SARS‐CoV‐2 infections ranges from mild dysgeusia to life‐threatening tissue necrosis, as seen in SARS‐CoV‐2‐associated mucormycosis. Angiotensin‐converting enzyme 2 (ACE2) which functions as a receptor for SARS‐CoV‐2 was reported in the epithelial surfaces of the oral and nasal cavities; however, a complete understanding of the expression patterns in deep oral and maxillofacial tissues is still lacking.

Silicon nitride nanostructures were prepared by reactive sputtering technique using silicon targets with different types of electrical conductivity (n-type and p-type) and Ar:N2 gas mixing ratio of 70:30. The optical microscopy and spectroscopic characteristics of these films were determined in order to introduce the effect of target conductivity type on these characteristics. The results showed that using p-type silicon target would produce Si3N4 films with lower tendency to adsorb water vapor and other constituents of the atmospheric air, higher absorbance in the visible range 400-700nm, and lower variation in the energy band gap with film thickness than the Si3N4 films prepared from n-type silicon target.

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue