The cost-effective carbon cross-linked Y zeolite nanocrystals composite (NYC) was prepared using an eco-friendly substrate prepared from bio-waste and organic adhesive at intermediate conditions. The green synthesis method dependent in this study assures using chemically harmless compounds to ensure homogeneous distribution of zeolite over porous carbon. The greenly prepared cross-linked composite was extensively characterized using Fourier transform infrared, nitrogen adsorption/desorption, Field emission scanning electron microscope, Dispersive analysis by X-ray, Thermogravimetric analysis, and X-ray diffraction. NYC had a surface area of 176.44 m2/g, and a pore volume of 0.0573 cm3/g. NYC had a multi-function nature, sustained at a long-exposure time during the adsorption process of methyl violet dye (MV) from aqueous solutions and achieved higher removal at normal temperature and pH. The Halsey and the Langmuir models were the most appropriate models for representing the equilibrium data with a maximum adsorption capacity of 108.7 mg/g. The kinetic studies showed that the pseudo-second-order kinetics model and Elovich model were the most suitable models to describe the experimental data which indicated the MV adsorption by NYC has a chemical nature. Also, the interpretation of data by the Boyd model demonstrated that the adsorption process of MV was determined by both film diffusion and intra-particle diffusion. The adsorption process of MV by NYC is spontaneous, feasible, and exothermic. The regeneration performance showed that the NYC can be easily regenerated and reused with keeping an acceptable performance until the fourth cycle. Eventually, this study confirmed that the greenly prepared composite can serve as an adorable adsorbent for the removal of cationic dyes such as methyl violet under mild conditions.
Abstract This research investigates how activated carbon (AC) was synthesized from potato peel waste (PPW). Different ACs were synthesized under the atmosphere's conditions during carbonation via two activation methods: first, chemical activation, and second, carbon dioxide-physical activation. The influence of the drying period on the preparation of the precursor and the methods of activation were investigated. The specific surface area and pore volume of the activated carbon were estimated using the Brunauer–Emmett–Teller method. The AC produced using physical activation had a surface area as high as 1210 m2/g with a pore volume of 0.37 cm3/g, whereas the chemical activation had a surface area of 1210 m2/g with a pore volume of 0.34 c
... Show Morethis paper contains preparation of Active carbon surface (AC) from pro so millet grain husks and Loading and activating by Iron oxide and hydrogen peroxide sequentially to obtain surface (ACIPE). The changes of previous processes on Active carbon surface were diagnosed by Fourier transform infrared spectroscopy (FTIR) and Scanning electron microscopy ( SEM ). These surfaces (AC and ACIPE ) were using as adsorbent for removing of congo red dye from aqueous solutions under certain conditions through batch system. More than one kinetic model was applied to congo red dye adsorption process and it was found that the most kinetic model applied to it is a model ( pseudo second order model).
New complexes have been prepared from the new ligand [2,2′‐(5,5‐dimethylcyclohexane‐1,3‐diylidene)bis(azan‐1‐yl‐1‐ylidene)dibenzoic acid] derived from 5,5‐dimethylcyclohexane‐1,3‐dione and 2‐aminobenzoic acid. Accordingly, its mono and binuclear Mn(II), Co(II), Cu(II), Zn(II), and Cd(II) complexes were prepared. The prepared components have been characterized by various spectroscopic techniques and elemental analysis. The thermal stability of the ligand and its complexes were performed by TGA. It was found that all the complexes have excellent thermal stability and do not contain water molecules within their structure, but the ligand has little stability. Additionally, theor
Background: The rapid integration of Artificial Intelligence (AI) into healthcare necessitates that nursing education evolves to equip students with essential technological competencies. Objectives: To explore pediatric nursing students' perceptions of AI in nursing and analyze associations with sociodemographic factors and prior AI knowledge. Methods: A descriptive cross-sectional study was conducted from December 2024 to March 2025 across five universities in Baghdad. A non-probability sample of 500 pediatric nursing students completed the Shinners Artificial Intelligence Perception (SAIP) tool. Data were analyzed using descriptive statistics and inferential comparisons (t-tests/ANOVA) via SPSS. Results: Participants had a mean ag
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