The ligand 2-[1-(1H-indol-3-yl)ethylimino) methyl]naphthalene-1-ol, derived from 1-hydroxy-2-naphthaldehyde and 2-(1H-indol-3-yl)ethylamine, was used to produce a new sequence of metal ions complexes. Thus ligand reactions with NiCl₂.6H₂O, PdCl₂, FeCl_3.6H₂O and H₂PtCl₆.6H₂O were sequentially made to collect mono-nuclear Ni(II), Pd(II), Fe (III), and Pt(IV). (IR or FTIR), Ultraviolet Reflective (UV–visible), Mass Spectra analysis, Bohr-magnetic (B.M.), metal content, chloride content and molar conductivity have been the defining features of the composites. The Fe(III) and Pt(IV) complexes have octahedral geometries, while the Ni(II) complex has tetra

Because of the conservation problems causes by the existence of water hyacinth (W.H) as an watery plant in water bodies of Iraq, our study aimed to make  use of (W.H) by isolation of microcrystalline cellulose, and a new method of preparation of Nano crystalline cellulose. Microcrystalline cellulose was produced using base bleaching method by sodium hypochlorite [NaOCl] to remove unorganized region of cellulose and lignin to create particles comprising of micro crystal and preparing of Nano crystalline cellulose from microcrystalline cellulose by acid hydrolysis and ultrasonic treatment. The Nano crystalline and microcrystalline cellulose characterized by AFM, FTIR, XRD and TGA. FT-IR spectra of microcrystalline cellules and Nano cr

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

tA novel synthesis procedure is presented for preparing triethanolamine-treated graphene nanoplatelets(TEA-GNPs) with different specific areas (SSAs). Using ultrasonication, the covalently functionalizedTEA-GNPs with different weight concentrations and SSAs were dispersed in distilled water to prepareTEA-GNPs nanofluids. A simple direct coupling of GNPs with TEA molecules is implemented to synthesizestable water-based nanofluids. The effectiveness of the functionalization procedure was validated by thecharacterization and morphology tests, i.e., FTIR, Raman spectroscopy, EDS, and TEM. Thermal conduc-tivity, dispersion stability, and rheological properties were investigated. Using UV–vis spectrometer, ahighest dispersion stability of 0.876

Manganese dioxide rotating cylinder electrode prepared by anodic deposition on a graphite substrate using MnSO4 solution in the presence of 0.918 M of H2SO4. The influence of different operational parameters (MnSO4 concentration, current density, time, and rotation speed) on the structure, and morphology of MnO2 deposit film was examined widely. The structure and crystal size determined by X-ray diffraction (XRD), the morphology examined by scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques. The γ-MnO2 obtained as the main product of the deposition process. It found that the four parameters have a significant influence on the structure, morphology, and roughness of the prepared MnO2 deposit. The crystal size in

The cost-effective removal of heavy metal ions represents a significant challenge in environmental science. In this study, we developed a straightforward and efficient reusable adsorbent by amalgamating chitosan and vermiculite (forming the CSVT composite), and comprehensively investigated its selective adsorption mechanism. Different techniques, such as Fourier-transform infrared spectroscopy (FTIR), zeta potential analysis, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer, Emmett, Teller (BET) analysis were employed for this purpose. The prepared CSVT composite exhibited a larger surface area and higher mesoporosity increasing from 1.9 to 17.24 m2/g compared to pristine chitosan. The adsorption capabilities of the