This research presents the possibility of using banana peel (arising from agricultural production waste) as biosorbent for removal of copper from simulated aqueous solution. Batch sorption experiments were performed as a function of pH, sorbent dose, and contact time. The optimal pH value of Copper (II) removal by banana peel was 6. The amount of sorbed metal ions was calculated as 52.632 mg/g. Sorption kinetic data were tested using pseudo-first order, and pseudo-second order models. Kinetic studies showed that the sorption followed a pseudo second order reaction due to the high correlation coefficient and the agreement between the experimental and calculated values of qe. Thermodynamic parameters such as enthalpy change (ΔH°), entropy change (ΔS°) and Gibbs free energy change (ΔG°) were also investigated. Free energy change showed that biosorption of Cu (II) was spontaneous and nature endothermic at all studied temperatures (25–45 °C).
A simple ,accurate and sensitive spectrophotometric method has been developed the determination of Cobalt(II) and Cupper (II) .The method is based on the chelation of Co(II) and Cu(II) ions with 4-(4´-pyrazolon azo) -2-Naphthol(APAN) in aqueous medium . The complexes have a maximum absorption at (513) and (506) nm and ? max 0.531×10 4 and 0.12×10 5 L.mol -1.cm -1 for Co(II) and Cu(II) respectively .The reagent and two complexes have been prepared in ethanolic solution.The stoichiometry of both complexes were found to be 1:2 (metal :legend) .The effects of various cations and anions on Co(II) and Cu(II) determination have been investigated .The stability constants and standard deviations for Co(II) and Cu(II) 0.291 x107 ,0.909X108 L.mol
... Show MoreThe present article discusses the synthesis of tetradentate Schiff base complexes formed by the condensation reaction of 2-hydroxy benzaldehyde and phthalohydrazide. The ligand (LH2) was detected using FT-IR spectra, 1H, 13C-NMR, UV-Vis spectroscopy, elemental microanalysis CHN, and mass spectrometry. The obtained solid complexes have been assessed using physicochemical and spectroscopic techniques, including UV-Vis, FT-IR, nuclear magnetic resonance (1H-NMR, 13C-NMR), mass spectrometry, thermal gravimetric analysis (TGA), and atomic absorption, in addition to complex conductivity and magnetic moment measurements. The infrared results demonstrated that ligands functioning as tetradentate ligands are chelated to metal ions via the ph
... Show MoreThe main aim of this paper is to study how the different estimators of the two unknown parameters (shape and scale parameter) of a generalized exponential distribution behave for different sample sizes and for different parameter values. In particular,
. Maximum Likelihood, Percentile and Ordinary Least Square estimators had been implemented for different sample sizes (small, medium, and large) and assumed several contrasts initial values for the two parameters. Two indicators of performance Mean Square Error and Mean Percentile Error were used and the comparisons were carried out between different methods of estimation by using monte carlo simulation technique .. It was obse
... Show MoreThe main aim of this paper is to study how the different estimators of the two unknown parameters (shape and scale parameter) of a generalized exponential distribution behave for different sample sizes and for different parameter values. In particular,
. Maximum Likelihood, Percentile and Ordinary Least Square estimators had been implemented for different sample sizes (small, medium, and large) and assumed several contrasts initial values for the two parameters. Two indicators of performance Mean Square Error and Mean Percentile Error were used and the comparisons were carried out between different methods of estimation by using monte carlo simulation techniq
... Show MoreThe penalized least square method is a popular method to deal with high dimensional data ,where the number of explanatory variables is large than the sample size . The properties of penalized least square method are given high prediction accuracy and making estimation and variables selection
At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and
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