02 دی 1403
حسين رعنايي

حسین رعنایی

مرتبه علمی: دانشیار
نشانی: دانشکده علوم و فناوری نانو و زیستی - گروه فیزیک
تحصیلات: دکترای تخصصی / فیزیک
تلفن: 07731223310
دانشکده: دانشکده علوم و فناوری نانو و زیستی

مشخصات پژوهش

عنوان Efficient removal of zinc ion pollution by carbon-based magnetic alloy: Experimental, theoretical modeling and DFT studies
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Zinc ion adsorption, Graphite-based alloy, Removal mechanism, Isotherm, DFT calculations, Artificial neural network
مجله INORGANIC CHEMISTRY COMMUNICATIONS
شناسه DOI https://doi.org/10.1016/j.inoche.2024.113735
پژوهشگران سعید زارعی (مجقق پسادکتری) (نفر اول) ، حسین رعنایی (نفر دوم) ، وحید محمد حسینی (نفر سوم) ، سعید کمالی (نفر چهارم)

چکیده

In this study, we prepared a graphite-iron alloy using the mechanical alloying method for the adsorption of zinc ions. The resulting compounds were characterized using various techniques, including scanning electron microscopy, energy dispersive x-ray spectroscopy, x-ray diffraction, x-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and vibrating sample magnetometer. Experimental results confirmed effective zinc adsorption, with ten isotherm models evaluated; the Toth model provided the best fit, yielding a maximum adsorption capacity of 729.7 mg/g. We employed response surface methodology (RSM) and artificial neural network genetic algorithms (ANN-GA) to identify optimal conditions for the adsorption process of Zn (II), achieving maximum removal efficiencies of 72.5 % and 72.49 %, respectively. The optimal adsorption parameters were determined to be a pH of 2.5, a temperature of 56 ◦C, and a contact time of 47.5 min. Additionally, we investigated graphite and a graphite-iron alloy, along with their electronic interactions with zinc (II) using density functional theory (DFT). This approach assessed electron donating and accepting capabilities, natural bond orbital analysis, and various thermodynamic and kinetic processes, with simulation results aligning well with experimental data.