title: The performance of nanorods material as adsorbent for removal of azo dyes and heavy metal ions: Application of ultrasound wave, optimization and modeling.
abstract: The present research is focused on the synthesis and characterization of zinc (II) oxide nanorods loaded on activated carbon (ZnO-NRs-AC) to prepare an outstanding adsorbent for the simultaneous adsorption of heavy metals and dyes as hazardous pollutant using ultrasound energy. The adsorbent was identified by Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), Energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) analysis. The individual effects and possible interactions between the most effective variables including initial metal ions (Cd2+and Co2+) and azo dyes (methylene blue (MB) and crystal violet (CV)) concentration, adsorbent dosage and ultrasonic time on the responses were investigated by response surface methodology (RSM) and optimum conditions was fixed at Cd2+, Co2+, MB and CV concentrations were 25, 24, 18 and 14mgL-1, respectively, 0.025g of ZnO-NRs-AC and 5.1min sonication to achieve maximum removal percentage (>97.0%) for targets compounds. The artificial neural network (ANN) model was applied for prediction of data with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 14 neurons. The minimum mean squared error (MSE) of 0.9646, 0.0402 and 0.0753 with high determination coefficient (R2) of 0.9996, 0.9991 and 0.9999 for train, test and validation, respectively, were able to predict and model the adsorption process. The results of examination of the time on experimental adsorption data and their subsequent fitting reveal applicability of pseudo-second-order and intraparticle diffusion model. The experimental equilibrium data was analyzed by Langmuir, Freundlich, Temkin and D-R isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 97.1, 92.6, 83.9 and 81.6mgg-1for Cd+2, Co+2ions, MB and CV dyes, respectively.
authors: Department of Chemistry, Yasouj University, Yasouj 75918-74831, Iran.
authors: Ebrahim Alipanahpour
authors: EA
authors: Dil
authors: Department of Chemistry, Yasouj University, Yasouj 75918-74831, Iran. Electronic address: m_ghaedi@mail.yu.ac.ir.
authors: Mehrorang
authors: M
authors: Ghaedi
authors: Department of Chemistry, Yasouj University, Yasouj 75918-74831, Iran.
authors: Arash
authors: A
authors: Asfaram
keywords: Artificial neural network; Dyes; Heavy metals; Response surface methodology; Ultrasonic assisted adsorption; ZnO-NRs-AC