Ghaedi M, Ghaedi A M, Ansari A, Mohammadi F, Vafaei A
Department of Chemistry, Yasouj University, Yasouj 75918-74831, Iran.
Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2014 Nov 11;132:639-54. doi: 10.1016/j.saa.2014.04.175. Epub 2014 May 15.
The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g(-1) for Au-NP-AC and 3.84 mg g(-1) for Tamarisk-AC.
使用多元线性回归（MLR）和人工神经网络（ANN）研究了变量，即初始染料浓度、吸附剂用量（克）、搅拌速度（转/分钟）和接触时间（分钟）对负载在活性炭（Au-NP-AC）和柽柳上的金纳米颗粒去除甲基橙（MO）的影响，并通过粒子群优化（PSO）对变量进行了优化。对使用所提出模型获得的结果进行比较，结果表明，对于预测使用Au-NP-AC和柽柳去除甲基橙，ANN模型优于MLR模型。使用最优的ANN模型，测试数据集的决定系数（R2）分别为0.958和0.989；Au-NP-AC和柽柳吸附剂的均方误差（MSE）值分别为0.00082和0.0006。在本研究中，报道了一种通过柽柳合成金纳米颗粒和活性炭的新颖绿色方法。使用扫描电子显微镜（SEM）、透射电子显微镜（TEM）、X射线衍射（XRD）和比表面积分析（BET）等不同技术对该材料进行了表征。研究了Au-NP-AC和柽柳活性炭从水溶液中去除甲基橙的可用性。研究了pH值、初始染料浓度、吸附剂用量（克）和接触时间（分钟）等变量对甲基橙去除的影响。将实验平衡数据拟合到各种等温线模型，如朗缪尔、弗伦德利希、坦普金和杜宾宁-拉杜舍维奇模型，结果表明朗缪尔模型具有适用性。伪一级、伪二级、埃洛维奇和颗粒内扩散等动力学模型表明，二级方程和颗粒内扩散模型控制着吸附过程的动力学。少量的Au-NP-AC和活性炭（分别为0.015克和0.75克）能够在短时间内（Au-NP-AC为20分钟，柽柳活性炭为45分钟）成功去除甲基橙（>98%），Au-NP-AC的吸附容量为161毫克/克，柽柳活性炭的吸附容量为3.84毫克/克。
