Fan Mingyi, Li Tongjun, Hu Jiwei, Cao Rensheng, Wei Xionghui, Shi Xuedan, Ruan Wenqian
Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China.
Department of Applied Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Materials (Basel). 2017 May 17;10(5):544. doi: 10.3390/ma10050544.
Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R² value than the pseudo-first-order model.
本研究采用化学沉积法合成了还原氧化石墨烯负载的纳米零价铁（nZVI/rGO）复合材料，然后通过傅里叶变换红外光谱（FTIR）和X射线光电子能谱（XPS）等多种方法对其进行了表征。制备的nZVI/rGO复合材料用于在不同初始Cd（II）浓度、初始pH值、接触时间和操作温度下，以分批模式从水溶液中去除Cd（II）。采用响应面法（RSM）和与遗传算法相结合的人工神经网络（ANN-GA）对Cd（II）的去除效率进行建模，并对四个去除过程变量进行优化。RSM模型和ANN-GA模型的预测误差平均值分别为6.47%和1.08%。虽然两个模型在预测Cd（II）的去除效率方面都被证明是可靠的，但发现ANN-GA模型比RSM模型更准确。此外，将实验数据拟合到朗缪尔、弗伦德里希和杜宾宁-拉杜舍维奇（D-R）等温线。结果发现，Cd（II）的吸附最符合朗缪尔等温线。对热力学参数的考察表明，去除过程本质上是自发的且放热的。此外，与准一级模型相比，准二级模型能以更好的R²值更好地描述Cd（II）去除的动力学。
