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使用蒙脱石负载合成的氧化锌纳米颗粒对分散染料进行光催化去除的人工神经网络建模。

Artificial neural network modeling of photocatalytic removal of a disperse dye using synthesized of ZnO nanoparticles on montmorillonite.

作者信息

Kıranşan Murat, Khataee Alireza, Karaca Semra, Sheydaei Mohsen

机构信息

Department of Chemistry, Faculty of Science, Atatürk University, 25240 Erzurum, Turkey.

Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2015 Apr 5;140:465-73. doi: 10.1016/j.saa.2014.12.100. Epub 2015 Jan 14.

DOI:10.1016/j.saa.2014.12.100
PMID:25638428
Abstract

In this study, the photocatalytic ability of ZnO/Montmorilonite (ZnO/MMT) nanocomposite under UV-A, UV-B and UV-C radiation was investigated. ZnO nanoparticles were synthesized on the surface of MMT and used as photocatalyst in decolorization of Disperse Red 54 (DR54) solution. Synthesized nanocomposite was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) techniques and nitrogen adsorption/desorption isotherms curves. The average width of synthesized ZnO particles is in the range of 30-45 nm. Effect of UV light regions, initial dye concentration, initial dosage of nanocomposite, and reusability of catalyst was studied on decolorization efficiency. The highest decolorization efficiency was achieved under UV-C radiation. A three-layered feed forward back propagation artificial neural network model was developed to predict the photocatalysis of DR54 under UV-C radiation. According to ANN model the ZnO/MMT dosage with a relative importance of 49.21% is the most influential parameter in the photocatalytic decolorization process.

摘要

在本研究中,研究了ZnO/蒙脱石(ZnO/MMT)纳米复合材料在UV-A、UV-B和UV-C辐射下的光催化能力。ZnO纳米颗粒在蒙脱石表面合成,并用作光催化剂对分散红54(DR54)溶液进行脱色。通过X射线衍射(XRD)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)技术和氮吸附/脱附等温线对合成的纳米复合材料进行了表征。合成的ZnO颗粒的平均宽度在30-45nm范围内。研究了紫外光区域、初始染料浓度、纳米复合材料的初始用量以及催化剂的可重复使用性对脱色效率的影响。在UV-C辐射下实现了最高的脱色效率。建立了一个三层前馈反向传播人工神经网络模型,以预测DR54在UV-C辐射下的光催化作用。根据人工神经网络模型,相对重要性为49.21%的ZnO/MMT用量是光催化脱色过程中最具影响力的参数。

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