人工神经网络(ANN)在评价SiO2掺杂TiO2薄膜光催化性能中的适用性研究

E. Rahmani, D. Jafari, A. Ahmadpour, M. Zebarjad, Hossein Rahmani
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引用次数: 0

摘要

采用溶胶-凝胶法在玻璃基质上制备了具有高光催化活性的TiO2和TiO2:SiO2纳米晶薄膜。然后对薄膜进行500℃的高温处理,使TiO2晶体生长。然后将TiO2:SiO2薄膜与10mg的水溶液接触。甲基橙(MO)的L -1),并在紫外线下辐照。所制备的膜具有较高的光催化活性。本研究采用人工神经网络(ANN)研究了TiO2晶体的光催化活性。这是通过预测不同SiO2浓度下MO的浓度和降解时间来实现的。为了进行建模,本文使用了多层感知器(MLP)网络,其学习算法为Levenberg-Marquardt (LM)。模拟结果表明,模拟结果与实验数据吻合良好。值得注意的是,在目前的工作中,研究了在最近的论文和专利中应用的纳米晶薄膜的制备方法和光催化性能的测定以及这些过程的建模。
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Studies on the Applicability of Artificial Neural Network (ANN) in Evaluation of Photocatalytic Performance of TiO2 thin Film Doped by SiO2
Nanocrystalline films of TiO2 and TiO2:SiO2 with high photocatalytic activity were prepared on glass sub- strates by the application of sol-gel method. Then the films were subjected to a high temperature treatment at 500˚C, which resulted in growth of TiO2 crystals. Afterwards the TiO2:SiO2 films were in contact with an aqueous solution (10 mg.L -1 ) of methyl orange (MO) and irradiated under UV. The resulted films showed a high photocatalytic activity. In the current study the photocatalytic activity of TiO2 crystals was studied by an Artificial Neural Network (ANN). This was achieved by predicting the concentration of MO in various values of SiO2 concentration and time of degradation. In order to perform the modeling, Multi-layer Perceptron (MLP) network was used in this work, with its learning algorithm being Levenberg-Marquardt (LM). The outcome of modeling showed that there was an excellent agreement between the results of simulation and the data obtained from the experiments. It is worth noting that in the current work, the methods applied in recent papers and patents for the preparation of nanocrystalline films and determination of their photocatalytic perform- ance and also modeling of such processes have been studied.
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