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混晶TiO2颗粒尺寸及相转变温度依存模型研究

Study on particle diameter and phase transition temperature dependence model of mixed phase TiO2
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摘要 实验原料及方法对于制备的TiO2粉体的颗粒尺寸和相转变温度具有重要影响.本文搜集了近年来涉及TiO2相转变的相关文献,并探究了其实验所用钛源A、实验方法B对其颗粒尺寸C及相转变温度D的影响.结果表明,实验方法B对颗粒尺寸C的影响更显著,实验所用钛源A对相转变温度D的影响更显著;与相转变温度D相比,钛源A及实验方法B对颗粒尺寸C的影响更显著;通过四层卷积神经网络建立了颗粒尺寸C及相转变温度D依存模型,并计算模型预测相转变温度Dp值与真实相转变温度D值的相对误差Re,除其中一组预测结果异常外,剩余29组的相对误差均不超过10%,最大相对误差为8.79%,预测结果准确率较高. Many investigation show that the raw materials and methods have an important effect on the particle diameter and phase transition temperature of the TiO2 particles.Therefore,this paper has collected relevant literatures related to the phase transition of TiO2 in recent years and explored the effects of titanium source A and experimental method B on its particle diameter C and thus the phase transition temperature D.The results show that the experimental method B has a more significant effect on the particle diameter C,and the titanium source A has a more significant effect on the phase transition temperature D.The effect of titanium source A and experimental method B on particle diameter C is more significant than phase transition temperature D.Subsequently,a four-layer convolutional neural network is used to establish a particle diameter C and phase transition temperature D dependent model and calculate the relative error Re.Except for one group with abnormal prediction results,the relative errors of the remaining 29 groups are not more than 10%,and the maximum relative error is 8.79%.
作者 于成龙 刘航 齐勇 李梦园 宋杰 程航 YU Cheng-long;LIU Hang;QI Yong;LI Meng-yuan;SONG Jie;CHENG Hang(School of Materials Science and Engineering, Shaanxi University of Science & Technology, Xi′an 710021, China;School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi′an 710021, China)
出处 《陕西科技大学学报》 CAS 2020年第4期114-120,共7页 Journal of Shaanxi University of Science & Technology
基金 国家自然科学基金项目(51302159) 陕西科技大学先进能源材料实验室夏季暑期大团队工作创新计划项目(GTWI-2018-02)。
关键词 混晶TiO2 相转变 四层卷积神经网络 mixed phase TiO2 phase transition four-layer convolutional neural network
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