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| 应用人工网络对化学传感器CaxPb1-xTiO3薄膜的晶格畸变进行评估 | |
| 引用本文： | 刘静波,郝伟达,杜雪岩,巴塞·赛义德,王智民. 应用人工网络对化学传感器CaxPb1-xTiO3薄膜的晶格畸变进行评估[J]. 黑龙江大学自然科学学报, 2011, 28(5): 724-732,736 |
| 作者姓名： | 刘静波 郝伟达 杜雪岩 巴塞·赛义德 王智民 |
| 作者单位： | 1. 美国德克萨斯农业与机械大学化学系,金斯威尔78363 2. 美国德克萨斯农业与机械大学计算机与电子工程系,金斯威尔78363 3. 兰州理工大学材料科学与工程学院,兰州,730050 4. 黑龙江大学化学化工与材料学院,哈尔滨,150080 |
| 基金项目： | Supported by the Developing a High Performance Computing Center Through Acquisition of a PC Cluster for Cross-Disciplinary Research and Education,National Science Foundation of the United State; Major Research Instrumentation Acquisition(CISE0619810); the University Research Council(160315-00005)at Texas A&M University-Kingsville |
| 摘 要： | 利用人工神经网络对化学传感器的晶格畸变进行了深入研究.实验结果表明:传感原件钙掺杂钛酸铅薄膜材料随合成参数的变化而呈现不同的晶格畸变度,该参数通过X射线粉末衍射精细结构确定,并作为人工神经网络的输出变量；合成参数作为输入变量,以MatlabTM作为操作平台,利用三层识别方法对传感元件的纳米结构进行评估与预测.实验结果与... |
| 关 键 词： | 合成参数 四方畸变度 钙钛矿薄膜 人工神经网络 |
Evaluation and prediction of lattice distortion of thin film sensor composed of Ca_xPb_(1-x)TiO_3 using artificial neural network |
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| LIU Jing-bo,HAO Wei-da,DU Xue-yan,BASHIR S,WANG Zhi-min. Evaluation and prediction of lattice distortion of thin film sensor composed of Ca_xPb_(1-x)TiO_3 using artificial neural network[J]. Journal of Natural Science of Heilongjiang University, 2011, 28(5): 724-732,736 | |
| Authors: | LIU Jing-bo HAO Wei-da DU Xue-yan BASHIR S WANG Zhi-min |
| Affiliation: | LIU Jing-bo1,HAO Wei-da2,DU Xue-yan3,BASHIR S1,WANG Zhi-min4,(1.Department of Chemistry,Texas A&M University-Kingsville,Kingsville 78363,USA,2.Department of Computer Science and Electrical Engineering,Texas A&M University,3.School of Materials Science and Engineering,Lanzhou University of Technology,Lanzhou 730050,China,4.School of Chemistry and Materials Science,Heilongjiang University,Harbin 150080,China) |
| Abstract: | An in - depth study on lattice distortion of a chemical sensor composed of calcium doped lead titanate (CaxPb1 -xTiO3 ) thin film is present.The micro- structural lattice distortion (defined as tetragonality,δ) is crucial for sensor's sensitivity and response time.To evaluation and predict the tetragonality,a three -layer artificial neural network (ANN) model is applied,based on experimental results related to the tetragonality values of the thin films determined via X - ray powder diffraction.The fabrication parameters,including heat- treatment temperature,dopant content,and heating rate have been considered as the input parameters,whereas the tetragonality as the output parameter.Function approximation was employed and simulation was implemented on the MatlabTM.The predicted results were compared with experimental results and it was found out that the results obtained from ANN model were accurate in predicting the nano - structural distortion of the thin film.The results showed that ANN is an effective tool in the simulation and prediction of thin - film tetragonality and highly useful compared with traditional trial - error experimental processes.Since predicted data by ANN model were essentially identical to the experimental results,this model can be used to estimate the tetragonality of different thin films for humidity sensors. |
| Keywords: | fabrication parameters tetragonality CaxPb1-xTiO3 thin film artificial neural network function approximation |
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