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Application of an Augmentation Method to MCR-ALS Analysis for XAFS and Raman Data Matrices in the Structural Change of Isopolymolybdates

机译:增强法在XAFS和拉曼数据矩阵中的应用分析在异聚族钼酸盐结构变化中的应用

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We measured X-ray absorption fine structure (XAFS) and Raman spectra of isopolymolybdates(VI) in highly concentrated HNO_(3) solution (0.15 – 4.0 M), which change their geometries depending on the acid concentration, and performed the simultaneous resolution of the XAFS and Raman data using a multivariate curve resolution by alternating least-squares (MCR-ALS) analysis. In iterative ALS optimization, initial data matrices were prepared by two different methods. For low sensitivity of the XAFS spectra to the geometrical change of the isopolymolybdates, the MCR-ALS result of single XAFS data matrix shows a large dependence on the preparation method of the initial data matrices. This problem is improved by the simultaneous resolution of the XAFS and Raman data: the MCR-ALS result of an augmented matrix of these data has little dependence on the initial data matrices. This indicates that the augmentation method effectively improves the rotation ambiguities in the MCR-ALS analysis of the XAFS data.
机译:在高浓HNO_(3)溶液(0.15-4)溶液(0.15-4)溶液(0.15-4.0M)中测量X射线吸收细结构(XAF)和拉曼光谱,这取决于酸浓度,并进行同时分辨率改变它们的几何通过交替最小二乘(MCR-ALS)分析,使用多变量曲线分辨率的XAF和拉曼数据。在迭代ALS优化中,通过两种不同的方法制备初始数据矩阵。对于XAFS光谱对Isopolymolbdate的几何变化的低灵敏度,单XAFS数据矩阵的MCR-ALS结果显示了对初始数据矩阵的准备方法的大依赖性。通过同时解析XAFS和拉曼数据的同时解决这个问题:这些数据的增强矩阵的MCR-ALS结果几乎没有对初始数据矩阵的依赖性。这表明增强方法有效地改善了XAFS数据的MCR-ALS分析中的旋转模糊。

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