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An Improved High-Throughput Data Processing Based on Combinatorial Materials Chip Approach for Rapid Construction of Fe-Cr-Ni Composition-Phase Map.
ACS Combinatorial Science Pub Date : 2019-11-12 , DOI: 10.1021/acscombsci.9b00149 Zhaoyang Zhao 1 , Ying Jin 1 , Peng Shi 1 , Yanpeng Xue 1 , Bingbing Zhao 2 , Yanpeng Zhang 3 , Feifei Huang 1 , Peng Bi 1 , Qingrui Wang 1
ACS Combinatorial Science Pub Date : 2019-11-12 , DOI: 10.1021/acscombsci.9b00149 Zhaoyang Zhao 1 , Ying Jin 1 , Peng Shi 1 , Yanpeng Xue 1 , Bingbing Zhao 2 , Yanpeng Zhang 3 , Feifei Huang 1 , Peng Bi 1 , Qingrui Wang 1
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The combinatorial materials chip approach is vastly superior to the conventional one that characterizes one sample at a time in the efficiency of composition-phase map construction. However, the resolution of its high-throughput characterization and the correct rate of automated composition-phase mapping are often affected by inherent experimental limitations and imperfect automated analyses, respectively. Therefore, effective data preprocessing and refined automated analysis methods are required to automatically process huge amounts of experiment data to score a higher correct rate. In this work, the pixel-by-pixel structural and compositional characterization of the Fe-Cr-Ni combinatorial materials chip annealed at 750 °C was performed by microbeam X-ray at a synchrotron light source and by electron probe microanalysis, respectively. The severe baseline drift and system noise in the X-ray diffraction patterns were successfully eliminated by the three-step automated preprocessing (baseline drift removal, noise elimination, and baseline correction) proposed, which was beneficial to the subsequent quantitative analysis of the patterns. Through the injection of human experience, hierarchy clustering analyses, based on three dissimilarity measures (the cosine, Pearson correlation coefficient, and Jenson-Shannon divergence), were further accelerated by the simplified vectorization of the preprocessed X-ray diffraction patterns. As a result, a correct rate of 91.15% was reached for the whole map built automatically in comparison with the one constructed manually, which confirmed that the present data processing could assist humans to improve and expedite the processing of X-ray diffraction patterns and was feasible for composition-phase mapping. The constructed maps were generally consistent with the corresponding isothermal section of the Fe-Cr-Ni ternary alloy system in the ASM Alloy Phase Diagram Database except the inexistence of the σ phase under insufficient annealing.
中文翻译：
一种基于组合材料芯片方法的高通量数据处理方法，用于快速构建Fe-Cr-Ni组成-相图。
组合材料芯片方法大大优于传统方法，后者在成分相图构建效率上一次可表征一个样品。但是，其高通量表征的分辨率和自动化的组成相映射的正确率通常分别受固有的实验局限性和不完善的自动化分析的影响。因此，需要有效的数据预处理和完善的自动化分析方法来自动处理大量的实验数据，以获得更高的正确率。在这项工作中，分别在同步加速器光源下通过微束X射线和电子探针显微分析对在750°C退火的Fe-Cr-Ni组合材料芯片进行了逐像素的结构和成分表征。通过建议的三步自动预处理（基线漂移消除，噪声消除和基线校正）成功消除了X射线衍射图中严重的基线漂移和系统噪声，这有助于后续对模式进行定量分析。通过注入人类经验，基于预处理的X射线衍射图样的简化矢量化，基于三个相异性度量（余弦，皮尔逊相关系数和詹森-香农散度）的层次聚类分析得到了进一步的加速。结果，与手动构建的地图相比，自动构建的整个地图的正确率为91.15％，这证实了目前的数据处理可以帮助人类改善和加快X射线衍射图样的处理，并且对于组成相图的绘制是可行的。构造图通常与ASM合金相图数据库中Fe-Cr-Ni三元合金系统的相应等温截面一致，只是在退火不足的情况下不存在σ相。
更新日期：2019-11-13
中文翻译：
一种基于组合材料芯片方法的高通量数据处理方法，用于快速构建Fe-Cr-Ni组成-相图。
组合材料芯片方法大大优于传统方法，后者在成分相图构建效率上一次可表征一个样品。但是，其高通量表征的分辨率和自动化的组成相映射的正确率通常分别受固有的实验局限性和不完善的自动化分析的影响。因此，需要有效的数据预处理和完善的自动化分析方法来自动处理大量的实验数据，以获得更高的正确率。在这项工作中，分别在同步加速器光源下通过微束X射线和电子探针显微分析对在750°C退火的Fe-Cr-Ni组合材料芯片进行了逐像素的结构和成分表征。通过建议的三步自动预处理（基线漂移消除，噪声消除和基线校正）成功消除了X射线衍射图中严重的基线漂移和系统噪声，这有助于后续对模式进行定量分析。通过注入人类经验，基于预处理的X射线衍射图样的简化矢量化，基于三个相异性度量（余弦，皮尔逊相关系数和詹森-香农散度）的层次聚类分析得到了进一步的加速。结果，与手动构建的地图相比，自动构建的整个地图的正确率为91.15％，这证实了目前的数据处理可以帮助人类改善和加快X射线衍射图样的处理，并且对于组成相图的绘制是可行的。构造图通常与ASM合金相图数据库中Fe-Cr-Ni三元合金系统的相应等温截面一致，只是在退火不足的情况下不存在σ相。
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