衍射
粉末衍射
材料科学
微晶
纹理(宇宙学)
结构精修
相(物质)
对分布函数
X射线晶体学
结晶学
分析化学(期刊)
光学
化学
计算机科学
数学
物理
色谱法
冶金
人工智能
有机化学
数学分析
图像(数学)
作者
James A. Kaduk,Simon J. L. Billinge,Robert E. Dinnebier,Nathan Henderson,Ian C. Madsen,Radovan Černý,Matteo Leoni,Luca Lutterotti,Seema Thakral,Daniel Chateigner
标识
DOI:10.1038/s43586-021-00074-7
摘要
Powder diffraction is a non-destructive technique, which is experimentally simple in principle. Because the physics behind diffraction is well understood, an exceptionally large amount of information can be obtained from a single measurement. The positions and relative intensities of the peaks yield a fingerprint that can be used for qualitative phase analysis. Quantitative phase analysis can be obtained by detailed analysis of the intensities. Unit cells can be derived from the peak positions. Crystal structures can be solved using powder diffraction data and refined by the Rietveld method. The peak profiles contain information about crystallite size, strain and nanostructure. Non-idealities in the intensities give information on texture. Abandoning the crystallographic model provides information about local structure, by pair distribution function analysis. For powder diffraction, everything is a sample; the technique is commonly applied to characterize minerals, ceramics, metals and alloys, catalysts, polymers, pharmaceuticals, organic compounds, environmental and forensic samples, among others. The major features of contemporary laboratory powder diffractometers are described. Methods for obtaining suitable powder specimens are summarized. Major applications of qualitative and quantitative phase analysis, structure solution, size/strain/nanostructure analysis using peak profiles, texture analysis and pair distribution function analysis are introduced. Material characterization by powder diffraction gives quantitative and qualitative insights into the phases present in a specimen. This Primer describes the key considerations during powder diffraction analysis, from data collection and specimen preparation to phase identification and structure solution.
科研通智能强力驱动
Strongly Powered by AbleSci AI