X-ray Diffraction Techniques for Mineral Characterization: A Review for Engineers of the Fundamentals, Applications, and Research Directions

X-ray diffraction (XRD) is an important and widely used material characterization technique. With the recent development in material science technology and understanding, various new materials are being developed, which requires upgrading the existing analytical techniques such that emerging intricate problems can be solved. Although XRD is a well-established non-destructive technique, it still requires further improvements in its characterization capabilities, especially when dealing with complex mineral structures. The present review conducts comprehensive discussions on atomic crystal structure, XRD principle, its applications, uncertainty during XRD analysis, and required safety precautions. The future research directions, especially the use of artificial intelligence and machine learning tools, for improving the effectiveness and accuracy of the XRD technique, are discussed for mineral characterization. The topics covered include how XRD patterns can be utilized for a thorough understanding of the crystalline structure, size, and orientation, dislocation density, phase identification, quantification, and transformation, information about lattice parameters, residual stress, and strain, and thermal expansion coefficient of materials. All these important discussions on XRD analysis for mineral characterization are compiled in this comprehensive review, so that it can benefit specialists and engineers in the chemical, mining, iron, metallurgy, and steel industries.

Automated summary

X-ray diffraction (XRD) is an important and widely used material characterization technique, but further improvements are needed in its characterization capabilities, especially for complex mineral structures. This review comprehensively discusses the principles, applications, uncertainties, and safety precautions of XRD, and explores the future research directions of using artificial intelligence and machine learning tools to enhance the effectiveness and accuracy of XRD technique.