X-ray Diffraction Phase Identification Using Ring Uniformity Clustering
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Solution Overview
Problem
Conventional crystalline phase identification methods using X-ray diffraction data often fail to accurately distinguish between crystalline phases due to the lack of consideration for the uniformity of diffraction patterns in the circumferential direction, leading to incorrect identification of candidates.
Innovation Solution
The method involves detecting peak positions and intensities, creating circumferential angle versus intensity data, grouping diffraction patterns based on uniformity, and using ring characteristic factors such as intensity range, standard variance, and coefficient of variation to cluster patterns, allowing for precise identification of crystalline phases by matching them with database entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional crystalline phase identification methods are used without considering circumferential uniformity, then the identification process is simple and fast, but the identification precision deteriorates and incorrect candidates are listed
Solution Approach 1:
The patent segments the diffraction pattern analysis by dividing it into multiple circumferential regions and evaluating intensity uniformity in each region. This segmentation allows the system to detect non-uniformities that indicate incorrect phase identification while maintaining a systematic analysis approach that doesn't overly complicate the overall process.
Solution Approach 2:
The patent introduces a new dimension of analysis by evaluating the circumferential uniformity of diffraction patterns. Instead of only analyzing peak positions and intensities, the method adds the dimension of spatial distribution uniformity around the diffraction ring, which provides additional information for distinguishing correct from incorrect phase candidates.
2Reliability
If diffraction patterns with different uniformities are grouped together, then the search process is faster, but the reliability of identification deteriorates due to incorrect candidate listing
Solution Approach 1:
The patent performs preliminary evaluation of circumferential uniformity for each diffraction pattern before the main phase identification search. By pre-categorizing patterns based on their uniformity characteristics, the system can quickly filter and compare only compatible patterns during the search process, maintaining high speed while ensuring reliability through pre-established uniformity matching.
3Measurement precision
If circumferential uniformity analysis is performed for each diffraction pattern, then identification precision is improved, but the analysis time increases
Solution Approach 1:
The patent applies local quality analysis by evaluating circumferential uniformity at specific regions and points around the diffraction pattern rather than uniformly analyzing every point. This localized approach focuses computational resources on critical regions that most indicate phase identification accuracy, reducing overall analysis time while maintaining precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the precision of crystalline phase identification by accurately grouping diffraction patterns based on their uniformity, reducing the likelihood of misidentifying crystalline phases and improving analysis accuracy.
Implementation Method 1
X-rays diffracted by a lattice plane at a diffraction angle of 2θ proceed along the generating line of a cone in which the half apex angle is 2θ when 2θ 90°. In other words, X-rays diffracted by a sample composed of powdery crystal or a polycrystalline substance form numerous cones having different central angles.
Implementation Method 2
When such X-rays are received in the detection surface of an X-ray detector, a concentric circular diffraction pattern is obtained. This diffraction pattern is referred to as Debye-Scherrer rings.
Data Source
Figure 1A
Figure 1B
Figure 2A
AI summary
A crystalline phase contained in a sample is identified, from X-ray diffraction data of the sample which contain data of a plurality of ring-shaped diffraction patterns, using a database in which are registered data related to peak positions and peak intensity ratios of X-ray diffraction patterns for a plurality of crystalline phases. Peak positions and peak intensities for a plurality of the diffraction patterns are detected from the X-ray diffraction data (step 102), and the circumferential angle versus intensity data of the diffraction patterns is created (step 103). The diffraction patterns are grouped into a plurality of clusters on the basis of the circumferential angle versus intensity data (step 105). Crystalline phase candidates contained in the sample are searched from the database on the basis of sets of ratios of peak positions and peak intensities of the diffraction patterns grouped into the same cluster (step 106).