Phase identification and analysis

Each powder diffraction pattern is characterized by a unique distribution of both positions and intensities of Bragg peaks, where peak positions are defined by the unit cell dimensions and reflection intensities are established by the distribution of atoms in the unit cell of every crystalline phase present in the sample (see Table 2.7 in Chapter 2). Thus, every individual crystalline compound has its own fingerprint , which enables the utilization of powder diffraction data in phase identification.  

A digitized representation of powder data is quite compact and is especially convenient for comparison with other patterns, provided a suitable database is available. In addition to a digitized pattern, each entry in such a database may (and usually does) contain symmetry, imit cell dimensions, and other useful information phase name, chemical composition, references, basic physical and chemical properties, etc. Powder diffraction databases find substantial use in both simple identification of compounds (qualitative analysis) and in quantitative determination of the amounts of crystalline phases present in a mixture (quantitative analysis). 

The diffraction pattern from a single crystal is also unique but due to the complexity of a three-dimensional distribution of intensities, phase recognition is difficult to formalize. Powder data are one-dimensional, and they can be converted into digitized patterns, which are in a way, unique barcodes enabling automated pattern recognition. 

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Figure 4.1. The flowchart illustrating common steps employed in a structural characterization of materials by using the powder diffraction method. It always begins with the sample preparation as a starting point, followed by a properly executed experiment both are considered in Chapter 3. Preliminary data processing and profile fitting are discussed in this chapter in addition to common issues related to phase identification and analysis. Unit cell determination, crystal structure solution and refinement are the subjects of Chapters 5,6, and 7, respectively. The flowchart shows the most typical applications for the three types of experiments, although any or all of the data processing steps may be applied to fast, overnight and weekend experiments when justified by their quality and characterization goals.

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