LECTURE 16. Dr. Teresa D. Golden University of North Texas Department of Chemistry
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1 LECTURE 16 Dr. Teresa D. Golden University of North Texas Department of Chemistry
2 A. Evaluation of Data Quality An ICDD study found that 50% of x-ray labs overestimated the accuracy of their data by an order of magnitude. The problem has increased due to automated diffraction systems. Automation tends to separate the user from the process. Automate systems use a peak hunting system that tends to find more peaks than manual methods.
3 A. Evaluation of Data Quality Figure of Merit (FOM) FOM value is an attempt to derive a quantitative measure of data quality.
4 A. Evaluation of Data Quality
5 A. Evaluation of Data Quality Figure of Merit (FOM) Can use FOMs to evaluate instrument performance and data quality evaluation. Principle application of XRD is qualitative phase identification. Smith and Synder introduced a FOM based on peak location to evaluate the quality of XRD patterns.
6 A. Evaluation of Data Quality Figure of Merit (FOM) The American Crystallographic Association, International Union of Crystallography and International Centre for Diffraction Data (ICDD) have adopted Smith & Snyder estimate of FOM which focuses on the d hkl values.
7 A. Evaluation of Data Quality Figure of Merit (FOM) The Figure of Merit, F N, is defined as: F N 1 N 2 N poss
8 A. Evaluation of Data Quality The Figure of Merit, F N where, 2 is the average absolute discrepancy between observed and calculated 2 values. N is the number of independent diffraction reflections possible. F N poss is the number of observed reflections. N 1 N 2 N poss
9 A. Evaluation of Data Quality The Figure of Merit, F N For the computation of N poss, all resolvable reflections allowed by the space group are counted with the following exceptions: systematic absences caused by symmetry elements and lattice type only one plane from a family is counted reflections with same spacing treated as a single line, (e.g (333) (511) in cubic system)
10 A. Evaluation of Data Quality The Figure of Merit, F N 1 N 2 N poss F N is given as xx.x(y.yyy,zz) where xx.x is the value of F N y.yyy is the average 2 error zz is the number of diffraction lines possible within the 2 range covered by the first N lines. F N
11 A. Evaluation of Data Quality Figures of Merit around are considered high quality, while those less than 20 are poor quality.
12 A. Evaluation of Data Quality The FOM value varies with the scan range and number of lines observed. i.e. for a pattern having 50 possible lines, using the first 35 will give a FOM of the first 42 will give a FOM of and using all 50 will give a FOM of 151.6
13 A. Evaluation of Data Quality F N is given as xx.x(y.yyy,zz)
14 A. Evaluation of Data Quality
15 B. Phase Identification Since materials give unique x-ray diffraction patterns, the technique can be used to identify unknowns.
16 B. Phase Identification The history of qualitative analysis in x-ray diffraction has several landmarks that has accumulated to a present day extensive database.
17 B. Phase Identification This database is updated annually by the International Centre for Diffraction Data (ICDD).
18 B. Phase Identification Traditional methods of storage (cards and books) limited the storage of x-ray diffraction data to reduced forms (d-i values). Line shape and intensity distribution is lost with reduced patterns.
19 B. Phase Identification With the development of personal computers using CD ROMS, full diffractograms (fully digitized) are stored in databases.
20 B. Phase Identification Problems in identification of unknowns include: poor sample preparation inadequate data treatment uncertainty of experimental wavelengths preferred orientation poor crystallinity line broadening Great improvements have been made in recent years using a technique called profile-fitting to give better measure of integrated peak intensities and profile maxima.
21 B. Phase Identification Improvement of instrumentation and computerization in the last decade has lead to an improvement and update of reference patterns. For example for patterns before 1980, 2 for the average pattern was 0.10 o This has improved to o for the newer instruments and o with the use of internal standards. In the 1980s, the ICDD initiated a critical review of all data in the PDF for sets 1-32.
22 B. Phase Identification 1. Databases There are a number of databases available for x-ray crystallography work. The majority are designed and maintained by the singlecrystal community.
23 B. Phase Identification 1. Databases Databases designed for the powder diffraction community are also available.
24 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) A collection of powder diffraction patterns maintained by ICDD. The PDF is large, so it is organized into subfiles.
25 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) A collection of powder diffraction patterns maintained by ICDD.
26 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) A collection of powder diffraction patterns maintained by ICDD.
27 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) A collection of powder diffraction patterns maintained by ICDD.
28 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) Each individual PDF card typically contains:» d-i list» chemical formula» chemical name» PDF number» reference» miller indices» unit cell» space group» physical constants» experimental details» comments
29 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) The PDF card number is in the form yy-nnnn, where yy represents the year.
30 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF) For data sets after set 5, the actual year of publication is found by adding 1950 to the value of yy. i.e. set 27 was published in the year = The character nnnn represents the number of the pattern published in its appropriate year. i.e. pattern is the 1 st pattern published in There are ~2000 patterns added to the database each year.
31 B. Phase Identification 1. Databases a. Powder Diffraction File (PDF)
32 B. Phase Identification 2. Search/Matching Methods a. Manual Methods
33 B. Phase Identification 2. Search/Matching Methods a. Manual Methods Most Common» Alphabetic Method» Hanawalt Method» Fink Method
34 B. Phase Identification 2. Search/Matching Methods a. Manual Methods Problems: With Hanawalt method, since patterns are indexed based on their strongest lines, it is difficult to match samples with preferred orientation and overlapping lines for complex mixtures.
35 B. Phase Identification 2. Search/Matching Methods a. Manual Methods Problems: Fink method is better, since it was based on the 8 largest d values. However the Fink manual method was not published after 1980s, because of the volume thickness.
36 B. Phase Identification 2. Search/Matching Methods b. Automated Methods Beginning in the 1980s, programs were written to automate the search/matching process. Siemens developed some of the 1 st software for the D-500 models using the Hanawalt strategy. Phillips then developed a method of probability searching using least square analysis. Recently whole pattern including background matches have evolved.
37 B. Phase Identification 2. Search/Matching Methods c. JADE
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46 Reading Assignment: Read Chapter 12 from: -Introduction to X-ray powder Diffractometry by Jenkins and Synder
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LECTURE 15. Dr. Teresa D. Golden University of North Texas Department of Chemistry
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