Structure Determination from Powder Diffraction Data (International Union of Crystallography Monogra

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Cox, and Andrew N. Ibberson, and William I.

Hill, and Ian C. David, and Devinderjit S. McCusker, and William I. Moliterni, Giampiero Polidori, and Rosanna Rizzi.

Structure Determination from Powder Diffraction Data

The high spectral brightness, high degree of collimation and tunability make synchrotron X-rays the ideal source for this purpose. The disadvantages for routine work are limited access and the likelihood of a long lead time for experiments.

Neutron powder diffraction data are generally more suited to structure refinement than solution, but R. David point out in chapter 5 that this is an over-simplification; there are several instances where it is advantageous to use thermal neutrons, particularly with time-of-flight diffractometers.

They also demonstrate that the combination of X-ray and neutron data provides a powerful tool for structure solution. Structures of greater complexity can be solved and the latter helps to locate light atoms. A crucial step in structure solution is a determination of the unit-cell dimensions from peak positions, leading to a reconstruction of the three-dimensional crystal lattice from the one-dimensional positions of reflections in the powder pattern. This procedure, often known as autoindexing, is covered in chapter 7, by P.

Structure Determination from Powder Diffraction Data - Oxford Scholarship

The three principal and most widely used indexing programs are considered and compared, and potential pitfalls are discussed. As Werner points out, all the programs are dependent, in different ways, on having data of high quality, with relatively small systematic errors. It is therefore often desirable to compare the results from different programs. Indexing is one of the success stories of powder diffraction. With accurate peak positions, combined with other available information about the sample, the unit cell and crystal lattice can usually be determined with confidence, provided that any unindexed lines can be attributed to impurities.

The next two chapters deal with the theoretical and experimental aspects of one of the basic problems of structure solution from powder data, namely that of extracting integrated intensities from overlapping reflections. Although the procedure is relatively straightforward, the success of structure solution depends on obtaining as many single-crystal-like intensities as possible. In chapter 9 it is shown that, for some materials, the number can be increased by collecting datasets under different experimental conditions.

A by-product of intensity extraction is the determination of one or more possible space groups.

Methods for solving the structure are covered in chapters 10 to These are grouped as adaptations of standard single-crystal techniques, direct-space methods based on prior chemical knowledge, and a combination of the two. In chapter 11 there is a useful list of pitfalls to avoid when using powder data.

Although this is specifically aimed at users of direct methods, several of the items listed are applicable generally. Other methods adapted to powder diffraction data are Patterson techniques and the maximum-entropy approach chapters 13 and Direct-space methods, global optimization strategies chapter 15 and simulated annealing chapter 16 , were mainly, but not exclusively, developed for molecular compounds. They involve creating a plausible structural model and then assessing its validity by comparing calculated and experimental powder patterns.

Extraction of the intensity of individual reflections is thus avoided in this approach.

W.I.F. David, K. Shankland, L.B. McCusker, and C. Bärlocher

As mentioned in chapter 1, the final stage in the process is refinement of the structure by means of the Rietveld method; this should confirm the correctness of the solution, but it can also reveal inadequacies in the structural model. The method is not treated separately, though there are references to it throughout the book.

In the final chapter, L. Baerlocher stress the importance of using additional information, such as chemical composition, physical properties, structural features of related compounds and the results of non-diffraction experiments. These factors can help to offset the relatively low information content of a powder pattern, compared with a single-crystal dataset. McCusker and Baerlocher show how the extra information can be used at every stage, from data collection onwards. This important chapter should therefore be read at the outset, in conjunction with chapter 6, since its content can influence strategy in subsequent stages.

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