Metal-Substituted Microporous Aluminophosphates

This chapter aims to present the zeotypes aluminophosphates (AlPOs) as a complementary alternative to zeolites in the isomorphic incorporation of metal ions within all-inorganic microporous frameworks as well as to discuss didactically the catalytic consequences derived from the distinctive features of both frameworks. It does not intend to be a compilation of either all or the most significant publications involving metal-substituted microporous aluminophosphates. Families of AlPOs and zeolites, which include metal ion-substituted variants, are the dominant microporous materials. Both these systems are widely used as catalysts, in particular through aliovalent metal ions substitution. Here, some general description of the synthesis procedures and characterization techniques of the MeAPOs (metal-contained aluminophosphates) is given along with catalytic properties. Next, some illustrative examples of the catalytic possibilities of MeAPOs as catalysts in the transformation of the organic molecules are given. The oxidation of the hardly activated hydrocarbons has probably been the most successful use of AlPOs doped with the divalent transition metal ions Co2+, Mn2+, and Fe2+, whose incorporation in zeolites is disfavoured. The catalytic role of these MeAPOs is rationalized based on the knowledge acquired from a combination of the most advanced characterization techniques. Finally, the importance of the high specificity of the structure-directing agents employed in the preparation of MeAPOs is discussed taking N,N-methyldicyclohexylamine in the synthesis of AFI-structured materials as a driving force. It is shown how such a high specificity could be predicted and how it can open great possibilities in the control of parameters as critical in catalysis as crystal size, inter-and intracrystalline mesoporosity, acidity, redox properties, incorporation of a great variety of heteroatom ions or final environment of the metal site (surrounding it by either P or Al)

A guide to the crystallographic analysis of icosahedral viruses

Determining the structure of an icosahedral virus crystal by X-ray diffraction follows very much the same course as conventional protein crystallography. The major differences arise from the relatively large sizes of the particles, which significantly affect the data collection process, data processing and management, and later, the refinement of a model. Most of the other differences are due to the high 5 3 2 point group symmetry of icosahedral viruses. This alters dramatically the means by which initial phases are obtained by molecular substitution, extended to higher resolution by electron density averaging and density modification, and the refinement of the structure in the light of high non-crystallographic symmetry. In this review, we attempt to lead the investigator through the various steps involved in solving the structure of a virus crystal. These steps include the purification of viruses, their crystallization, the recording of X-ray diffraction data, and its reduction to structure amplitudes. It further addresses the problems attending phase determination and ultimately the refinement of a model. Finally, we describe the unique properties of virus crystals and the factors that influence their physical and diffraction properties

Bulk‐solvent and overall scaling revisited: faster calculations, improved results. Corrigendum.

Equations in Sections 2.3 and 2.4 of the article by Afonine et al. [Acta Cryst. (2013). D69, 625-634] are corrected

Phaser crystallographic software

Phaser is a program for phasing macromolecular crystal structures by both molecular replacement and experimental phasing methods. The novel phasing algorithms implemented in Phaser have been developed using maximum likelihood and multivariate statistics. For molecular replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solutions from noise, and for single-wavelength anomalous dispersion experimental phasing, the new algorithms, which account for correlations between F + and F, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences F. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallographic community. 1

Complex zeolite structure solved by combining powder diffraction and electron microscopy

Many industrially important materials, ranging from ceramics to catalysts to pharmaceuticals, are polycrystalline and cannot be grown as single crystals. This means that non-conventional methods of structure analysis must be applied to obtain the structural information that is fundamental to the understanding of the properties of these materials. Electron microscopy might appear to be a natural approach, but only relatively simple structures have been solved by this route. Powder diffraction is another obvious option, but the overlap of reflections with similar diffraction angles causes an ambiguity in the relative intensities of those reflections. Various ways of overcoming or circumventing this problem have been developed(1,2), and several of these involve incorporating chemical information into the structure determination process(3-7). For complex zeolite structures, the FOCUS algorithm(8,9) has proved to be effective. Because it operates in both real and reciprocal space, phase information obtained from high-resolution transmission electron microscopy images can be incorporated directly into this algorithm in a simple way. Here we show that by doing so, the complexity limit can be extended much further. The power of this approach has been demonstrated with the solution of the structure of the zeolite TNU-9 (\H-9.3\[ Al9.3Si182.7O384]; ref. 10) with 24 topologically distinct ( Si, Al) atoms and 52 such O atoms. For comparison, ITQ-22 ( ref. 11), the most complex zeolite known to date, has 16 topologically distinct (Si, Ge) atoms.</p