|The IUCr is an International Scientific Union. Its objectives are to promote international cooperation in crystallography and to contribute to all aspects of crystallography, to promote international publication of crystallographic research, to facilitate standardization of methods, units, nomenclatures and symbols, and to form a focus for the relations of crystallography to other sciences.|
The Diffraction Data Deposition Working Group (DDDWG) of the IUCr is continuing its work to evaluate the desirability of preserving diffraction images and other raw data sets as part of the permanent record of a scientific investigation. While the case has yet to be made for comprehensive archiving by centralised agencies, the Working Group has encouraged the assignment of persistent identifiers to data sets archived by a researcher's local institution as an interim approach.
Following the University of Manchester Library's assignment of Digital Object Identifiers (DOIs) to research data sets stored in its repository, a suite of articles on the platins and histidine by Helliwell and co-authors has been updated to include these DOI-based links in the supporting information (e.g. Tanley & Helliwell, 2014).
Prior to the University's implementation of data storage with associated DOIs, several of these data sets were also archived informally at the University of Utrecht and in the Australian Store-Synchrotron facility. The story of the reprocessing of some of these data sets by an independent investigator has already been told in a recent article related to the work of the DDDWG (Kroon-Batenburg & Helliwell, 2014).
The work and progress of the DDDWG are described within the various postings in an IUCr Forum, which also includes links to pertinent documents from e.g. the International Council for Science and CODATA on the data archiving policies and practice in other scientific fields. The presentations from the recent DDDWG workshops in Bergen and Rovinj are also available on this site.
The International Union of Crystallography is pleased to announce that Acta Crystallographica Section E: Crystallographic Communications has been accepted for indexing in the Emerging Sources Citation Index, a new edition of Web of Science. Acta E is thus also under consideration by Thomson Reuters to be accepted in the Science Citation Index Expanded.
Acta E is changing to publish Research Communications only. These are longer papers with text sections designed to help authors bring out the science behind their structure determinations. Figures are included in the published paper and authors are encouraged to report and discuss multiple structures in these articles. The open-access Research Communication format makes Acta E the natural home for structure determinations with interesting science to report.
The Emerging Sources Citation Index (ESCI) expands coverage of journal content across Web of Science collections and also makes content important to funders, key opinion leaders, and evaluators visible in Web of Science. Journals indexed in ESCI have been evaluated by the Thomson Reuters editorial staff. Factors considered during the selection process include the journal’s publishing standards, its editorial content, and citation data from Web of Science.
Metal-organic frameworks (MOFs) are currently the subject of over a thousand research papers per year and have attracted the attention of many research groups across the world. This is the result of a predisposition for structural diversity, tuneable properties and timely relevance to a range of technological applications.
MOFs, demonstrate a wide variety of behaviour in their response to pressure [Coudert, F.-X. (2015). Acta Cryst. B71, doi:10.1107/S2052520615020934]. A growing number of materials in this topical family have been shown to be stimuli-responsive, with most of the attention paid to structural transitions upon temperature change and guest adsorption or evacuation – including some eye-catching phenomena with names such as gate opening and breathing. However [McKellar, S. C. & Moggach, S. A. (2015). Acta Cryst. B71, doi:10.1107/S2052520615018168] show in their feature article, pressure is a powerful thermodynamic variable which can be varied within a wide range and is tensorial in nature (compared with e.g. the scalar nature of temperature). Being built partly on weaker interactions than traditional inorganic microporous materials, and because they reach ultra-high porosities, MOFs tend to be generally more compressible than, say, zeolites. This magnifies their unusual responses to the application of high pressures, with large-scale changes of structure and properties upon modest stimulation, and can in turn be leveraged into devices and applications. McKellar and Moggach have reviewed the recent literature in this rapidly growing field, highlighting some of the key behaviours that have been observed in MOFs.
This story is a short extract reprinted from a commentary published in the journal Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials
Accurate strain/stress characterization has important implications in practice, where it is typically connected with safe operation of engineering components. Improving the accuracy and spatial resolution of the measurement techniques can be therefore very beneficial for materials science by helping the understanding of complex material behaviour and the development of novel materials with improved properties [Borbély, A. (2015). J. Appl. Cryst. 48, doi:10.1107/S1600576715018981]
Strain measurement by diffraction methods is based on Bragg’s law, which in the case of a strained crystal predicts a shift of the diffraction peaks compared to their position measured on a reference state. Classically this shift is determined as the difference between the corresponding absolute peak positions. In view of the large number of parameters needed to measure these absolute readings it is interesting to ask what the accuracy of today’s X-ray diffraction (XRD) methods are and how these might be improved.
Two recently published articles by [Petit et al. (2015). J. Synchrotron Rad. 22, 980-994; doi:10.1107/ S1600577515005780] and [Zhang et al. (2015). J. Appl. Cryst. 48, doi:10.1107/S1600576715018397] introduce evaluation schemes based on the shift of diffraction peaks determined by digital image correlation (DIC). This new approach eliminates the uncertainties related to the choice of an adequate mathematical model describing the experimental intensity distribution. The papers explain in a clear and intuitive way why the innovative idea of applying DIC to diffraction peaks works so well for Laue spots.The authors go on to discuss additional implications of the DIC technique for strain determination by diffraction methods.
MASSIF-1 [Bowler et al. (2015). J. Synchrotron Rad. 22, doi:10.1107/S1600577515016604] is a beamline for the fully automatic characterisation and data collection from macromolecular crystals. The beamline has been open since September 2014 and to date has processed 10,000 samples. The service offered provides a new tool for structural biologists to screen initial crystallisation hits or collect large numbers of data sets without having to control the end station themselves. The automatic routines available allow data collection to be performed consistently, taking crystal size and flux into account when calculating data collection strategies. Flexibility is introduced into the system by allowing sample specific parameters to be specified in the database ISPyB. Whilst measurements are currently limited to variations around classic experiments, it is hoped that more complex strategies such as helical data collections and goniometer realignment can be included soon. It is also foreseen to include dehydration experiments in the automated pipeline.
The new level of automation should decrease project lifecycles and in partnership with development being made in the automatic mounting of crystals, a fully automatic pipeline from protein to structure can now be envisioned.
In recent years, advances in materials synthesis techniques have enabled scientists to produce increasingly complex functional materials with enhanced or novel macroscopic properties. For example, ultra-small core-shell metallic nanoparticles used for catalysis, high entropy alloys made of 6 or 7 elements to give high strength at high temperatures and pharmaceuticals engineered at the nano-scale for more effective drug delivery. Modern engineered materials drive progress in many scientific disciplines and are at the heart of next-generation technologies in industrial fields including electronics, energy production and storage, environmental engineering, and biomedicine. As the optical, electronic and mechanical properties of such materials are deeply influenced by atomic structure, solving the structure of engineered materials is of critical importance to unlocking their true potential.
However, the structures of such materials are often complex and non-periodic at the atomic scale or at the nanoscale. For example, many of the best known thermoelectric materials have structures that are crystallographic on average, but derive their high thermoelectric figure of merit from local atomic distortions. Disordered collections of nanoparticles, on the other hand, have a high degree of short- range order but no long-range order beyond the nanoscale. Additionally, many novel materials are composites, which exhibit complex ordering on multiple length scales, so the complexity faced by the materials scientist wishing to understand the structure of new and novel materials is considerable.
The standard techniques of crystallography have proven successful in characterizing a vast array of bulk materials whose atomic structures can be described with crystal models that require only tens or hundreds of parameters. Since X-ray diffraction data typically yield information on hundreds or thousands of diffraction peaks, a unique structure solution can almost always be found for crystalline materials. However, for the types of complex materials described above the number of degrees of freedom in suitable structure models is often considerably larger than in the case of a typical crystal. Additionally, complex engineered materials often produce extremely broad peaks in diffraction experiments, due to the fact that they are non-periodic or disordered. Thus, the structure problem is doubly complicated, as diffraction experiments produce less information than corresponding experiments on bulk materials. From a standard crystallographic perspective, the structure problem for many complex materials is inherently ill posed, making a unique solution impossible. When the standard techniques of crystallography fail, it is sometimes possible to develop new analytical tools to maximise the information extracted from a diffraction pattern, but even with such advances a unique solution can often not be found to a fundamentally ill-posed problem without defining new constraints or adding additional data.
To obtain unique structure solutions for complex materials, a new paradigm of analysis is needed: a methodology that can combine different information sources and models into a coherent framework to solve problems using global optimisation. Within this framework, a material with unknown structure could be probed with various experimental tools, such as X-ray diffraction, transmission electron microscopy, small-angle X-ray or neutron scattering, Raman spectroscopy etc. to yield an array of data sets that would then be fed into a global optimiser. Additionally, theoretical inputs, such as density- functional theory could be integrated into the optimisation. While each single experimental or theoretical input may not generate enough information to produce a solution, together the pieces of information would regularise the problem, resulting in a unique solution.
A group of scientists from the United States of America [Juhás et al. (2015), Acta Cryst. A71, 562-568 doi:10.1107/S2053273315014473] provide a complete description of an implementation of complex modelling, one which is robust, modular and easily adaptable to different types of problems and different combinations of data sets and theoretical inputs. The key is to break the process down into its constituent parts, which can then be combined and linked as necessary to solve the problem at hand.