numerous conformations. The rapid rise of MOFs (vide infra – this class of material
now represents about 10% of the CSD entries; see Fig. 6) provides another example
of highly functional materials that pose challenges for the crystallographer – such as
flexibility, large void spaces and significant amounts/proportions of solvent. Porous
materials, single molecule magnets, frameworks and supramolecular complexes are
all examples of areas of chemistry that have grown fantastically fast over the last two
decades and that rely fundamentally on crystallographic analysis.
The advances in instrumentation outlined above provide an excellent basis for
addressing these challenges. The strength of X-ray sources, combined with noiseless, large dynamic range detectors, can produce a far clearer and greater quality
standard of data at a much faster rate. In some senses, this empowers the crystallographer; however, in many cases, this produces even greater challenges when
attempting to analyse this data. Having instrumentation that can make more detailed
measurements on more challenging systems is now tending to expose either the
complexities of these materials or the complexities of the solid state itself. This
means that the number of cases of disorder is rapidly rising and that they are
becoming more complicated (as discussed later in Figs. 3 and 8). Furthermore, the
incidence of modulated and incommensurate structures is increasing and becoming
more apparent in molecular systems – this is a phenomenon that has been considered
very rare until recently and also one that we are poorly prepared to address.
2.4 Future Considerations
These shifts in instrumentation and chemistry being undertaken clearly indicate two
needs for future development to focus on. While software has been well developed to
drive diffractometers, mainly by manufacturers of this instrumentation, there is much
to do regarding processing and working up data. This is generally the part of the
process where the most time and expert input is required and often the stage when a
study is aborted. In many of these cases, there would be potential to extract some
structural information albeit possibly of a different standard to that which is currently
demanded.
Firstly, there is a shortfall in the drive to technically develop data processing
algorithms and approaches. The advent of new detector technology highlights the
fact that many laboratories are using integration and correction routines that were
designed for CCD data over a decade ago. Currently this is appropriate for the
majority of use, but for the HPC detectors that will be commonplace in the future, we
are essentially using the first generation of this software, and there are developments
that can be made. There are also different approaches that could be used or
developed in order to get a lower level of structural information from particularly
‘poor’ diffraction patterns. Furthermore, as photon counting and next-generation
integrating detectors gain more widespread use, it will also be necessary to devote
more attention to data processing methods.
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S. J. Coles et al.
now represents about 10% of the CSD entries; see Fig. 6) provides another example
of highly functional materials that pose challenges for the crystallographer – such as
flexibility, large void spaces and significant amounts/proportions of solvent. Porous
materials, single molecule magnets, frameworks and supramolecular complexes are
all examples of areas of chemistry that have grown fantastically fast over the last two
decades and that rely fundamentally on crystallographic analysis.
The advances in instrumentation outlined above provide an excellent basis for
addressing these challenges. The strength of X-ray sources, combined with noiseless, large dynamic range detectors, can produce a far clearer and greater quality
standard of data at a much faster rate. In some senses, this empowers the crystallographer; however, in many cases, this produces even greater challenges when
attempting to analyse this data. Having instrumentation that can make more detailed
measurements on more challenging systems is now tending to expose either the
complexities of these materials or the complexities of the solid state itself. This
means that the number of cases of disorder is rapidly rising and that they are
becoming more complicated (as discussed later in Figs. 3 and 8). Furthermore, the
incidence of modulated and incommensurate structures is increasing and becoming
more apparent in molecular systems – this is a phenomenon that has been considered
very rare until recently and also one that we are poorly prepared to address.
2.4 Future Considerations
These shifts in instrumentation and chemistry being undertaken clearly indicate two
needs for future development to focus on. While software has been well developed to
drive diffractometers, mainly by manufacturers of this instrumentation, there is much
to do regarding processing and working up data. This is generally the part of the
process where the most time and expert input is required and often the stage when a
study is aborted. In many of these cases, there would be potential to extract some
structural information albeit possibly of a different standard to that which is currently
demanded.
Firstly, there is a shortfall in the drive to technically develop data processing
algorithms and approaches. The advent of new detector technology highlights the
fact that many laboratories are using integration and correction routines that were
designed for CCD data over a decade ago. Currently this is appropriate for the
majority of use, but for the HPC detectors that will be commonplace in the future, we
are essentially using the first generation of this software, and there are developments
that can be made. There are also different approaches that could be used or
developed in order to get a lower level of structural information from particularly
‘poor’ diffraction patterns. Furthermore, as photon counting and next-generation
integrating detectors gain more widespread use, it will also be necessary to devote
more attention to data processing methods.
104
S. J. Coles et al.