Structure determination from powder diffraction data

Advances made over the past decade in structure determination from powder diffraction data are reviewed with particular emphasis on algorithmic developments and the successes and limitations of the technique. While global optimization methods have been successful in the solution of molecular crystal structures, new methods are required to make the solution of inorganic crystal structures more routine. The use of complementary techniques such as NMR to assist structure solution is discussed and the potential for the combined use of X-ray and neutron diffraction data for structure verification is explored. Structures that have proved difficult to solve from powder diffraction data are reviewed and the limitations of structure determination from powder diffraction data are discussed. Furthermore, the prospects of solving small protein crystal structures over the next decade are assessed.

Clustering techniques applied to multiple-models structures

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Dimension and coverage of multiple-models structures using clustering techniques

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Structures and negative thermal expansion properties of the one-dimensional cyanides, CuCN, AgCN and AuCN

The behaviour of the lattice parameters of HTCuCN (high-temperature form), AgCN and AuCN have been investigated as a function of temperature over the temperature range 90–490 K. All materials show one-dimensional negative thermal expansion (NTE) along the ––(M––CN)–– chain direction c (ac(HT-CuCN) ¼32.1 10–6 K1, ac(AgCN)¼23.910–6 K1 and ac(AuCN) ¼9.3106 K1 over the temperature range 90–490 K). The origin of this behaviour has been studied using RMC modelling of Bragg and total neutron diffraction data from AgCN and AuCN at 10 and 300 K. These analyses yield details of the local motions within the chains responsible for NTE. The low-temperature form of CuCN, LT-CuCN, has been studied using single-crystal X-ray diffraction. In this form of CuCN, wavelike distortions of the ––(Cu––CN)–– chains occur in the static structure, which are reminiscent of the motions seen in the RMC modelling of AgCN and AuCN, which are responsible for the NTE behaviour.

Computer simulations of structures, energetics and dynamics of myoglobin center dot center dot center dot ligand complexes

Myoglobin has been studied in considerable detail using different experimental and computational techniques over the past decades. Recent developments in time-resolved spectroscopy have provided experimental data amenable to detailed atomistic simulations. The main theme of the present review are results on the structures, energetics and dynamics of ligands ( CO, NO) interacting with myoglobin from computer simulations. Modern computational methods including free energy simulations, mixed quantum mechanics/molecular mechanics simulations, and reactive molecular dynamics simulations provide insight into the dynamics of ligand dynamics in confined spaces complementary to experiment. Application of these methods to calculate and understand experimental observations for myoglobin interacting with CO and NO are presented and discussed.

Spectroscopic properties and electronic structures of 17-electron half-sandwich ruthenium acetylide complexes, [Ru(CCAr)(L2)Cp′]+ (Ar=phenyl, p-tolyl, 1-naphthyl, 9-anthryl; L2=(PPh3)2, Cp′=Cp; L2=dppe; Cp′=Cp∗)

A series of half-sandwich bis(phosphine) ruthenium acetylide complexes [Ru(C CAr)(L-2)Cp'] (Ar = phenyl, p-tolyl, 1-naphthyl, 9-anthryl; L2 = (PPh3)(2), Cp' = Cp; L-2 = dppe; Cp' = Cp*) have been examined using electrochemical and spectroelectrochemical methods. One-electron oxidation of these complexes gave the corresponding radical cations [Ru(C CAr)(L2)Cp'](+). Those cations based on Ru(dppe)Cp*, or which feature a para-tolyl acetylide substituent, are more chemically robust than examples featuring the Ru(PPh3)(2)Cp moiety, permitting good quality UV-Vis-NIR and IR spectroscopic data to be obtained using spectroelectrochemical methods. On the basis of TD DFT calculations, the low energy (NIR) absorption bands in the experimental electronic spectra for most of these radical cations are assigned to transitions between the beta-HOSO and beta-LUSO, both of which have appreciable metal d and ethynyl pi character. However, the large contribution from the anthryl moiety to the frontier orbitals of [Ru(C CC14H9)(L2)CP'](+) suggests compounds containing this moiety should be described as metal-stabilised anthryl radical cations.

Structures of Pd(CN)2and Pt(CN)2: intrinsically nanocrystalline materials?

Analysis and modeling of X-ray and neutron Bragg and total diffraction data show that the compounds referred to in the literature as “Pd(CN)2”and“Pt(CN)2” are nanocrystalline materials containing of small sheets of vertex-sharing square-planar M(CN)4 units, layered in a disordered manner with an intersheet separation of 3.44 A at 300 K. The small size of the crystallites means that the sheets’ edges form a significant fraction of each material. The Pd(CN)2 nanocrystallites studied using total neutron diffraction are terminated by water and the Pt(CN)2 nanocrystallites by ammonia, in place of half of the terminal cyanide groups, thus maintaining charge neutrality. The neutron samples contain sheets of approximate dimensions 30 A x 30 A. For sheets of the size we describe, our structural models predict compositions of Pd(CN)2-xH2O and Pt(CN)2-yNH3 (x = y = 0.29). These values are in good agreement with those obtained from total neutron diffraction and thermal analysis, and are also supported by infrared and Raman spectroscopy measurements. It is also possible to prepare related compounds Pd(CN)2-pNH3 and Pt(CN)2-qH2O, in which the terminating groups are exchanged. Additional samples showing sheet sizes in the range 10 A x 10 A (y = 0.67) to 80 A x 80 A (p = q = 0.12), as determined by X-ray diffraction, have been prepared. The related mixed-metal phase, Pd1/2Pt1/2(CN)2-qH2O(q = 0.50), is also nanocrystalline (sheet size 15 A x 15 A). In all cases, the interiors of the sheets are isostructural with those found in Ni(CN)2. Removal of the final traces of water or ammonia by heating results in decomposition of the compounds to Pd and Pt metal, or in the case of the mixed-metal cyanide, the alloy, Pd1/2Pt1/2, making it impossible to prepare the simple cyanides, Pd(CN)2, Pt(CN)2 or Pd1/2Pt1/2(CN)2, by this method.

Generation, description and storage of dendritic morphology data

It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure–function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three–dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single–cell neuroanatomy can be characterized quantitatively at several levels. In computer–aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This ‘Cartesian’ description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise ‘blueprint’ of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of ‘fundamental’, measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L–NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the ‘computational neuroanatomy’ strategy for neuroscience databases.

Highly proton-ordered water structures on oxygen precovered Ru{0001}

We present helium scattering measurements of a water ad-layer grown on a O(2 1)/Ru(0001) surface. The adsorbed water layer results in a well ordered helium diffraction pattern with systematic extinctions of diffraction spots due to glide line symmetries. The data reflects a well-defined surface structure that maintains proton order even at surprisingly high temperatures of 140 K. The diffraction data we measure is consistent with a structure recently derived from STM measurements performed at 6 K. Comparison with recent DFT calculation is in partial agreement, suggesting that these calculations might be underestimating the contribution of relative water molecule orientations to the binding energy.

Evaluating the effectiveness of common structures in property portfolio construction

A good portfolio structure enables an investor to diversify more effectively and understand systematic influences on their performance. However, in the property market, the choice of structure is affected by data constraints and convenience. Using individual return data, this study tests the hypothesis that some common structures in the UK do not explain a significant amount about property returns. It is found that, in the periods studied, not all the structures were effective and, for the annual returns, no structures were significant in all periods. The results suggest that the drivers represented by the structures take some time to be reflected in individual property returns. They also confirm the results of other studies in finding property type a much stronger factor in explaining returns than regions.

Synthesis and properties of tetra-μ-acetatodiruthenium(II,III) phenylphosphinate and phenylphosphonate complexes: X-ray crystal structures of [Ru2(μ-O2CCH3)4(HPhPO2)2]H and [Ru2(μ-O2CCH3)4(PhPO3H)2]H·H2O

Phenylphosphinic acid (HPhPO2H) and phenylphosphonic acid (PhPO3H2) react with a methanolic solution of [Ru2(μ-O2CCH3)4(O2CCH3)2]H·0.7H2O at room temperature to give [Ru2(μ-O2CCH3)4(HPhPO2)2H (1) and [Ru2(μ-O2CCH3)4 (PhPO3H)2]H·H2O (2), respectively. The X-ray crystal structures of 1 and 2 each show the RuRu core to be ligated by four bridging bidentate acetate ligands [RuRu distances: 1 = 2.272(1) Å; 2 = 2.267(2) Å] and two axial phenylphosphinate and phenylphosphonate ligands, respectively. In each complex the individual bimetallic molecules are linked together by a hydrogen ion which bridges the oxygen atoms of neighbouring axial ligands. In 2 the water molecule is also hydrogen-bonded to one of the axial phenylphosphonate groups. Spectroscopic, magnetic and cyclic voltammetric data for the complexes are given.

Sterically hindered metal alkenyls. Part 1. Synthesis, reactions, and crystal and molecular structures of some palladium and platinum σ-alkenyls

The compounds trans-[PtBr{C(C10H15)CH2}(PEt3)2](1)(C10H15= adamant-1-yl), trans-[MBr{C(C10H7)CMe2}(PEt3)2][M = Pd (2) or Pt (3); C10H7= naphth-1-yl], and trans-[MBr{C(Ph)CMe2}(PEt3)2][M = Pd (4) or Pt (5)] have been prepared from Grignard [for (2) and (3)] or lithium reagents [for (1), (4), and (5)] and appropriate dichlorobis(phosphine)metal derivatives. Full single-crystal X-ray data are reported for (1) and (3), and reveal unusually long Pt–C(sp2) bonds. Insertion reactions into these M–C bonds occur with MeNC [for (1), (3), and (5)], and with CO [for (1) and (3)]; the latter, the first reported insertion into a Pt–C(sp2) bond, occurs under mild conditions as expected for the abnormally long M–C bonds.

Stepwise construction of [Os6(CO)18(μ6-P)]-, a hexanuclear osmium cluster monoanion with trigonal-prismatic co-ordination for phosphorus: X-ray crystal structures of [Os6(μ-H)2(CO)20(MeCN)(μ3-PH)] and [PPh3Me][Os6(CO)18(μ6-P)]

Treatment of the labile cluster [Os3(CO)11(MeCN)] with PH3 affords the substituted product [Os3(CO)11(PH3)](1) in high yield. Subsequent reaction of (1) with Na2CO3 in MeOH, followed by acidification, gives the hydrido phosphido cluster [Os3(µ-H)(CO)10(µ-PH2)](2). When (2) is heated to 45–60 °C in the presence of [Os3(CO)11(MeCN)] a hexanuclear complex with the formulation [Os6(µ-H)2(CO)21(µ3-PH)](3) is obtained. If this reaction is repeated using [Os3(CO)10(MeCN)2] instead of [Os3(CO)11(MeCN)], an acetonitrile-containing product, [Os6(µ-H)2(CO)20(MeCN)(µ3-PH)](4), is obtained. An X-ray analysis of (4) shows that two Os3 triangular units are linked by a µ3-phosphinidene ligand, which symmetrically bridges an Os–Os edge of one triangle and is terminally co-ordinated to one Os atom of the second triangle. When (3) is treated with a weak base, such as [N(PPh3)2]Cl or [PPh3Me] Br, deprotonation to the corresponding cluster monoanion [Os6(µ-H)(CO)21(µ3-PH)]–(5) occurs. Treatment of (5) with a weak acid regenerates (3) in quantitative yield. Thermolysis of (3) leads to a closing up of the metal framework, affording the cluster [Os6(µ-H)(CO)18(µ6-P)], which readily deprotonates to give the anion [Os6(CO)18(µ6-P)]–(7) in the presence of [N(PPh3)2] Cl or [PPh3Me]Br. The same anion (7) may also be obtained by direct thermolysis of (5). An X-ray analysis of the [PPh3Me]+ salt of (7) confirms that the phosphorus occupies an interstitial site in a trigonal-prismatic hexaosmium framework, and co-ordinates to all six metal atoms with an average Os–P distance of 2.31 (1)Å. Proton and 31P n.m.r. data on all the new clusters are presented, and the position of the phosphorus resonance in the 31P n.m.r. spectrum is related to the changes in the environment of the phosphorus atom.

Synthesis, characterisation, Mössbauer spectra, and structures of some trinuclear iron-tin clusters

Reactions of [Fe3(CO)12] with diaryltin species SnR2(R1= 2,4,6-triisopropylphenyl, R2= 2,6-diethylphenyl, R3= pentamethylphenyl) and with Sn[CH(PPh2)2]2 have been investigated. The tin reagents SnR2(R = R1 or R2) reacted under mild conditions to give in moderate yields the trinuclear species [Fe2(CO)8(µ-SnR12)]1 or [Fe2(CO)8(µ-SnR22)]2, as orange-red crystalline solids, which decompose in air on prolonged exposure. The compound [Fe2(CO)8(µ-SnR42)]3(R4= 2,4,6-triphenylphenyl) can be similarly obtained. Prolonged treatment of the carbonyl with the novel tin reagent SnR32, by contrast, afforded the known compound spiro-[(OC)8Fe2SnFe2(CO)8]4 for which data are briefly reported. Reactions with tin or lead reagents M[CH(PPh2)2]2(M = Sn or Pb) afforded [Fe2(CO)6(µ-CO)(µ-dppm)][dppm = 1,2-bis(diphenylphosphino)methane] rapidly and almost quantitatively. Full crystal and molecular structural data are reported for [Fe2(CO)8(µ-SnR12)] and [Fe2(CO)8(µ-SnR22)]. Mössbauer data are also presented for compounds 1–3, and interpreted in terms of the structural data for these and other systems.

Citation and peer review of data: moving towards formal data publication

This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax.