178 resultados para hydrogen generation


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Reactions in (molecular) organic crystalline solids have been shown to be important for exerting control that is unattainable over chemical transformations in solution. Such control has also been achieved for reactions within metal– organic cages. In these examples, the reactants are already in place within the crystals following the original crystal growth. The post-synthetic modification of metal–organic frameworks (MOFs and indeed reactions and catalysis within MOFs have been recently demonstrated; in these cases the reactants enter the crystals through permanent channels. Another growing area of interest within molecular solid-state chemistry is synthesis by mechanical co-grinding of solid reactants—often referred to as mechanochemistry. Finally, in a small number of reported examples, molecules also have been shown to enter nonporous crystals directly from the gas or vapor phase, but in only a few of these examples does a change in covalent bonding result, which indicates that a reaction occurs within the nonporous crystals. It is this latter type of highly uncommon reaction that is the focus of the present study.

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The discovery of polymers with stimuli responsive physical properties is a rapidly expanding area of research. At the forefront of the field are self-healing polymers, which, when fractured can regain the mechanical properties of the material either autonomically, or in response to a stimulus. It has long been known that it is possible to promote healing in conventional thermoplastics by heating the fracture zone above the Tg of the polymer under pressure. This process requires reptation and subsequent re-entanglement of macromolecules across the fracture void, which serves to bridge, and ‘heal’ the crack. The timescale for this mechanism is highly dependent on the molecular weight of the polymer being studied. This process is in contrast to that required to affect healing in supramolecular polymers such as the plasticised, hydrogen bonded elastomer reported by Leibler et al. The disparity in bond energies between the non-covalent and covalent bonds within supramolecular polymers results in fractures propagating through scission of the comparatively weak supramolecular interactions, rather than through breaking the stronger, covalent bonds. Thus, during the healing process the macromolecules surrounding the fracture site only need sufficient energy to re-engage their supramolecular interactions in order to regenerate the strength of the pristine material. Herein we describe the design, synthesis and optimization of a new class of supramolecular polymer blends that harness the reversible nature of pi-pi stacking and hydrogen bonding interactions to produce self-supporting films with facile healable characteristics.

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A series of six low molecular weight elastomers with hydrogen bonding end-groups have been designed, synthesised and studied. The poly(urethane) based elastomers all contained essentially the same hard block content (ca. 11%) and differ only in the nature of their end-groups. Solution state 1H NMR spectroscopic analysis of model compounds featuring the end-groups demonstrate that they all exhibit very low binding constants, in the range 1.4 to 45.0 M-1 in CDCl3, yet the corresponding elastomers each possess a markedly different nanoscale morphology and rheology in the bulk. We are able to correlate small variations of the binding constant of the end-groups with dramatic changes in the bulk properties of the elastomers. These results provide an important insight into the way in which weak non-covalent interactions can be utilized to afford a range of self-assembled polyurethane based materials that feature different morphologies.

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A combination of photoelectron spectroscopy, temperature programmed desorption and low energy electron diffraction structure determinations have been applied to study the p(2 x 2) structures of pure hydrogen and co-adsorbed hydrogen and CO on Ni {111}. In agreement with earlier work atomic hydrogen is found to adsorb on fcc and hcp sites in the pure layer with H-Ni bond lengths of 1.74Angstrom. The substrate interlayer distances, d(12) = 2.05Angstrom and d(23) = 2.06Angstrom, are expanded with respect to clean Ni {111} with buckling of 0.04Angstrom in the first layer. In the co-adsorbed phase Co occupies hcp sites and only the hydrogen atoms on fcc sites remain on the surface. d(12) is even further expanded to 2.08Angstrom with buckling in the first and second layer of 0.06 and 0.02Angstrom, respectively. The C-O, C-Ni, and H-Ni bond lengths are within the range of values also found for the pure adsorbates.

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An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

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The Schiff base ligand, HL (2-[1-(3-methylamino-propylimino)-ethyl]-phenol), the 1:1 condensation product of 2-hydroxy acetophenone and N-methyl-1,3-diaminopropane, has been synthesized and characterized by X-ray crystallography as the perchlorate salt [H2L]ClO4 (1). The structure consists of discrete [H2L](+) cations and perchlorate anions. Two dinuclear Ni-II complexes, [Ni2L2(NO2)(2)] (2), [Ni2L2(NO3)(2)] (3) have been synthesized using this ligand and characterized by single crystal X-ray analyses. Complexes 2 and 3 are centrosymmetric dimers in which the Ni-II ions are in distorted fac- and mer-octahedral environments, respectively, bridged by two mu(2)-phenolate ions of deprotonated ligand, L. The plane of the phenyl rings and the Ni2O2 basal plane are nearly coplanar in 2 but almost perpendicular in 3. We have studied and explained this different behavior using high level DFT calculations (RI-BP86/def2-TZVP level of theory). The conformation observed in 3, which is energetically less favorable, is stabilized via intermolecular non-covalent interactions. Under the excitation of ultraviolet light, characteristic fluorescence of compound 1 was observed; by comparison fluorescence intensity decreases in case of compound 3 and completely quenched in compound 2.

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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.

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Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

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An unusual hexanuclear Cu-II complex, [{[Cu(NHDEPO)](3)(mu(3)-O)(O3ClO)}(2)(mu-H)]center dot 7ClO(4)center dot 4H(2)O (1) was prepared starting from Cu(ClO4)(2)center dot 6H(2)O and the oxime-based Schiff base ligand NHDEPO (= 3-[3-(diethylamino)propylimino]butan-2-one oxime). Structural characterization of the complex reveals that it consists of two triangular Cu3O units, the copper ions being at the corners of an equilateral triangle, separated by an O center dot center dot center dot O distance of 2,447(5) angstrom, held together solely by a proton. In each triangle, the copper atoms are in square-pyramid environments. The equatorial plane consists of the bridging oxygen of the central OH-(O2-) group together with three atoms (N, N, O) of the Schiff base. All Unusual triply coordinated perchlorate ion (mu(3)-kappa O:kappa O':kappa O '') interacts in axial position with the three copper ions, Variable-temperature (2-300 K) magnetic susceptibility measurements show that complex 1 is antiferromagnetically Coupled (J = -148 cm(1-)). The EPR data at low temperature clearly indicates the presence of spin frustration phenomenon in the complex.

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Waves with periods shorter than the inertial period exist in the atmosphere (as inertia-gravity waves) and in the oceans (as Poincaré and internal gravity waves). Such waves owe their origin to various mechanisms, but of particular interest are those arising either from local secondary instabilities or spontaneous emission due to loss of balance. These phenomena have been studied in the laboratory, both in the mechanically-forced and the thermally-forced rotating annulus. Their generation mechanisms, especially in the latter system, have not yet been fully understood, however. Here we examine short period waves in a numerical model of the rotating thermal annulus, and show how the results are consistent with those from earlier laboratory experiments. We then show how these waves are consistent with being inertia-gravity waves generated by a localised instability within the thermal boundary layer, the location of which is determined by regions of strong shear and downwelling at certain points within a large-scale baroclinic wave flow. The resulting instability launches small-scale inertia-gravity waves into the geostrophic interior of the flow. Their behaviour is captured in fully nonlinear numerical simulations in a finite-difference, 3D Boussinesq Navier-Stokes model. Such a mechanism has many similarities with those responsible for launching small- and meso-scale inertia-gravity waves in the atmosphere from fronts and local convection.

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Two new mono-aqua-bridged dinuclear Cu(II) complexes of tridentate NNO Schiff bases, [Cu-2(mu-H2O)L-2(1)(H2O)(2)](BF4)(2)center dot 2H(2)O (1) and [Cu-2(mu-H2O)L-2(2)(H2O)(2)](BF4)(2)center dot 2H(2)O (2) where HL1 = 2-[1-(2-dimethylamino-ethylimino)-ethyl]-phenol and HL2 =2-[(2-dimethylamino-ethylimino)-methyl]-phenol were synthesized. Both the complexes were characterized by single-crystal X-ray diffraction analyses and variable-temperature magnetic measurements. For both the complexes each Cu(II) ion is in a square-pyramidal environment being bonded to three atoms from the tridentate NNO Schiff base and a terminal H2O molecule in the equatorial plane; a second H2O ligand acts as a bridge between the two Cu(II) centres through the axial positions. Hydrogen bonds between the terminal H2O ligand and the Schiff base of the adjacent centre complete the intra-dimer linkages. Variable-temperature (4-300 K) magnetic susceptibility measurement shows the presence of significant antiferromagnetic coupling for both the complexes (J = -12.2 and -12.5 cm(-1), respectively, for 1 and 2), mediated mainly through the intra-dimer H-bonds.

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Electricity consumption in Ghana is estimated to be increasing by 10% per annum due to the demand from the growing population. However, current sources of production (hydro and thermal facilities) generate only 66% of the current demand. Considering current trends, it is difficult to substantiate these basic facts, because of the lack of information. As a result, research into the existing sources of generating electricity, electricity consumption and prospective projects has been performed. This was achieved using three key techniques; review of literature, empirical studies and modelling. The results presented suggest that, current annual installed capacity of energy generation (i.e. 1960 MW) must be increased to 9,405.59 MW, assuming 85% plant availability. This is then capable to coop with the growing demand and it would give access to the entire population as well as support commercial and industrial activities for the growth of the economy. The prospect of performing this research is with the expectation to present an academic research agenda for further exploration into the subject area, without which the growth of the country would be stagnant.