The Reasons for Ligand-Dependent Quantum Yields and Absorption Spectrum of Four Polypyridylruthenium(II) Complexes with a Tetrazolate-Based Ligand: TDDFT Study

The quantum yield, lifetime, and absorption spectrum of four [Ru(bpy)(2)L](+) [where bpy is 2,2'-bipyridyl; L is represented by the deprotonated form of 2-(1H-tetrazol-5-yl)pyridine (L1) or 2-(1H-tetrazol-5-yl)pyrazine (L2)], as well as their methylated complexes [Ru(bpy)(2)LMe](2+) (RuL1Me and RuL2Me) are closely ligand dependent. In this paper, density functional theory (DFT) and time-dependent DFT (TDDFT) were performed to compare the above properties among these complexes. The calculated results reveal that the replacement of pyridine by pyrazine or the attachment of a CH3 group to the tetrazolate ring greatly increases the pi-accepting ability of the ancillary ligands.

Metal-organic frameworks based on the pyridine-2,3-dicarboxylate and a flexible bispyridyl ligand: syntheses, structures, and photoluminescence

Three new metal-organic coordination polymers, [Cu(2,3-pydc)(bpp)]center dot 2.5H(2)O (1), [Zn(2,3-pydc)(bpp)]center dot 2.5H(2)O (2) and [Cd(2,3-pydc)(bpp)(H2O)]center dot 3H(2)O (3) (2,3-pydcH(2) = pyridine-2,3-dicarboxylic acid, bpp 1,3-bis(4-pyridyl)propane), have been synthesized at room temvperature. All complexes have metal ions serving as 4-connected nodes but represent two quite different structural motifs. Complexes 1 and 2 are isomorphous, both of which feature 2D -> 3D parallel interpenetration. Each two-dimensional (2D) layer with (4, 4) topology is interlocked by two nearest neighbours, one above and one below, thus leading to an unusual 3D motif. Complex 3 has a non-interpenetrating 3D CdSO4 framework with cavities occupied by uncoordinated water molecules.

Ethylene Polymerization by the New Chromium Catalysts Based on Amino-Pyrrolide Ligands

A series of amino-pyrrolide ligands (1-4a) and their derivatives aminothiophene ligand (5a), amino-indole ligand (6a) were prepared. Chromium catalysts, which were generated in situ by mixing the ligands with CrCl3(thf)(3) in toluene, were tested for ethylene polymerization. The preliminary screening results revealed that the tridentate amino-pyrrolide ligands containing soft pendant donor, 3a, 4a/CrCl3(thf)(3) systems displayed high catalytic activities towards ethylene polymerization in the presence of modified methyaluminoxane. The electronic and steric factors attached to the ligand backbone significantly affected both the catalyst activity and the polymer molecular weight. Complex 4b was obtained by the reaction of CrCl3(thf)(3) with one equivalent of the lithium salts of 4a, which was the most efficient ligand among the tested ones. The effect of polymerization parameters such as cocatalyst concentration, ethylene pressure, reaction temperature, and time on polymerization behavior were investigated in detail. The resulting polymer obtained by 4b display wax-like and possess linear structure, low molecular weight, and unimodal distribution.

Quantifying intrinsic specificity: A potential complement to affinity in drug screening

We report here the investigation of a novel description of specificity in protein-ligand binding based on energy landscape theory. We define a new term, intrinsic specificity ratio (ISR), which describes the level of discrimination in binding free energies of the native basin for a protein-ligand complex from the weaker binding states of the same ligand. We discuss the relationship between the intrinsic specificity we defined here and the conventional definition of specificity. In a docking study of molecules with the enzyme COX-2, we demonstrate a statistical correspondence between ISR value and geometrical shapes of the small molecules binding to COX-2. We further observe that the known selective (nonselective) inhibitors of COX-2 have higher (lower) ISR values. We suggest that intrinsic specificity ratio may be a useful new criterion and a complement to affinity in drug screening and in searching for potential drug lead compounds.

Ligand effect on the performance of organic light-emitting diodes based on europium complexes

A series of europium complexes were synthesized and their electroluminescent (EL) characteristics were studied. It was found by comparison that the different substituted groups, such as methyl, chlorine, and nitryl, on ligand 1,10-phenanthroline affect significantly the EL performance of devices based on these complexes. The more methyl-substituted groups on ligand 1,10-phenanthroline led to higher device efficiency. A chlorine-substituted group showed the approximate EL performance as two methyl-substituted groups, whereas a nitryl substituent reduced significantly the EL luminous efficiency. However, beta-diketonate ligand TTA and DBM exhibited similar EL performance. The improved EL luminous efficiency by proper substituted groups on the 1, 10-phenanthroline was attributed to the reduction of the energy loss caused by light hydrogen atom vibration, as well as concentration quenching caused by intermolecular interaction, and the match of energy level between the ligand and Eu3+.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Formation of Novel Di-Nuclear Lanthanide Luminescent Sm(III), Eu(III) and Tb(III) Triple Stranded Helicates from a C2 Symmetrically 2,6-Dicarboxylic Amide based 1,3-Xylene Ligand in CH3CN

The synthesis of the C2-symmetrical ligand 1 consisting of two naphthalene units connected to two pyridine-2,6-dicarboxamide moieties linked by a xylene spacer and the formation of LnIII-based (Ln1/4 Sm, Eu, Tb, and Lu) dimetallic helicates [Ln2 · 13] in MeCN by means of a metal-directed synthesis is described. By analyzing the metal-induced changes in the absorption and the fluorescence of 1, the formation of the helicates, and the presence of a second species [Ln2 · 12] was confirmed by nonlinear- regression analysis. While significant changes were observed in the photophysical properties of 1, the most dramatic changes were observed in the metal-centred lanthanide emissions, upon excitation of the naphthalene antennae. From the changes in the lanthanide emission, we were able to demonstrate that these helicates were formed in high yields (ca. 90% after the addition of 0.6 equiv. of LnIII), with high binding constants, which matched well with that determined from the changes in the absorption spectra. The formation of the LuIII helicate, [ Lu2 · 13 ] , was also investigated for comparison purposes, as we were unable to obtain accurate binding constants from the changes in the fluorescence emission upon formation of [Sm2 · 13], [Eu2 · 13], and [Tb2 · 13].

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.