New ruthenium(II) complexes of functionalized monoanionic aryldiamine N,C,N'-terdentate ligands: Syntheses of [Ru-II{2,6-(Me(2)NCH(2))(2)-4-R-C6H2}(terpy)]Cl+(-); X-ray structure of a dimeric organolithium compound,[Li{2,6-(Me(2)NCH(2))(2)-4-Ph-C6H2}](2)

The new anionic functionalized aryldiamine ligands [2,6-(Me(2)NCH(2))(2)-4-R-C6H2](-) (R = Me(3)SiC=C, C6H5, Me(3)Si), formally derived from [2,6-(Me(2)NCH(2))(2)C6H3](-), have been prepared as their lithium compounds. The compound [Li{2,6-(Me(2)NCH(2))(2)-4-Ph-C6H2}](2) crystallizes in the monoclinic space group C2/c (no. 15) with a = 13.1225(5), b = 13.5844(7), c = 15.9859(12) Angstrom, beta = 105.329(5)degrees, V = 3264.0(3)Angstrom(3), Z = 4. The structure refinement converged to R(1) = 0.0374 for 2037 observed reflections [F-o>4 sigma(F-o)] and wR(2) = 0.0922 for 2560 unique data. The organolithium compounds have been used in transmetalation reactions to give the corresponding functionalized organoruthenium(II) complexes [Ru-II{2,6-(Me(2)NCH(2))(2)-4-R-C6H2}(terpy)]Cl-+(-) (terpy = 2,2';6',2 ''-terpyridine). The Ru-II species with R = HC = C has also been synthesized.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Spin-Based Diagnostic of Nanostructure in Copper Phthalocyanine-C-60 Solar Cell Blends

Nanostructure and molecular orientation play a crucial role in determining the functionality of organic thin films. In practical devices, such as organic solar cells consisting of donor-acceptor mixtures, crystallinity is poor and these qualities cannot be readily determined by conventional diffraction techniques, while common microscopy only reveals surface morphology. Using a simple nondestructive technique, namely, continuous-wave electron paramagnetic resonance spectroscopy, which exploits the well-understood angular dependence of the g-factor and hyperfine tensors, we show that in the solar cell blend of C-60 and copper phthalocyanine (CuPc)-for which X-ray diffraction gives no information-the CuPc, and by implication the C-60, molecules form nanoclusters, with the planes of the CuPc molecules oriented perpendicular to the film surface. This information demonstrates that the current nanostructure in CuPc:C-60 solar cells is far from optimal and suggests that their efficiency could be considerably increased by alternative film growth algorithms.