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A new polyoxovanadium cluster compound, [VO{(OCHCH)N(CHCHOH)}]·0.5CHCN, was synthesized and characterized by single-crystal X-ray diffraction analysis, FTIR and UV-vis spectroscopy, and TGA. The cluster is composed of a fully reduced cyclic {VNO} framework, which adopts an Anderson-like structure and is comprised of a ring of six edge-sharing {VON} octahedra incorporating six {(OCHCH)N(CHCHOH)} ligands. Two (OCHCH-) arms of each of the six triethanolamine ligands are directly incorporated into the oxometalate core and the third {-CHCHOH} arm remains pendant. In the condensed phase, the clusters form discrete hcp layers through inter-cluster hydrogen bonding. These layers stack through soft chemical interactions to form a 3D network structure. The neutral cluster, [VO{(OCHCH)N(CHCHOH)}], is the isopolyoxovanadium analogue to the cationic clusters contained in a series of heteropolyoxovanadium compounds previously introduced by our laboratory, e.g., [LiVO{(OCHCH)N(CHCHOH)}]; its existence shows that a heteroatom is not required to form or stabilize the common organofunctionalized vanadium oxide framework: [VO{(OCHCH)N(CHCHOH)}]. To the best of our knowledge, the isopolyoxovanadium and heteropolyoxovanadium clusters represent the first reported isopoly-heteropoly analogues in the polyoxometalate field. We compare the TGA profile, FTIR and UV-vis spectra of the new compound with two of its cationic heteropoly analogues.

A new diethanolamine functionalized oxovanadium cationic cluster was synthesized and characterized; electrochemical and UV-vis absorption properties are consistent with two distinct MLCT processes.

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In this paper, we study how to compute the optimal capacity planning in a multi-radio multi-channel (MR-MC) wireless network, that is, to find solutions for a set of coupled problems including channel assignment, scheduling, and routing, with the objective to optimize network capacity. The current state of the art mainly resorts to formulation of a mixed integer programming problem, which is NP-hard in general, and then computes an approximate solution to such a problem. We develop a novel concept of multi-dimensional conflict graph (MDCG) in this paper. Based on MDCG, the optimal capacity planning can be modeled as a linear programming (LP) multicommodity flow (MCF) problem, augmented with constraints derived from the MDCG. The MDCG-based MCF solution will provide not only the maximum throughput or utility, but also the optimal channel assignment, scheduling and routing to achieve it. Moreover, the MDCG-based optimal capacity planning can exploit dynamic channel swapping, which is difficult to achieve for those existing heuristic algorithms. Numerical results are presented to demonstrate the efficiency of the MDCG-based capacity planning, with comparison to the well-known heuristic algorithm presented in [1].