50 research outputs found

    Structures and Spin States of Crystalline [Fe(NCS)2L2] and [FeL3]2+ Complexes (L = an Annelated 1,10-Phenanthroline Derivative)

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    The phase behaviour and spin states of [Fe(NCS)2(dpq)2] (1; dpq = dipyrido[3,2-f:2′,3′-h]quinoxaline), [Fe(NCS)2(dppz)2] (2; dppz = dipyrido[3,2-a:2′3′-c]phenazine) and [Fe(NCS)2(dppn)2] (3; dppn = dipyrido[3,2-a:2′3′-c]benzophenazine) have been investigated. Solvent-free 1 and 2 are isostructural and low-spin in the crystalline state, in contrast to previously published 2·py (py = pyridine) which exhibits a hysteretic spin-crossover (SCO) transition near 140 K. The inactivity of 1 and 2 towards SCO may relate to their more crowded intermolecular lattice environment, particularly two very short intermolecular anion⋯π contacts involving the NCS− ligands. Two solvate phases of 1 are also described, including 1·2py which undergoes gradual SCO with T½ca. 188 K. Bulk samples of 2 and 3 are predominantly low-spin and isostructural with the crystals of 2 by powder diffraction, but bulk samples of 1 contain an extra phase that exhibits hysteretic SCO, but was not crystallographically characterised. Crystal structures of low-spin [Fe(dppz)3][ClO4]2 (4) and a solvate of [Fe(dppn)3][BF4]2 (5) are also described, which are the first homoleptic complexes of these ligands to be crystallographically characterised

    An active chaotic micromixer integrating thermal actuation associating PDMS and silicon microtechnology

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    Due to scaling laws, in microfluidic, flows are laminar. Consequently, mixing between two liquids is mainly obtained by natural diffusion which may take a long time or equivalently requires centimetre length channels. To reduce time and length for mixing, it is possible to generate chaotic-like flows either by modifying the channel geometry or by creating an external perturbation of the flow. In this paper, an active micromixer is presented consisting on thermal actuation with heating resistors. In order to disturb the liquid flow, an oscillating transverse flow is generated by heating the liquid. Depending on the value of boiling point, either bubble expansion or volumetric dilation controlled the transverse flow amplitude. A chaotic like mixing is then induced under particular conditions depending on volume expansion, liquid velocity, frequency of actuation... This solution presents the advantage to achieve mixing in a very short time (1s) and along a short channel distance (channel width). It can also be integrated in a more complex device due to actuator integration with microfluidics

    Efficient Implementation of Short Fundamental Equations of State for the Numerical Simulation of Dense Gas Flows

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    An artificial neural network (ANN) of the multi-layer perceptron (MLP) type is used to generate an explicit auxiliary thermodynamic equation, whose mathematical form is particularly well suited for implementation within Computational Fluid Dynamics (CFD) solvers. This equation directly relates the thermodynamic quantity of interest (tempera- ture) to the conservative variables (density, momentum per unit volume, total energy per unit volume), via the density and the internal energy per unit volume e. The resulting relationship, of the form T = T(, e), is added to the usual thermal and caloric equations of state, in order to avoid expensive iterative computations of the temperature. We select 15 dense gases of industrial interest, whose thermodynamic properties can be described by the 12-parameter Span-Wagner fundamental equation. The accuracy and computa- tional cost of the proposed formulation are verified a priori, via detailed comparisons with data provided by the baseline thermodynamic model, and a posteriori, by propagating the approximated thermodynamic model through a numerical flow simulation. Results are shown for transonic dense gas flows through a two-dimensional turbine cascade, for sample thermodynamic conditions close to the saturated vapor line. A 53% average reduction in computation time is observed, and the convergence and numerical stability of the numerical solution is greatly enhanced, while deviations less than 3% are observed on the computed quantities of interest with respect to the baseline solver
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