The solvation and electrochemical behaviour of Copper acetylacetonate complexes in ionic liquids

The behavior of copper(II) complexes of pentane-2,4-dione and 1,1,1,5,5,5-hexafluoro-2,4-pentanedione, [Cu(acac)(2) (1) and [Cu(HFacac)(2)(H2O)] (2), in ionic liquids and molecular organic solvents, was studied by spectroscopic and electrochemical techniques. The electron paramagnetic resonance characterization (EPR) showed well-resolved spectra in most solvents. In general the EPR spectra of [Cu(acac)(2)] show higher g(z) values and lower hyperfine coupling constants, A(z), in ionic liquids than in organic solvents, in agreement with longer Cu-O bond lengths and higher electron charge in the copper ion in the ionic liquids, suggesting coordination of the ionic liquid anions. For [Cu(HFacac)(2)(H2O)] the opposite was observed suggesting that in ionic liquids there is no coordination of the anions and that the complex is tetrahedrically distorted. The redox properties of the Cu(II) complexes were investigated by cyclic voltammetry (CV) at a Pt electrode (d = 1 mm), in bmimBF(4) and bmimNTf(2) ionic liquids and, for comparative purposes, in neat organic solvents. The neutral copper(II) complexes undergo irreversible reductions to Cu(I) and Cu(0) species in both ILs and common organic solvents (CH2Cl2 or acetonitrile), but, in ILs, they are usually more easier to reduce (less cathodic reduction potential) than in the organic solvents. Moreover, 1 and 2 are easier to reduce in bmimNTf(2) than in bmimBF(4) ionic liquid. (C) 2013 Elsevier B.V. All rights reserved.

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

The solvation and redox behaviour of mixed ligand COPPER(II) complexes of acetylacetonate and aromatic dimines in ionic liquids

The behavior of two cationic copper complexes of acetylacetonate and 2,2'-bipyridine or 1,10-phenanthroline, [Cu(acac)(bipy)]Cl (1) and [Cu(acac)(phen)]Cl (2), in organic solvents and ionic liquids, was studied by spectroscopic and electrochemical techniques. Both complexes showed solvatochromism in ionic liquids although no correlation with solvent parameters could be obtained. By EPR spectroscopy rhombic spectra with well-resolved superhyperfine structure were obtained in most ionic liquids. The spin Hamiltonian parameters suggest a square pyramidal geometry with coordination of the ionic liquid anion. The redox properties of the complexes were investigated by cyclic voltammetry at a Pt electrode (d = 1 mm) in bmimBF(4) and bmimNTf(2) ionic liquids. Both complexes 1 and 2 are electrochemically reduced in these ionic media at more negative potentials than when using organic solvents. This is in agreement with the EPR characterization, which shows lower A(z) and higher g(z) values for the complexes dissolved in ionic liquids, than in organic solvents, due to higher electron density at the copper center. The anion basicity order obtained by EPR is NTf2-, N(CN)(2)(-), MeSO4- and Me2PO4-, which agrees with previous determinations. (C) 2013 Elsevier B.V. All rights reserved.

Fast unsupervised technique for extraction of endmembers spectra from hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the linear unmixing. The key to linear unmixing is to find the set of reference substances, also called endmembers, that are representative of a given scene. This paper presents the vertex component analysis (VCA) a new method to unmix linear mixtures of hyperspectral sources. The algorithm is unsupervised and exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O (n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.

Fast unsupervised extraction of endmembers spectra from hyperspectral data

Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.