Nickel(II) and copper(II) complexes of unsymmetrical tetradentate reduced Schiff base ligands

Two new reduced Schiff base ligands, [HL1 = 4-(2-[(pyridin-2-ylmethyl)-amino]-ethylimino)-pentan-2-one and HL2 =4-[2-(1-pyridin-2-yl-ethylamino)-ethylimino]-pentan-2-one] have been prepared by reduction of the corresponding tetradentate unsymmetrical schiff bases derived from 1.1: 1 condensation of 1,2-ethanediamine, acetylacetone and pyridine-2-carboxaldehyde/2-acetyl pyridine. Four complexes, [Ni(L-1)]ClO4 (1), [Cu(L-1)]ClO4 (2). [Ni(L-2)]ClO4 (3). and [Cu(L-2)]ClO4 (4) with these two reduced Schiff base ligands have been synthesized and structurally characterized by X-ray crystallography. The mono-negative ligands L-1 and L-2 are chelated in all four complexes through the four donor atoms to form square planar nickel(II) and copper(II) complexes Structures of 3 and 4 reveal that enantiomeric pairs are crystallized together with opposite chirality in the nitrogen and carbon atoms. The two Cu-II complexes (2 and 4) exhibit both irreversible reductive (Cu-II/Cu-II, E-pc. -1.00 and -1.04 V) and oxidative (Cu-II/CUII, E-pa, + 1.22 and + 1.17 V, respectively) responses in cyclic voltammetry. The electrochemically generated Cu-1 species for both the complexes are unstable and undergo disproportionation.

Spectro-electrochemical and DFT studies of a planar Cu(II)–phenolate complex active in the aerobic oxidation of primary alcohols

A square-planar compound [Cu(pyrimol)Cl] (pyrimol = 4-methyl-2-N-(2-pyridylmethylene)aminophenolate) abbreviated as CuL–Cl) is described as a biomimetic model of the enzyme galactose oxidase (GOase). This copper(II) compound is capable of stoichiometric aerobic oxidation of activated primary alcohols in acetonitrile/water to the corresponding aldehydes. It can be obtained either from Hpyrimol (HL) or its reduced/hydrogenated form Hpyramol (4-methyl-2-N-(2-pyridylmethyl)aminophenol; H2L) readily converting to pyrimol (L-) on coordination to the copper(II) ion. Crystalline CuL–Cl and its bromide derivative exhibit a perfect square-planar geometry with Cu–O(phenolate) bond lengths of 1.944(2) and 1.938(2) Å. The cyclic voltammogram of CuL–Cl exhibits an irreversible anodic wave at +0.50 and +0.57 V versus ferrocene/ferrocenium (Fc/Fc+) in dry dichloromethane and acetonitrile, respectively, corresponding to oxidation of the phenolate ligand to the corresponding phenoxyl radical. In the strongly donating acetonitrile the oxidation path involves reversible solvent coordination at the Cu(II) centre. The presence of the dominant CuII–L. chromophore in the electrochemically and chemically oxidised species is evident from a new fairly intense electronic absorption at 400–480 nm ascribed to a several electronic transitions having a mixed pi-pi(L.) intraligand and Cu–Cl -> L. charge transfer character. The EPR signal of CuL–Cl disappears on oxidation due to strong intramolecular antiferromagnetic exchange coupling between the phenoxyl radical ligand (L.) and the copper(II) centre, giving rise to a singlet ground state (S = 0). The key step in the mechanism of the primary alcohol oxidation by CuL–Cl is probably the alpha-hydrogen abstraction from the equatorially bound alcoholate by the phenoxyl moiety in the oxidised pyrimol ligand, Cu–L., through a five-membered cyclic transition state.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

The interplay of secondary Hg⋯S, Hg⋯N and Hg⋯π bonding interactions in supramolecular structures of phenylmercury(ii) dithiocarbamates

Three new phenylmercury(II) and one mercury(II) dithiocarbamate complexes viz. PhHg S2CN(PyCH2) Bz (1), PhHg S2CN(PyCH2)CH3 (2), PhHg S2CN(Bz)CH3 (3), and [Hg (NCS2(PyCH2)Bz)(2)] (4) (Py = pyridine; Bz = benzyl) have been synthesized and characterized by elemental analyses, IR, electronic absorption, H-1 and C-13 NMR spectroscopy. The crystal structures of 1, 2 and 3 showed a linear S-Hg-C core at the centre of the molecule, in which the metal atom is bound to the sulfur atom of the dithiocarbamate ligand and a carbon atom of the aromatic ring. In contrast the crystal structure of 4 showed a linear S-Hg-S core at the Hg(II) centre of the molecule. Weak intermolecular Hg center dot center dot center dot N (Py) interactions link molecules into a linear chain in the case of 1, whereas chains of dimers are formed in 2 through intermolecular Hg center dot center dot center dot N (Py) and Hg center dot center dot center dot S interactions. 3 forms a conventional face-to-edge dimeric structure through intermolecular Hg center dot center dot center dot S secondary bonding and 4 forms a linear chain of dimers through face-to-face Hg center dot center dot center dot S secondary bonding. In order to elucidate the nature of these secondary bonding interactions and the electronic absorption spectra of the complexes, ab initio quantum chemical calculations at the MP2 level and density functional theory calculations were carried out for 1-3. Complexes 1 and 2 exhibited photoluminescent properties in the solid state as well as in the solution phase. Studies indicate that Hg center dot center dot center dot S interactions decrease and Hg center dot center dot center dot N interactions increase the chances of photoluminescence in the solid phase

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.