Substituierte 2-(Thiazol-4-yl)-phenole als Liganden und potentielle Extraktionsmittel für Kupfer(II)

Substituted 2-(Thiazol-4-yl)-phenols as Ligands and Potential Extractants for Copper (II) Substituted 2-(thiazol-4-yl)-phenols are obtained by the HANTZSCH synthesis. Their solubility in toluene is higher than in n-octane depending on the position, the chain length, and the polarity of the substituents. From alcoholic solutions complexes of the type CuL₂ⁿ are precipitated. According to the substituents their structure is distorted octahedral (CuL₂², CuL₂³) or distorted tetrahedral (CuL₂⁸, CuL₂⁹). The new complexes are nearly insoluble in toluene and aliphatic hydrocarbons, but CuL₂⁸ and CuL₂⁹ are soluble in chloroform. Therefore copper(II) may be extracted by HL⁸ and HL⁹ using chloroform as a diluent.     

Models of steroid binding based on the minimum deviation of structurally assigned 13C NMR spectra analysis (MiDSASA)

This paper develops a quantitative k-nearest neighbors modeling technique. The technique is used to demonstrate that a compound's biological binding activity to a receptor can be calculated from the minimum of the square root of the sum of squared deviations (SSSD) of a structurally assigned chemical shift on a template between the unknown compound to be predicted and a set of known compounds with known activities. When building models of biological activity, nonlinear relationships are built into the input training data. If a model is developed by selecting only compounds with minimum structurally assigned chemical shift deviations from the unknown compound, some of the nonlinear relationships can be removed. The smaller the total chemical shift deviation between a compound with known activity and another compound with unknown activity, the more likely it will have similar biological, chemical, and physical properties. This means that a model can be produced without rigorous statistics or neural networks. This technique is similar to structure-activity relationship (SAR) modeling, but instead of relying on substructure fragments to produce a model, this new model is based on minimum chemical shift differences on those substructure fragments. We refer to this method as minimum deviation of structurally assigned spectra analysis (MiDSASA) modeling. Modeling by the minimum deviation concept can be applied to other chemoinformatic data analyses such as metabolite concentrations in metabolic pathways for metabolomics research. A MiDSASA template model for 30 steroids binding the corticosterone binding globulin based on the activity factors of the two nearest compounds had a correlation of 0.88. A MiDSASA template model for 50 steroids binding the aromatse enzyme based on the average activity of the four nearest compounds had a correlation of 0.71.     

Random Copolymers of 1,5-Dioxepan-2-one

ABSTRACT

Copolymers of 1,5-dioxepan-2-one (DXO) and e-caprolactone (?-CL), δ-valerolactone (δ-VL) or L-lactide (LLA) have been synthesized and characterized. High molecular weight copolymers were obtained using stannous-2-ethyl hexanoate as catalyst in bulk. Reactivity ratios for the copolymerization of DXO and δ-VL were determined at 110°C as r_VL=0.5 and r_DXO=2.3. At high conversion, depolymerization of δ-VL occurred, resulting in lower molecular weight and variations in the copolymer composition.

Physical properties, such as crystallinity and melting temperature of the DXO-copolymers proved to be strongly dependent on the choice of comonomer and on the molar composition of the copolymers. DXO appears to be incorporated into the poly-?-caprolactone (PCL) crystals and to some extent into the poly-δ-valerolactone (PVL) crystals, resulting in a more gradual decrease in crystallinity with increasing amount of DXO.     

Fusion around the barrier for 7Li+12C

Fusion cross-sections for the ⁷Li + ¹²C reaction have been measured at energies above the Coulomb barrier by the direct detection of evaporation residues. The heavy evaporation residues with energies below 3 MeV could not be separated out from the α-particles in the spectrum and hence their contribution was estimated using statistical model calculations. The present work indicates that suppression of fusion cross-sections due to the breakup of ⁷Li may not be significant for ⁷Li + ¹²C reaction at energies around the barrier.     

Secondary ion yields produced by keV atomic and polyatomic ion impacts on a self-assembled monolayer surface

A suite of keV polyatomic or 'cluster' projectiles was used to bombard unoxidized and oxidized self-assembled monolayer surfaces. Negative secondary ion yields, collected at the limit of single ion impacts, were measured and compared for both molecular and fragment ions. In contrast to targets that are orders of magnitude thicker than the penetration range of the primary ions, secondary ion yields from polyatomic projectile impacts on self-assembled monolayers show little to no enhancement when compared with monatomic projectiles at the same velocity. This unusual trend is most likely due to the structural arrangement and bonding characteristics of the monolayer molecules with the Au(111). Copyright 1999 John Wiley & Sons, Ltd.     

Metallextraktion und Komplexbildung mit α-Aminoximen

Metal Extraction and Complex Formation by α-Amino Oximes Cyclic secondary α-amino oximes (RNHR′NOH) in toluene as a diluent extract copper(II) from weakly acid solutions. The distribution ratio D_Cu amounts to 80. The extraction process is connected with the elimination of two protons (formation of [Cu(RNHR′NOH)₂? H]NO₃). Apart from this 1,2-complex 1,1-complexes Cu(RNHR′NOH)X₂ (X ? Cl, Br) are obtained. A blue and a black isomer of Cu(RNHDodNOH)Cl₂ are described. According to x-ray structure analysis, the latter isomer is square-planar with the amino oxime in the E-configuration. Basing on the i.r. spectrum, for the blue isomer a monodentate coordination of the Z-isomer of RNHDodNOH is discussed. In the case of nickel(II) only the 1,2-chelates [Ni(RNHR′NOH)₂? H]Y (Y ? NO₃, ClO₄, I), in the case of palladium(II) only the 1,1-chelates Pd(RNHR′NOH)Cl₂ were isolated.     

Aus der analytischen Praxis

Ohne ZusammenfassungMitteilungen aus der Königl. Württembergischen landwirtschaftlichen Versuchsstation Hohenheim.     

Use of 13C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity

We have developed a spectroscopic data-activity relationship (SDAR) model based on 13C NMR spectral data for 30 estrogenic chemicals whose relative binding affinities (RBA) are available for the alpha (ERalpha) and beta (ERbeta) estrogen receptors. The SDAR models segregated the 30 compounds into strong and medium binding affinities. The SDAR model gave a leave-one-out (LOO) cross-validation of 90%. Two compounds that were classified incorrectly in the SDAR model were in the transition zone between classifications. Real and predicted 13C NMR chemical shifts were used with test compounds to evaluate the predictive behavior of the SDAR model. The 13C NMR SDAR model using predicted 13C NMR data for the test compounds provides a rapid, reliable, and simple way to screen whether a compound binds to the estrogen receptors.     

Producing 13C NMR, infrared absorption, and electron ionization mass spectrometric data models of the monodechlorination of chlorobenzenes, chlorophenols, and chloroanilines

We have developed four spectroscopic data-activity relationship (SDAR) models of monodechlorination of 32 chlorinated benzene compounds in anaerobic estuarine sediment. The SDAR models were based on combinations of 13C nuclear magnetic resonance (NMR), infrared absorption (IR), and electron ionization mass spectrometric (EI MS) data. The SDAR models segregated the 32 compounds into 17 readily monodechlorinated compounds and 15 not readily monodechlorinated compounds. The SDAR model based on 13C NMR, IR, and EI MS data gave a leave-one-out cross-validation of 93.8%. The SDAR model based on a composite of 13C NMR and IR data gave a leave-one-out cross-validation of 90.6%. The SDAR model based on a composite of IR and EI MS data gave a leave-one-out cross-validation of 84.4%. The SDAR model based on a composite of 13C NMR and EI MS data gave a leave-one-out cross-validation of 84.4%. These reliable SDAR models provide a rapid and simple way to predict whether a chlorinated benzene compound will readily go through monodechlorination. The FDA has filed a patent application on methods of using any combination of spectral data (NMR, MS, UV-vis, IR, and fluorescence, phosphorescence) to model a chemical, physical, or biological endpoint.