An investigation of the activation process of high temperature shift catalyst

The aim of this work is to address the activation process of a high temperature shift (HTS) catalyst, composed of Fe2O3/Cr2O3/CuO, by analyzing it before activation (HTS-V) and after activation (HTS-A) using complementary characterization techniques. The textural and morphological characterizations were done by transmission electron rnicroscopy (TEM) and nitrogen physisorption at 77 K; crystallographic structure was confirmed by X-ray diffraction (XRD); electronic structure was analyzed by X-ray absorption spectroscopy (XAS) and the chemical composition of the catalyst`s surface was obtained by X-ray photoelectron spectroscopy (XPS). The investigation pointed out that the HTS-V catalyst presents good textural and morphological properties, which are not deeply affected by the activation process (sample HTS-A). The iron oxide phase in the HTS-V catalyst is hematite whereas in HTS-A catalyst is magnetite with Fe2+/Fe3+ ratio close to the expected value (0.5). For both samples, the Cr ions seem to be incorporated in the iron oxide lattice with higher concentration at particle surface. In the HTS-V catalyst, the Cu ions have oxidation number II and occupy in average distorted octahedral sites; after the activation, the Cu ions are partially reduced, suggesting that the reduction of the Cu species is complex. (C) 2007 Elsevier B.V. All rights reserved.

A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Arylpiperazine compounds are promising 5-HT1A receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT1A receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and b, the high correlation coefficients (q(2) = 0.76, r(2) = 0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT1A receptor ligands that are able to improve antidepressant treatment. (c) 2007 Elsevier Masson SAS. All rights reserved.