Preliminary study of the thermally stimulated blue luminescence of ulexite

In this paper, novel results on the blue thermally stimulated luminescence (TSL) emission of ulexite (NaCaB₅O₆(OH)₆·5H₂O) have been studied. The four maxima appearing at 60, 110, 200 and 240°C on the TSL glow curves of this borate could be respectively associated to: (i) the first dehydration (NaCaB₅O₆(OH)₆·5H₂O→NaCaB₅O₆(OH)₆·3H₂O), (ii) the creation-annihilation of the three-hydrated phase, (iii) the Na-coordinated chains dehydroxylation and the starting point of the alkali self-diffusion through the lattice and (iv) the amorphisation of the lattice. These results are fairly well correlated with the differential thermal analyses (DTA), in situ thermal observations under environmental scanning electron microscope (TESEM) and thermal X-ray diffraction (TXRD) techniques.     

Titanium Dioxide/Electrolyte Solution Interface: Electron Transfer Phenomena

Electron transfer between a titanium dioxide/electrolyte solution interface has been studied. As found by other researchers of similar interfaces (TiO(2)- and ZnO-electrolyte solution), a slow consumption of OH(-) ions takes place in this type of interface. A theoretical model has been developed for calculating the change in the Fermi energy of both electrolyte solution and semiconductor, showing that ion consumption from the solution is favoured by the decrease of the difference between their Fermi energies. A kinetic constant (upsilon) is found to characterise the consumption process, its value increasing with electrolyte and semiconductor mass concentrations. Furthermore, this process may be used to estimate the point of zero charge of a titanium dioxide colloidal dispersion. Copyright 2000 Academic Press.     

Dichlorocarbene Addition to

In contrast to the terminal phosphinidene complex PhPW(CO)(5) (2), which adds to [5]metacyclophane (1) in a 1,4-fashion, dichlorocarbene preferentially adds in a 1,2-fashion to the formal "anti-Bredt" type double bond of the aromatic ring of 1 to afford the norcaradiene 11b, which immediately rearranges to the bridged cycloheptatriene 12b and further by a [1,5] sigmatropic chlorine migration to the isomeric 13b as the first observable product. More slowly, the latter isomerizes via a dissociative mechanism to give 15b. A computational study supports the notion that the [1,5] chlorine migration in the rearrangement 12b --> 13b, for which an activation barrier of 70.2 kJ mol(-)(1) was calculated, is essentially concerted with minor charge separation. In contrast, the analogous [1,5] chlorine migration in the flat model compound 7,7-dichlorocycloheptatriene (12a) displays features of a dissociative pathway.     

Theoretical study of GSK−3<Emphasis Type="Italic">α</Emphasis>: neural networks QSAR studies for the design of new inhibitors using 2D descriptors

Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK−3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK−3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK−3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK−3α inhibitors against different molecules found in the literature.