Structural Characterization of Rare-Earth Doped Yttrium Aluminoborate Laser Glasses Using Solid State NMR

The structure of laser glasses in the system (B(2)O(3))(0.6){(Al(2)O(3))(0.4-x)(Y(2)O(3))(x)} (0.1 <= x <= 0.25) has been investigated by means of (11)B, (27)Al, and (89)Y solid state NMR as well as Y-3d core-level X-ray photoelectron spectroscopy, (11)B magic-angle spinning (MAS) NMR spectra reveal that the majority of the boron atoms are three-coordinated, and a slight increase of four-coordinated boron content with increasing x can be noticed. (27)Al MAS NMR spectra show that the alumina species are present in the coordination states four, five and six. All of them are in intimate contact with both the three- and the four-coordinate boron species and vice versa, as indicated by (11)B/(27)Al rotational echo double resonance (REDOR) data. These results are consistent with the formation of a homogeneous, nonsegregated glass structure. For the first time, (89)Y solid state NMR has been used to probe the local environment of Y(3+) ions in a glass-forming system. The intrinsic sensitivity problem associated with (89)Y NMR has been overcome by combining the benefits of paramagnetic doping with those of signal accumulation via Carr-Purcell spin echo trains. Both the (89)Y chemical shifts and the Y-3d core level binding energies are found to be rather sensitive to the yttrium bonding state and reveal that the bonding properties of the yttrium atoms in these glasses are similar to those found in the model compounds YBO(3) and YAl(3)(BO(3))(4), Based on charge balance considerations as well as (11)B NMR line shape analyses, the dominant borate species are concluded to be meta- and pyroborate anions.

Polymeric electro-mechanic devices applied to antibiotic-controlled release

Polymeric electroactive blends formed by electropolymerized aniline inside a non-conductive polyacrylamide porous matrix were already shown as suitable materials for the electrocontrolled release of model compounds like safranin. In this paper the intermolecular interactions between the two components of the blend are put in evidence by Raman spectroscopy measurements. Also, in situ optical microscopy was used to follow changes occurring in the polyaniline/polyacrylamide blend during pyrocathecol violet release tests. These two sets of experiments show the possibility of controlling electrochemically the release of both, safranin (a cation) and pyrocathecol violet (an anion) and allow to infer a release mechanism based on the electromechanical properties of the blends explaining the dependence of the release kinetics on the applied potential. Tetracycline release curves for different potentials and pHs are shown and the obtained profiles are in agreement with those expected for a device acting as an electrochemically driven pump due to the artificial muscle properties of the conducting phase of the blends. (c) 2007 Elsevier B.V. All rights reserved.

Monodisperse lignin fractions as standards in size-exclusion analysis Comparison with polystyrene standards

The difficulty of preparing monodisperse lignin fractions on a large scale is a limiting factor in many applications. The present paper addresses this problem by examining the properties and size-exclusion behavior of lignin isolated by the acetosolv pulping process from post-extraction crushed sugarcane bagasse. The isolated lignin was subjected to a solvent pretreatment, followed by preparative gel permeation chromatography fractionation. The fractions were analyzed by high-performance size-exclusion chromatography (HPSEC) and these samples showed a great decrease in polydispersity, compared to the original acetosolv lignin. Several fractions of very low polydispersity, close to unity, were employed as calibration curve standards in HPSEC analysis. This original analytical approach allowed calibration with these lignin fractions to be compared with the polystyrene standards that are universally employed for lignin molecular mass determination. This led to a noteworthy result, namely that the lignin fractions and polystyrene standards showed very similar behavior over a large range of molecular masses in a typical HPSEC analysis of acetosolv lignin. (C) 2009 Elsevier B.V. All rights reserved.

Deterpenation of Bergamot Essential Oil Using Liquid-Liquid Extraction: Equilibrium Data of Model Systems at 298.2 K

The deterpenation of bergamot essential oil can be performed by liquid liquid extraction using hydrous ethanol as the solvent. A ternary mixture composed of 1-methyl-4-prop-1-en-2-yl-cydohexene (limonene), 3,7-dimethylocta-1,6-dien-3-yl-acetate (linalyl acetate), and 3,7-dimethylocta-1,6-dien-3-ol (linalool), three major compounds commonly found in bergamot oil, was used to simulate this essential oil. Liquid liquid equilibrium data were experimentally determined for systems containing essential oil compounds, ethanol, and water at 298.2 K and are reported in this paper. The experimental data were correlated using the NRTL and UNIQUAC models, and the mean deviations between calculated and experimental data were lower than 0.0062 in all systems, indicating the good descriptive quality of the molecular models. To verify the effect of the water mass fraction in the solvent and the linalool mass fraction in the terpene phase on the distribution coefficients of the essential oil compounds, nonlinear regression analyses were performed, obtaining mathematical models with correlation coefficient values higher than 0.99. The results show that as the water content in the solvent phase increased, the kappa value decreased, regardless of the type of compound studied. Conversely, as the linalool content increased, the distribution coefficients of hydrocarbon terpene and ester also increased. However, the linalool distribution coefficient values were negatively affected when the terpene alcohol content increased in the terpene phase.

Prediction of Viscosities of Fatty Compounds and Biodiesel by Group Contribution

In the present work, a group contribution method is proposed for the estimation of viscosity of fatty compounds and biodiesel esters as a function of the temperature. The databank used for regression of the group contribution parameters (1070 values for 65 types of substances) included fatty compounds, such as fatty acids, methyl and ethyl esters and alcohols, tri- and diacylglycerols, and glycerol. The inclusion of new experimental data for fatty esters, a partial acylglycerol, and glycerol allowed for a further refinement in the performance of this methodology in comparison to a prior group contribution equation (Ceriani, R.; Goncalves, C. B.; Rabelo, J.; Caruso, M.; Cunha, A. C. C.; Cavaleri, F. W.; Batista, E. A. C.; Meirelles, A. J. A. Group contribution model for predicting viscosity of fatty compounds. J. Chem. Eng. Data 2007, 52, 965-972) for all classes of fatty compounds. Besides, the influence of small concentrations of partial acylglycerols, intermediate compounds in the transesterification reaction, in the viscosity of biodiesels was also investigated.

Ozone formation potentials of volatile organic compounds and ozone sensitivity to their emission in the megacity of Sao Paulo, Brazil

In the present study, a three-dimensional Eulerian photochemical model was employed to estimate the impact that organic compounds have on tropospheric ozone formation in the Metropolitan Area of Sao Paulo (MASP). In the year 2000, base case simulations were conducted in two periods: August 22-24 and March 13-15. Based on the pollutant concentrations calculated by the model, the correlation coefficient relative to observations for ozone ranged from 0.91 to 0.93 in both periods. In the simulations employed to evaluate the ozone potential of individual VOCs, as well as the sensitivity of ozone to the VOC/NO(x) emission ratio, the variation in anthropogenic emissions was estimated at 15% (according to tests performed previously variations of 15% were stable). Although there were significant differences between the two periods, ozone concentrations were found to be much more sensitive to VOCs than to NO(x) in both periods and throughout the study domain. In addition, considering their individual rates of emission from vehicles, the species/classes that were most important for ozone formation were as follows: aromatics with a kOH>2x 10(4) ppm(-1) min(-1); olefins with a kOH 7 x 10(4) ppm(-1) min(-1); olefins with a kOH 7 x 10(4) ppm(-1) min(-1); ethene; and formaldehyde, which are the principal species related to the production, transport, storage and combustion of fossil fuels.

Non-Fermi-liquid behavior in UCu(4+x)Al(8-x) compounds

We report on experimental studies of the Kondo physics and the development of non-Fermi-liquid scaling in UCu(4+x)Al(8-x) family. We studied 7 different compounds with compositions between x = 0 and 2. We measured electrical transport (down to 65 mK) and thermoelectric power (down to 1.8 K) as a function of temperature, hydrostatic pressure, and/or magnetic field. Compounds with Cu content below x = 1.25 exhibit long-range antiferromagnetic order at low temperatures. Magnetic order is suppressed with increasing Cu content and our data indicate a possible quantum critical point at x(cr) approximate to 1.15. For compounds with higher Cu content, non-Fermi-liquid behavior is observed. Non-Fermi-liquid scaling is inferred from electrical resistivity results for the x = 1.25 and 1.5 compounds. For compounds with even higher Cu content, a sharp kink occurs in the resistivity data at low temperatures, and this may be indicative of another quantum critical point that occurs at higher Cu compositions. For the magnetically ordered compounds, hydrostatic pressure is found to increase the Neel temperature, which can be understood in terms of the Kondo physics. For the non-magnetic compounds, application of a magnetic field promotes a tendency toward Fermi-liquid behavior. Thermoelectric power was analyzed using a two-band Lorentzian model, and the results indicate one fairly narrow band (10 meV and below) and a second broad band (around hundred meV). The results imply that there are two relevant energy scales that need to be considered for the physics in this family of compounds. (C) 2011 Elsevier B.V. All rights reserved.

Two-photon absorption properties of a novel class of triarylamine compounds

Two-photon absorption spectra of a triarylamine compounds dissolved in toluene were measured using the well-known Z-scan technique, employing 120-fs laser pulse-width. According to the results, an extra band located at around 900 nm was observed only for triarylamine with azoaromatic units. On the other hand, a shift in the two-photon absorption band for triarylamine, with and without azoaromatic units, is observed when different electron donor/acceptors groups are changed. The fitting of the spectra, using sum-over-states model, allowed us to obtain the spectroscopic parameters of each molecule, which appears to be in reasonable agreement with molecules presenting similar structural moieties. (C) 2010 Elsevier B.V. All rights reserved.

Pharmacophore Model of Cruzain Inhibitors

Chagas disease, caused by the protozoan Trypanosoma cruzi, is one of the most serious amongst the so-called neglected diseases in Latin America, specially in Brazil. So far there has been no effective treatment for the chronic phase of this disease. Cruzain is a major cysteine protease of T cruzi and it is recognized as a valid target for Chagas disease chemotherapy. The mechanism of cruzain action is associated with the nucleophilic attack of an activated sulfur atom towards electrophilic groups. In this report, features of a putative pharmacophore model of the enzyme, developed as a virtual screening tool for the selection of potential cruzain inhibitors, are described. The final proposed model was applied to the ZINC v.7 database and afterwards experimentally validated by an enzymatic inhibition assay. One of the compounds selected by the model showed cruzain inhibition in the low micromolar range.

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

Reaction of a model siderophore with Ni(II), Co(II) and Zn(II) in aqueous solution: Kinetics and spectroscopy

A spectroscopic study was performed showing that the [Fe(III)(L(2-))(2)](1-) (L(2-) = dopacatecholate) complex reacts with Ni(II), Co(II) and Zn(II) in an aqueous solution containing S(2)O(3)(2-) resulting in the soluble [M(L(1-))(3)](1-) (L(1-) = dopasemiquinone; M = Ni(II), Co(II) or Zn(II) complex species. The Raman and IR spectra of the [CTA][M(L(1-))(3)] complexes, CTA hexadecyltrimethylammonium cation, in the solid state were obtained. The kinetic constants for the metal substitution reactions were determined at four different temperatures, providing values for Delta W(not equal) Delta S(not equal) and Delta G(not equal). The reactions were slow (k = 10(-1)1 M s(-1)) and endothermic. The system investigated can be considered as a simplified model to explain some aspects of siderophore chemistry. (c) 2007 Elsevier Inc. All rights reserved.

Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model

There is a need of scientific evidence of claimed nutraceutical effects, but also there is a social movement towards the use of natural products and among them algae are seen as rich resources. Within this scenario, the development of methodology for rapid and reliable assessment of markers of efficiency and security of these extracts is necessary. The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. Cystoseira is a brown alga containing fucoxanthin and other carothenes whose pressure-assisted extracts were assayed to discover a possible beneficial effect on complications related to diabetes evolution in an acute but short-term model. Urine was selected as the sample and CE-TOF-MS as the analytical technique to obtain the fingerprints in a non-target metabolomic approach. Multivariate data analysis revealed a good clustering of the groups and permitted the putative assignment of compounds statistically significant in the classification. Interestingly a group of compounds associated to lysine glycation and cleavage from proteins was found to be increased in diabetic animals receiving vehicle as compared to control animals receiving vehicle (N6, N6, N6-trimethyl-L-lysine, N-methylnicotinamide, galactosylhydroxylysine, L-carnitine, N6-acetyl-N6-hydroxylysine, fructose-lysine, pipecolic acid, urocanic acid, amino-isobutanoate, formylisoglutamine. Fructoselysine significantly decreased after the treatment changing from a 24% increase to a 19% decrease. CE-MS fingerprinting of urine has provided a group of compounds different to those detected with other techniques and therefore proves the necessity of a cross-platform analysis to obtain a broad view of biological samples.

A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.