Nanorod carbon nitride as a carbo catalyst for selective oxidation of hydrogen sulfide to sulfur.

Two-dimensional mesoporous carbon nitride and its highly efficient nanorod framework were prepared via hard-templating method. The obtained materials were fully characterized. The results showed that the samples structural ordering and morphology were similar to those of the parent silica templates; they had large pore volumes as well as high surface area structures. Carbon nitride carbocatalysts were used for H2S selective oxidation. The catalytic tests were carried out at 190, 210 and 230 °C in a fixed bed reactor. The obtained selectivity values for mesoporous carbon nitride rod and mesoporous carbon nitride toward elemental sulfur at 190 °C were 88.8% and 83%, respectively. Both samples were highly active due to their alkaline surface, appropriate pore size distribution and structure. In comparison with other carbon-based materials used for this process, mesoporous carbon nitride rod is the first carbonaceous material reported so far that could yield high levels of both conversion and selectivity at 190 °C. This superiority could be caused by the narrow pore size distribution (<3 nm), incorporation of nitrogen into the carbon matrix and its appropriate morphology. Nitrogen species act as electron donors in H2S oxidation. Rod-like particles might have acted as nanoreactors that facilitated the reaction progress.

Facile synthesis of a SnO2@rGO nanohybrid and optimization of its methane-sensing parameters.

Stannic oxide nanoparticles and various compositions of SnO2@rGO (reduced graphene oxide) nanohybrids were synthesized by a facile hydrothermal method and utilized as chemiresistive methane gas sensors. To characterize the synthesized nanohybrids, BET (Brunauer-Emmett-Teller), XRD, FESEM, TEM, FTIR, and Raman techniques were used. Sensing elements were tested using a U-tube flow chamber with temperature control. To obtain the best sensor performance, i.e., the highest signal and the fastest response and recovery times, the sensing element composition, operating temperature, and gas flow rate were optimized. The highest response (change in resistance) of 47.6% for 1000 ± 5ppm methane was obtained with the SnO2@rGO1% nanohybrid at 150°C and a flow rate of 160sccm; the response and recovery times were 61s and 5min, respectively. A sensing mechanism was suggested, based on the experiments.

Chemometric study of the aggregation of alcohol dehydrogenase and its suppression by beta-caseins: a mechanistic perspective.

Molecular chaperones interact preferentially with certain aggregation-prone intermediates of target protein molecules. An estimation of the chaperone activity based on suppression of aggregation is required to be mechanistically understood. In this study, the multivariate curve resolution chemometric technique was applied on horse alcohol dehydrogenase (ADH) UV-spectra under thermal stress, to obtain the required information about the number and change in concentrations of the species involved. Chemometric analysis of UV-absorption spectra of horse ADH under thermal stress, led to the existence of three different molecular species including native (N), aggregation-prone intermediate (I) and final aggregate (A) species. Appearance and buildup of two molecular species I and A were connected to the disappearance of N-species. In the presence of beta-caseins (BCN), however, a new complex between I and BCN (I-BCN) was formed. Meanwhile, by accretion of concentration of I-BCN complex, the light scattering intensity diminished. The data presented in this study clearly demonstrate that the interaction of BCN as a chaperone molecule with I-species takes place in a temperature-dependent manner and leads to a reversible I-BCN complex. In the absence of chaperones, I-state is subsequently converted to the final aggregate species. In the presence of BCN, this molecular species could be converted to the final aggregate state and/or form the I-BCN complex.

Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis.

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offord's charge over mass term (Q/M(2/3)), corrected steric substituent constant (E(s,c)) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report - up to 42 amino acid residues as compared to 13 amino acids in the initial study - that also included highly charged and hydrophobic peptides. The entire data set was obtained from the published result by Janini and co-workers. The results of this model are compared with those obtained using multiple linear regressions (MLR) model developed in this work and the multi-variable model released by Janini et al. Better predictive ability of the BP-ANN model over the MLR indicates the non-linear characteristics of the electrophoretic mobility of peptides. The present model exhibits better robustness than the MLR models in predicting CZE mobilities of a diverse data set at different experimental conditions. To explore the utility of the ANN model in simulation of the CZE peptide maps, the profiles for the endoproteinase digests of melittin, glucagon and horse cytochrome C is studied in the present work.

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the Offord model and artificial neural networks.

The aim of this work was to explore the usefulness of empirical models and multivariate analysis techniques in predicting electrophoretic mobilities of small peptides in capillary zone electrophoresis (CZE). The data set consists of electrophoretic mobilities, measured at pH 2.5, for 125 peptides ranging in size between 2 and 14 amino acids. Among the existing empirical models, the Offord model (i.e., mu identical with Q/M(2/3)) gave the best correlation for the data set. A quantitative structure-mobility relationship (QSMR) was developed using the Offord's charge-over-mass term (Q/M(2/3)) as one descriptor combined with the corrected steric substituent constant (E(s, c)) and molar refractivity (MR) descriptors to account for the steric effects and bulkiness of the amino acid side chains. The multilinear regression (MLR) of the data set showed an improvement in the predictive ability of the model over the simple Offord's relationship. A 3-4-1 back propagation artificial neural networks (BP-ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. The improved correlations by the BP-ANN analysis suggest the existence of nonlinear characteristic in the mobility-charge relationships.

Draize rabbit eye test compatibility with eye irritation thresholds in humans: a quantitative structure-activity relationship analysis.

Draize rabbit eye test scores, as modified maximum average score (MMAS), for 68 pure bulk liquids were adjusted by the liquid-saturated vapor pressure P. These 68 adjusted scores, as log (MMAS/P), were shown to be completely equivalent to eye irritation thresholds (EIT), expressed as log (1/EIT), for 23 compounds in humans. Thus, for the first time the Draize eye test in rabbits for pure bulk liquids is shown to be perfectly compatible with eye irritation thresholds in humans. The total data set for 91 compounds was analyzed by the general solvation equation of Abraham. Values of log (MMAS/P) or log (1/EIT) could be fitted to a five-parameter equation with R2 = 0.936, SD = 0.433, AD = 0.000, and AAD = 0.340 over a range of 9.6 log units. When divided into a training set of 45 compounds, the corresponding equation could be used to predict the remaining 46 compounds in a test set with AD = -0.037 and AAD = 0.345 log units. Thus, the 91-compound equation can now be used to predict further EIT values to around 0.4 log units. It is suggested that the mechanism of action in the Draize test and in the human EIT involves passive transfer of the compound to a biophase that is quite polar, is a strong hydrogen bond base, a moderate hydrogen bond acid, and quite hydrophobic. The biophase does not resemble water or plasma, but resembles an organic solvent such as N-methylformamide.