02244nas a2200265 4500008004100000022001400041245013100055210006900186260005700255300001200312490000600324520126300330653002701593653001201620653001801632653002501650653002201675100002901697700002401726700002801750700002201778700002001800700002601820856013201846 2008 eng d a1931-757300aSulfur nanoparticles synthesis and characterization from H2S gas, using novel biodegradable iron chelates in w/o microemulsion0 aSulfur nanoparticles synthesis and characterization from H2S gas a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcJUN a221-2290 v33 a

Sulfur nanoparticles were synthesized from hazardous H2S gas using novel biodegradable iron chelates in w/o microemulsion system. Fe3+-malic acid chelate (0.05 M aqueous solution) was studied in w/o microemulsion containing cyclohexane, Triton X-100 and n-hexanol as oil phase, surfactant, co-surfactant, respectively, for catalytic oxidation of H2S gas at ambient conditions of temperature, pressure, and neutral pH. The structural features of sulfur nanoparticles have been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), energy dispersive spectroscopy (EDS), diffused reflectance infra-red Fourier transform technique, and BET surface area measurements. XRD analysis indicates the presence of alpha-sulfur. TEM analysis shows that the morphology of sulfur nanoparticles synthesized in w/o microemulsion system is nearly uniform in size (average particle size 10 nm) and narrow particle size distribution (in range of 5-15 nm) as compared to that in aqueous surfactant systems. The EDS analysis indicated high purity of sulfur (> 99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur.

10aAntimicrobial activity10aH2S gas10airon chelates10asulfur nanoparticles10aw/o microemulsion1 aDeshpande, Aniruddha, S.1 aKhomane, Ramdas, B.1 aVaidya, Bhalchandra, K.1 aJoshi, Renuka, M.1 aHarle, Arti, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/sulfur-nanoparticles-synthesis-and-characterization-h2s-gas-using-novel-biodegradable-iron-001767nas a2200253 4500008004100000022001400041245011600055210006900171260007400240300001200314490000700326520086700333653001901200653000901219653002201228653003501250653001201285653000801297653000801305100002101313700002401334700002601358856012901384 2012 eng d a0928-070700aSodium-doped lithium zirconate nano squares: synthesis, characterization and applications for CO2 sequestration0 aSodiumdoped lithium zirconate nano squares synthesis characteriz aVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSbSPRINGERcFEB a316-3200 v613 a

Nano squares of sodium-doped lithium zirconate have been synthesized by a simple citrate based sol-gel method at room temperature in the presence of cetyl trimethylammonium bromide (CTAB) as a surfactant. The structural and morphological properties were investigated by X-ray diffraction (XRD) and Transmission Electron Microscopy (TEM) respectively. XRD analysis of sample calcined at 900 A degrees C for 4 h shows the formation of monoclinic lithium zirconate phase. The TEM shows the interesting morphology of formation of nano squares of sodium doped lithium zirconate sample. The carbon dioxide sorption capacity was carried out by TGA analysis at different high temperatures. It is observed that the sample shows similar to 20 wt% carbon dioxide sorption at higher temperature (650 A degrees C) within 20 min which are better than the known reports.

10aCarbon dioxide10aCTAB10aLithium zirconate10aSodium doped lithium zirconate10asol-gel10aTEM10aXRD1 aKhokhani, Mrinal1 aKhomane, Ramdas, B.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/sodium-doped-lithium-zirconate-nano-squares-synthesis-characterization-and-applications-002881nas a2200325 4500008004100000022001400041245011500055210006900170260011100239300001200350490000700362520173200369653001702101653001902118653001902137653002002156653003402176653001602210653001502226653002102241653001802262653001602280100001502296700001802311700001502329700002302344700003302367700002602400856012902426 2015 eng d a1386-207300aStudy of applications of machine learning based classification methods for virtual screening of lead molecules0 aStudy of applications of machine learning based classification m aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG a658-6720 v183 a

The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 %) and toxicophoric (70.64 %). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.

10aAnti-anginal10aanti-arrythmic10aanti-bacterial10aanti-convulsant10aanti-depressant anti-diabetic10abinary QSAR10achemophore10amachine learning10apharmacophore10atoxicophore1 aVyas, Renu1 aBapat, Sanket1 aJain, Esha1 aTambe, Sanjeev, S.1 aKarthikeyan, Muthukumarasamy1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/study-applications-machine-learning-based-classification-methods-virtual-screening-lead-0