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<p>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 (&gt; 99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur.</p>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-002522nas a2200265   4500008004100000022001400041245011700055210006900172260009500241300001400336490000700350520150500357653003001862653003101892653001801923653002301941653002501964100001801989700002902007700002202036700002402058700002302082700002602105856012502131       2014                        eng d  a0263-876200aBiodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies0 aBiodegradable iron chelate for H2S abatement modeling and optimi  a165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLANDbINST CHEMICAL ENGINEERScJUN  a1119-11320 v923 a<p>A batch reactor process for the abatement of a common pollutant, namely, H2S using Fe3+-malic acid chelate (Fe3+-MA) catalyst has been developed. Further, process modeling and optimization was conducted in the three stages with a view to maximize the H2S conversion: (i) sensitivity analysis of process inputs was performed to select the most influential process operating variables and parameters, (ii) an artificial neural network (ANN)-based data-driven process model was developed using the influential process variables and parameters as model inputs, and H2S conversion (%) as the model output, and (iii) the input space of the ANN model was optimized using the artificial immune systems (AIS) formalism. The AIS is a recently proposed stochastic nonlinear search and optimization method based on the human biological immune system and has been introduced in this study for chemical process optimization. The AIS-based optimum process conditions have been compared with those obtained using the genetic algorithms (GA) formalism. The AIS-optimized process conditions leading to high (approximate to 97%) H2S conversion, were tested experimentally and the results obtained thereby show an excellent match with the AIS-maximized H2S conversion. It was also observed that the AIS required lesser number of generations and function evaluations to reach the convergence when compared with the GA. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.</p>10aArtificial immune systems10aArtificial neural networks10aBatch reactor10aGenetic algorithms10aSensitivity analysis1 aHamid, Aashti1 aDeshpande, Aniruddha, S.1 aBadhe, Yogesh, P.1 aBarve, Prashant, P.1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/biodegradable-iron-chelate-h2s-abatement-modeling-and-optimization-using-artificial-0