@article { ISI:000257381600004,
	title = {Sulfur nanoparticles synthesis and characterization from H2S gas, using novel biodegradable iron chelates in w/o microemulsion},
	journal = {Nanoscale Research Letters},
	volume = {3},
	number = {6},
	year = {2008},
	month = {JUN},
	pages = {221-229},
	publisher = {SPRINGER},
	address = {233 SPRING ST, NEW YORK, NY 10013 USA},
	abstract = {<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>},
	keywords = {Antimicrobial activity, H2S gas, iron chelates, sulfur nanoparticles, w/o microemulsion},
	issn = {1931-7573},
	doi = {10.1007/s11671-008-9140-6},
	author = {Deshpande, Aniruddha S. and Khomane, Ramdas B. and Vaidya, Bhalchandra K. and Joshi, Renuka M. and Harle, Arti S. and Kulkarni, Bhaskar D.}
}
@article { ISI:000337885300011,
	title = {Biodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies},
	journal = {Chemical Engineering Research \& Design},
	volume = {92},
	number = {6},
	year = {2014},
	month = {JUN},
	pages = {1119-1132},
	publisher = {INST CHEMICAL ENGINEERS},
	type = {Article},
	address = {165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND},
	abstract = {<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>},
	keywords = {Artificial immune systems, Artificial neural networks, Batch reactor, Genetic algorithms, Sensitivity analysis},
	issn = {0263-8762},
	doi = {10.1016/j.cherd.2013.10.017},
	author = {Hamid, Aashti and Deshpande, Aniruddha S. and Badhe, Yogesh P. and Barve, Prashant P. and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.}
}
