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 andn-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 α-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.

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This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI(24))] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were approximately 3 and approximately 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were approximately 0.99 and approximately 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.

In the present work, statistical experimental methodology was used to enhance the production of amidase from Rhodococcus erythropolis MTCC 1526. R. erythropolis MTCC 1526 was selected through screening of seven strains of Rhodococcus species. The Placket-Burman screening experiments suggested that sorbitol as carbon source, yeast extract and meat peptone as nitrogen sources, and acetamide as amidase inducer are the most influential media components. The concentrations of these four media components were optimised using a face-centred design of response surface methodology (RSM). The optimum medium composition for amidase production was found to contain sorbitol (5 g/L), yeast extract (4 g/L), meat peptone (2.5 g/L), and acetamide (12.25 mM). Amidase activities before and after optimisation were 157.85 units/g dry cells and 1,086.57 units/g dry cells, respectively. Thus, use of RSM increased production of amidase from R. erythropolis MTCC 1526 by 6.88-fold.

Sulfur nanoparticles were synthesized from hazardous H2S gas by desulfurization based on liquid redox process [1]. The use of novel biodegradable iron chelates, in particular, FeCl3-malic acid chelate system has been extensively studied in various aqueous surfactant systems of Tween 80, SDS, CTAB 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 XRD, TEM, and DLS measurements. XRD analysis indicates the presence of Metal-sulfur (JCPDS-08247). TEM analysis shows that the morphology of sulfur nanoparticles synthesized in aqueous surfactant system of Tween 80 is nearly uniform in size of 12nm average particle size, in SDS surfactant system shows 15nm average particle size, where as sulfur nanoparticles synthesized in CTAB shows average particle size of 7nm. The DLS result shows the mono-dispersity of the sulfur nanoparticles in the aqueous surfactant systems. The described process serves mainly two objectives; (a) waste utilization for preparation of commercially important nano-sulfur product and (b) reduction in environmental pollution. 1. G. Nagal, Chem. Eng. 104, 125 (1997).