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-002249nas a2200241   4500008004100000022001400041245010900055210006900164260009900233300001400332490000700346520131600353653001801669653002401687653003301711653002101744100002401765700002801789700002201817700002101839700002101860856012601881       2008                        eng d  a0960-852400aUse of response surface optimization for the production of biosurfactant from rhodococcus spp. MTCC 25740 aUse of response surface optimization for the production of biosu  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcNOV  a7875-78800 v993 a<p>The production of biosurfactant from Rhodococcus spp. MTCC 2574 was effectively enhanced by response surface methodology (RSM). Rhodococcus spp. MTCC 2574 was selected through screening of seven different Rhodococcus strains. The preliminary screening experiments (one-factor at a time) suggested that carbon source: mannitol, nitrogen source: yeast extract and meat peptone and inducer: n-hexadecane are the critical medium components. The concentrations of these four media components were optimized by using central composite rotatable design (CCRD) of RSM. The adequately high R-2 value (0.947) and F score 19.11 indicated the statistical significance of the model. The optimum medium composition for biosurfactant production was found to contain mannitol (1.6 g/L), yeast extract (6.92 g/L), meat peptone (19.65 g/L), n-hexadecane (63.8 g/L). The crude biosurfactant was obtained from methyl tert-butyl ether extraction. The yield of biosurfactant before and after optimization was 3.2 g/L of and 10.9 g/L, respectively. Thus, RSM has increased the yield of biosurfactant to 3.4-fold. The crude biosurfactant decreased the surface tension of water from 72 mN/m to 30.8 mN/m (at 120 mg L-1) and achieved a critical, micelle concentration (CMC) value of 120 mg L-1. (C) 2008 Published by Elsevier Ltd.</p>10aBiosurfactant10aMedium optimization10aResponse surface methodology10aRhodococcus spp.1 aMutalik, Snehal, R.1 aVaidya, Bhalchandra, K.1 aJoshi, Renuka, M.1 aDesai, Kiran, M.1 aNene, Sanjay, N.  uhttps://library.ncl.res.in/content/use-response-surface-optimization-production-biosurfactant-rhodococcus-spp-mtcc-2574-001976nas a2200253   4500008004100000022001400041245013800055210006900193260008000262300001200342490000700354520098200361653001201343653002401355653003001379653003301409653002901442100002801471700002401499700002201523700002101545700002601566856013001592       2009                        eng d  a1367-543500aEnhanced production of amidase from Rhodococcus erythropolis MTCC 1526 by medium optimisation using a statistical experimental design0 aEnhanced production of amidase from Rhodococcus erythropolis MTC  aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY  a671-6780 v363 a<p>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.</p>10aAmidase10aMedium optimisation10aPlackett-Burman screening10aResponse surface methodology10aRhodococcus erythropolis1 aVaidya, Bhalchandra, K.1 aMutalik, Snehal, R.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/enhanced-production-amidase-rhodococcus-erythropolis-mtcc-1526-medium-optimisation-using-002362nas a2200277   4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013101953       2009                        eng d  a1367-543500aMedia optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach0 aMedia optimization for biosurfactant production by rhodococcus e  aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY  a747-7560 v363 a<p>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 (% EI24)] 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 similar to 3 and similar to 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 similar to 0.99 and similar to 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.</p>10aArtificial neural network10aBiosurfactant10aGenetic algorithm10aMedia optimization10aResponse surface methodology10aRhodococcus1 aPal, Moumita, P.1 aVaidya, Bhalchandra, K.1 aDesai, Kiran, M.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-0