Bioprocess Scale-up for Acetohydroxamic Acid Production by Hyperactive Acyltransferase of Immobilized Rhodococcus Pyridinivorans

Catalysis Letters - In this study, Rhodococcus pyridinivorans cells containing hyperactive acyltransferase was immobilized on various macromolecules based-polymeric matrices and used to improve...     

Effects of Microbial Polysaccharides on the Oil Absorption and Quality Characteristics of a Deep-Fried Snack Namkeen

This study investigates the potential of microbial polysaccharides (MP) gellan gum and pullulan to reduce oil uptake in a deep-fried snack (Namkeen). The concentration of MP in refined wheat flour varied between 1–10% (w/w) and the frying time and temperature are optimized. The results reveal that MP added namkeens showed a maximum relative oil reduction of 33.87% with gellan gum and 26.93% with pullulan at 10% (w/w). Gellan gum and pullulan result in oil reduction of 23.19% and 15.97% with 5% (w/w). The results reveal that gellan gum added namkeens exhibited 1.45 times lower oil uptake than pullulan added namkeen indicating potential oil reduction capabilities. The hardness of namkeen is observed to increase with an increase in MP concentration. Sensorial and textural characteristics of control and MP incorporated fried samples are comparable up to 5% (w/w) with MP concentration. The results for the control samples and MP incorporated samples are statistically significant. Furthermore, overall sensorial acceptability of pullulan added namkeens (7.74 ± 0.53) is slightly higher than that of gellan gum added namkeens (7.64 ± 0.36). Practical Application: Low-fat diet and functional foods are expected to help in managing the conditions of hyperlipidaemia. This study focuses on the potential of MP in reducing the oil content of deep-fried snacks. The efficacy of MP gellan gum and pullulan as functional ingredients for oil reduction in fried snacks demonstrates its aid in formulating low-fat foods thereby retaining sensorial characteristics.     

Experimental Investigation on the Performance of a Solar Still Using SiO2 Nanoparticles /Jatropha curcas L

Silicon - Now, enticing systematic civic since everywhere the world is used in green synthesis and benefit of the simple is eco-friendly with an emergent method of producing nanoparticles (NPs)....     

Scale up criteria for dual stirred gas-liquid unbaffled tank with concave blade impeller

Experimental investigation has been done in unbaffled gas-liquid stirred tanks using dual concave blade impeller to analyze the mass transfer, power consumption and gas holdup. Optimal impeller clearance has been suggested for lower and upper impeller based on maximum mass transfer rate. Numerical modeling has been done to analyze the flow pattern for different combinations of impeller clearance. The lower impeller positioned at 0.3 of tank diameter and clearance between lower and upper impeller at 0.4 of tank diameter gave the maximum mass transfer coefficient. Scale-up criteria for mass transfer rate, power and gas holdup have been developed for optimal geometrical similar systems of unbaffled stirred tanks with dual concave impeller.     

A kinetic model for the enzyme-catalyzed self-epoxidation of oleic acid

This paper reports a kinetic model for the selfepoxidation of oleic acid with toluene as solvent and Novozym 435 (a commercially available preparation of immobilized Candida antarctica lipase) as catalyst at 30°C. The effects of various parameters on the conversion and rates of reaction were studied. Both the initial rate and the progress curve data were used to fit an ordered bi-bi model. At low temperatures, the rate of epoxidation was faster than the rate of deactivation of the enzyme by hydrogen peroxide.     

Structural characteriztion of Nd-doped in silica host matrix prepared by net chemical process

Silica host matrix containing neodymium which is potentially important for the formation of nanocrystalline metal oxides was prepared by solgel method, using tetra-ethoxysilane and Nd(NO3)3 as precursor materials. The prepared samples were changed from amorphous to nanocrystallites phase at sintered temperature 550℃ (4 h), 750℃ (8 h) and 950 ℃ (12 h). The thermally treated sample microstructures were investigated using X-ray diffraction (XRD), Fourier transformation infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). While a further increase of the temperature at 750 ℃ and annealing time (8 h) resulted in the formation of cubic and hexagonal Nd2O3 nanocrystallites. At constant sintering temperature 950℃ for 12 h, the samples showed sharper and intense peaks. The sizes of Nd2O3 nanocrystallites were characterized by XRD with average size～46 nm.     

Solution properties of methyl methacrylate–acrylonitrile copolymers

Characterization of methyl methacrylate-acrylonitrile copolymers is done through viscosity, swelling, and differential refractometric studies. Viscosities of the copolymers and homopolymers were determined at 30, 40, and 50°C. The activation parameters of viscous flow, voluminosity, and shape factor were also calculated. The average shape factor was observed to be 2.5 ± 0.005 for all copolymer systems. Viscosity molecular weights were calculated, and from intramolecular expansion factor (α), it was observed that copolymers are less flexible than are homopolymers. dn/dc values obtained from differential refractometry are in good agreement with those calculated theoretically. © 1993 John Wiley & Sons, Inc.     

An ensemble-based incremental learning approach to data fusion.

This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data.