Aspergillus fumigatus

A cellulolytic fungal strain, Aspergillus fumigatus NITDGPKA3, was isolated from straw retting ground. Cellulase and xylanase production by A. fumigatus NITDGPKA3 in submerged fermentation of rice straw was studied. The culture conditions for maximum enzyme production were found to be initial pH 4, 1% substrate concentration, temperature 30°C, incubation time 5 days, 0.2% tryptone as nitrogen source, and inoculum volumes 7% v/v (for cellulase) and 5% v/v (for xylanase). Addition of Tween 80 in fermentation broth improved xylanase production (193.58 IU/ml) much more compared to cellulase production (6.53 IU/ml). Xylanase activity found in the culture broth was approximately 50% higher compared to most of the reported data. The crude enzyme was further applied for reducing sugar production from alkali pretreated rice straw, where a dosage of 40 IU/g CMCase produced 0.522 g reducing sugar/g dry substrate after 36 hours which was higher than that in the reported literature. The high concentration of reducing sugar yield was most probably due to the extraordinarily high titer of β-glucosidase (80.1 IU/ml) found in the crude enzyme. The crude enzymes secreted by Aspergillus fumigatus NITDGPKA3 efficiently hydrolyzed alkali pretreated rice straw suggesting that Aspergillus fumigatus NITDGPKA3 is a robust microorganism

Cellulase and Xylanase Production from Rice Straw by a Locally Isolated Fungus Aspergillus fumigatus NITDGPKA3 under Solid State Fermentation – Statistical Optimization by Response Surface Methodology

Alkali pretreated rice straw was used as substrate for cellulase production by a locally isolated fungus Aspergillus fumigatus NITDGPKA3 under solid state fermentation. Critical process parameters such as incubation period, temperature, basal medium content and pH were statistically optimized for an enhanced cellulase and xylanase yield by response surface methodology. The design predicted an optimum yield of 3.1 IU/g dry substrate, 64.18 IU/g dry substrate and 1040.57 IU/g dry substrate for FPase, CMCase and xylanase respectively under the optimum conditions of incubation period of 90 h, temperature at 33oC, initial basal medium content of 62% and initial pH 4. The experimental values under optimum conditions correlated well with the predicted results. Further, crude enzyme extract from Aspergillus fumigatus NITDGPKA3 was used for saccharification of pretreated rice straw and this released 189.50 mg/g of reducing sugar. This work was carried out in the Department of Biotechnology, National Institute of Technology, Durgapur-713209, West Bengal, India, during the period 2010 to 2011

Biosorptive uptake of arsenic(V) by steam activated carbon from mung bean husk: equilibrium, kinetics, thermodynamics and modeling

Abstract The present investigation emphasizes on the biosorptive removal of toxic pentavalent arsenic from water using steam activated carbon prepared from mung bean husk (SAC-MBH). Characterization of the synthesized sorbent was done using different instrumental techniques, i.e., SEM, BET and point of zero charge. Sorptive uptake of As(V) over steam activated MBH as a function of pH (3–9), agitation speed (40–200 rpm), dosage (50–1000 mg) and temperature (298–313 K) was studied by batch process at arsenic concentration of 2 mg L−1. Lower pH increases the arsenic removal over the pH range of 3–9. Among three adsorption isotherm models examined, Langmuir model was observed to show superior results over Freundlich model. The mean sorption energy (E) estimated by Dubinin–Radushkevich model suggested that the process of adsorption was chemisorption. Thermodynamic parameters confer that the sorption process was spontaneous, exothermic and feasible in nature. The pseudo-second-order rate kinetics of arsenic gave better correlation coefficients as compared to pseudo-first-order kinetics equation. Three process parameters, viz. adsorbent dosage, agitation speed and pH were opted for optimizing As(V) elimination using central composite design matrix of response surface methodology (RSM). The identical design setup was used for artificial neural network (ANN) for comparing its prediction capability with RSM towards As(V) removal. Maximum arsenic removal was observed to be 98.75% at sorbent dosage 0.75 gm L−1, pH 3.0, agitation speed 160 rpm and temperature 308 K. The study concluded that SAC-MBH could be a competent adsorbent for As(V) removal and ANN model was better in arsenic removal predictability results than RSM model