Silver nanoparticles are receiving increasing attention in the field of agriculture. This study aims at evaluating the antifungal properties of green synthesised silver nanoparticles (AgNPs) from Aloe vera leaf extract against two pathogenic fungus Rhizopus sp. and Aspergillus sp. Results revealed that synthesised nanoparticles showed strong absorption maximum at 400 nm corresponding to the surface plasmon resonance. The prepared nanoparticles were characterized by SEM, FT-IR and UVVis spectroscopy. From the scanning photograph it is clear that particles are heterogeneous in shape such as rectangular, triangular and spherical with uniform distribution. FT-IR study showed sharp absorption peaks at 1,631 and 3,433 cm -1 for amide and alcoholic hydroxide groups, respectively. On the other hand, synthesised silver nanoparticles showed highest antifungal activity against Aspergillus sp. than Rhizopus sp. by application of 100 lL of 1 M silver nanoparticles with maximum inhibition of the growth of fungal hyphae. However, microscopic observation revealed that synthesised nanoparticles caused detrimental effects on conidial germination along with other deformations such as structure of cell membrane and inhibited normal budding process of both the tested species. Therefore, it has been concluded that Aloe vera leaf extract origin silver nanoparticles have tremendous potentiality towards controlling pathogenic fungus. However, further research is needed to check the efficacy of sizedependent AgNPs on different species of fungus.
HighlightsIsolation of two rod-shaped Gram-positive bacteria.Isolates tolerate up to 4500 ppm and 550 ppm concentration of arsenate and arsenite.Bacteria mediated arsenic bioremediation.
In the present work, the possibility of using a non-conventional finely ground (250 lm) Azadirachta indica (neem) bark powder [AiBP] has been tested as a low-cost biosorbent for the removal of arsenic(III) from water. The removal of As(III) was studied by performing a series of biosorption experiments (batch and column). The biosorption behavior of As(III) for batch and column operations were examined in the concentration ranges of 50-500 lg L-1 and 500.0-2000.0 lg L-1 , respectively. Under optimized batch conditions, the AiBP could remove up to 89.96 % of As(III) in water system. The artificial neural network (ANN) model was developed from batch experimental data sets which provided reasonable predictive performance (R 2 = 0.961; 0.954) of As(III) biosorption. In batch operation, the initial As(III) concentration had the most significant impact on the biosorption process. For column operation, central composite design (CCD) was applied to investigate the influence on the breakthrough time for optimization of As(III) biosorption process and evaluation of interacting effects of different operating variables. The optimized result of CCD revealed that the AiBP was an effective and economically feasible biosorbent with maximum breakthrough time of 653.9 min, when the independent variables were retained at 2.0 g AiBP dose, 2000.0 lg L-1 initial As(III) concentrations, and 3.0 mL min-1 flow rate, at maximum desirability value of 0.969. Keywords Arsenic(III) removal Á Low-cost adsorbent Á Biosorption experiments Á Artificial neural network model Á Central composite design
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