This study investigated the bioethanol production from green algae Chlorella vulgaris depending on its carbohydrate-enriched biomass. Four different phosphorous concentrations were employed to stimulate bioethanol production from Chlorella vulgaris. The impact of various phosphorous values on Chlorella vulgaris growth rate as well as primary product (carbohydrate) were evaluated. High performance liquid chromatography was utilized in this work. The stationary phase was identified as day 14, 12, 10 and 6 in treatments 6, 4, 2 and g/L, respectively. The findings suggest that the treatment without phosphorous addition had the highest record of carbohydrate content (22.64% dry weight) as well as the highest bioethanol yield (20.66% dry weight). It was also found that at 0 g/L treatment, the growth rate was the highest with 0.75 (day-1) while the lowest was recorded at 0.42 with at 6 g/L. Finally, with the treatment of 0 g/L, the shortest doubling time was obtained with 1.35 days, while the highest one was observed with 2.4 days at 6 g/L treatment.
The objective of this study was to progress another method for coagulation/flocculation of the microalga Chlorella vulgaris via pellet-forming of the fungal species Aspergillus niger which was isolated from municipal wastewater mud and the facultative heterotrophic microalga "C.vulgaris was used. The main factors studies were spore inoculums, organic carbon concentration in medium as well as pH variation which had considerably positive effects on microalgae/fungi co-pelletization formation. The process parameters are an inoculum1×104 spores/ML, 15 g/l sucrose as carbon source and pH ranged from 5 - 7.0 were found optimal for efficient microalgae/fungi co-pelletization formation. For autotrophic growth, when pH of culture broth was adjusted
... Show MoreApplications of microalgae in environmental studies have recently increased. Current uses of immobilized microalga Chlorella vulgaris include reducing pharmaceutical substances such as amoxicillin AMX and potassium dichromate K2Cr2O7 on freshwater clam Pseudodontopsis euphraticus as a biotic model. Recent research pointed out a change in biomarkers of oxidative stress in an evaluation of induced toxicity. Where clams were exposed to different concentrations100, 200, and 400 mg/L for 7 days and 20, 30, and 50 mg/L for 5 days of amoxicillin and potassium dichromate, respectively. The results showed that exposure to AMX and K2Cr2O7 led to a signific
... Show MoreToday technology using nanoparticle when treatment pathogentic microorganism and we focused on this here. It was found that the species of streptococcus used in present study were sensitive to erythromycin. In present study focusing biofilm formation by Streptococcus spp was evaluated. Species S. mutans was found that highest amount of biofilm compare with the other species. The aim of report effect (SNPs) on ability of biofilm form different species of streptococcus. The anti-biofilm effect of SNPs was in concentration dependent manner. The highest effect of SNP against biofilm formation was found the concentration 160 μg/ml, while the lowest effect was found the lowest used concentration (80 μg/ml) of SNPs. In vivo study revealed that s
... Show MoreTwo series of 1,3,4-oxadiazole derivatives at the sixth position of the 2,4-di-
silver nanoparticle which synthesized by.
This study aims to remove Cd(II) ions from simulated wastewater by using Chlorophyceae algae (CA). Different parameters were studied to show their effects on the biosorption efficiency of CA. These parameters are: the effect of pH 3-7, initial metal ion concentration 20-200 mg/L, sorbent dos-age 0.05-2 g/L, contact time 5-180 min, and agitation speed 100-300 rpm. We found that both the Langmuir and Freundlich models appropriate for characterizing the metal removal process. The biosorption data fit best with the results of the pseudo-second-order kinetic model, demonstrating that the chemisorption process is the dominant mechanism controlling the removal. CA was char-acterized using the scanning electron microscopy test, prior to and post bi
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