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Start date: 2020Subject: search.filters.subject.Chlorella vulgaris

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Biochemical shifts in Chlorella vulgaris via post-stationary magnesium sulfate stress: optimizing biomass for advanced bio-fertilizers
    (Frontiers Media SA, 2026-06-09) Dodangodage, C. A; Kasturiarachchi, J. C; Perera, T.A; Rajapakshe, S.D; Niyangoda, S.S; Halwatura, R.U
    Sustainable agriculture requires bio-fertilizers that improve both nutrient efficiency and soil resilience. Microalgae are promising candidates; however, conventional optimization using sodium chloride (NaCl) stress introduces phytotoxic sodium residues that limit soil application. To address this, a biphasic cultivation strategy for Chlorella vulgaris was developed using magnesium sulfate (MgSO4) as a dual-function stressor. Following the onset of a nitrogen-limited stationary phase (Day 18), the addition of 0.4 g L-¹ MgSO4 induced intracellular macromolecular accumulation. Biomass increased by 44.8% (2.810 ± 0.090 g L-¹), driven by intracellular densification, with enrichment in both total carbohydrate (42.15 ± 2.10%) and lipid (36.24 ± 1.11%) fractions. Substituting NaCl with MgSO4 eliminates the risk of sodium-induced phytotoxicity upon soil application, while simultaneously pre-loading the biomass with essential secondary macronutrients. Overall, this study demonstrates that targeted MgSO4-induced metabolic shifts can generate high-density, functionally enhanced, sodium-free microalgal biomass to serve as a potential bio-fertilizer feedstock.
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    PublicationOpen Access
    Valorization of acid-hydrolyzed tea stem waste for sustainable biodiesel production using Chlorella vulgaris: a biorefinery approach
    (Frontiers Media SA, 2026-05-18) Dodangodage, C. A; Rathnapriya R.H.N.S.; Gamage, G. N; Kasturiarachchi, Jagath C.; Perera, Thilini A.; Rajapakshe, S. D; Niyangoda, Sayuri S.; Halwatura, R.U
    The prohibitive cost of synthetic cultivation media remains a fundamental bottleneck in the commercial deployment of microalgal biodiesel. This study investigates the valorization of recalcitrant tea stem waste, an abundant agro-industrial by-product, as a low-cost, nutrient-rich medium for Chlorella vulgaris within an integrated biorefinery framework. Following thermochemical acid hydrolysis, a two-stage optimization of hydrolysate concentration and incident irradiance was conducted to maximize biomass production. Undiluted (100%) hydrolysate under elevated irradiance (240 µmol photons m-2 s-1) compensated for optical attenuation in the dark medium and yielded a maximum biomass concentration of 1.65 ± 0.07 g L-1, representing an approximately 5-fold increase over the synthetic Bold’s Basal Medium (BBM) control. Concurrently, substantial nutrient recovery was achieved, with 83.23% nitrate and 95.60% phosphate assimilation by Day 10. The resulting nutrient limitation acted as a secondary abiotic stressor, triggering enhanced intracellular lipid accumulation and yielding a peak volumetric lipid concentration of 0.094 ± 0.005 g L-1, approximately 4.5-fold higher than the autotrophic control. Fatty acid methyl ester (FAME) profiling revealed a saturated-dominant composition (85.57% SFA), corresponding to favorable predicted biodiesel properties, including low iodine value and high cetane number, consistent with international fuel standards. Overall, this study establishes tea stem hydrolysate as an efficient integrated cultivation matrix for simultaneous mixotrophic growth and lipid biosynthesis, advancing a scalable circular waste-to-energy pathway for agro-industrial systems.
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    PublicationEmbargo
    Development of an artificial neural network model to simulate the growth of microalga Chlorella vulgaris incorporating the effect of micronutrients
    (Elsevier, 2020-03-20) Liyanaarachchi, V. C; Nishshanka, G. K. S. H; Nimarshana, P. H.V; Ariyadasa, T. U; Attalage, R. A
    Artificial neural network (ANN) models can be trained to simulate the dynamic behavior of biological systems. In the present study, an ANN model was developed upon multilayer perceptron neural network architecture with 23-20-1 configuration to predict the cell concentration of microalga Chlorella vulgaris at a given time. Irradiance level, photoperiod, temperature, air flow rate, CO2 percentage of the air stream, initial cell concentration, cultivation time and the nutrient concentrations of the media were considered as the input variables of the model. Resilient backpropagation learning algorithm was used to train the model by means of 484 experimental data belonging to four studies. Bias and accuracy factors of the developed model fall into the range of 0.95–1.11 indicating the model has an excellent prediction ability. Parity plot showed a good agreement between the predicted and experimental values with R2 = 0.98. Relative importance of the inputs was evaluated using Garson’s algorithm. The results of the study indicated that CO2 supply had the highest impact on the growth of C. vulgaris within the selected range of input parameters. Among macronutrients and micronutrients, highest influence was demonstrated by nitrogen and copper respectively.