Biological selenite removal and recovery of selenium nanoparticles by haloalkaliphilic bacteria isolated from the Nakdong River

Won, Sangmin; Ha, Myung-Gyu; Nguyễn, Văn Khánh; Kang, Ho Young

doi:10.1016/j.envpol.2021.117001

Cited by 11 publications

(10 citation statements)

References 41 publications

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“…DCX [20], selenium nanoparticles with an average size of 120 nm were generated under an argon atmosphere. In addition, in earlier studies carried out using haloalkaliphilic strain B. Lysinibacillus, selenium nanoparticles in the size range of 200-500 nm were synthesised [22]. Thus, our results on particle size were consistent with the biological production of selenium nanoparticles using different strains of bacteria.…”

Section: Particle Size Zeta Potential Measurements and Tem Analysissupporting

confidence: 86%

“…The growth delay of strain LAB-Se2 at pH 4.0 exhibited that the clearance rate of Se (IV) could be compensated by prolonging the growth time. In a previously reported study on the effect of selenium on the growth of strain NDSe-7, we found that there was also a growth delay at pH 10.0 [22]. The rapid growth of bacteria within 24 h was accompanied by a rapid decrease in the amount of Se (IV) (Figure 2c).…”

Section: Selenite Reduction and Se Nanoparticle Production By Strain ...mentioning

confidence: 54%

“…Figure 8c,d shows the TEM images of the purified SeNPs at a resolution of 1 µm and 500 nm, respectively, where spherical Se nanoparticles could be seen. These particles varied in size (about 50-400 nm in diameter), similar to the size of nano-selenium produced by Lysinibacillus bacilli (50-400 nm in diameter) [22].…”

Section: Particle Size Zeta Potential Measurements and Tem Analysismentioning

confidence: 65%

“…There is a large number of published studies describing the role of selenium-rich microorganisms, such as Bacillus subtillus, Providencia sp. DCX, Bacillus amyloliquefaciens, Proteus mirabilis, Lysinibacillus fusiformis, and Lactobacillus casei [5,8,[20][21][22]. At present, the research on selenium enrichment is very popular, and the elemental form of selenium is used as an additive in many foods, but there are few reports on enrichment of lactic acid bacteria with selenium.…”

Section: Bacterial Isolationmentioning

confidence: 99%

“…Strain LAB-Se2 was able to grow at a salinity up to 6.5% and still perform Se (IV) reduction. Sangmin et al reported that the growth of strain NDSe-7 was not inhibited under the addition of selenium at 7% salinity [22]. Based on its salt tolerance, strain LAB-Se2 was considered to be effective for the treatment of selenium-containing wastewater environments [28].…”

Section: Selenite Reduction and Se Nanoparticle Production By Strain ...mentioning

confidence: 99%

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Biological Selenite Reduction, Characterization and Bioactivities of Selenium Nanoparticles Biosynthesised by Pediococcus acidilactici DSM20284

Wang

Kuang

et al. 2023

Molecules

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Selenium (Se) is in great demand as a health supplement due to its superior reactivity and excellent bioavailability, despite selenium nanoparticles (SeNPs) having signs of minor toxicity. At present, the efficiency of preparing SeNPs using lactic acid bacteria is unsatisfactory. Therefore, a probiotic bacterial strain that is highly efficient at converting selenite to elemental selenium is needed. In our work, four selenite-reducing bacteria were isolated from soil samples. Strain LAB-Se2, identified as Pediococcus acidilactici DSM20284, had a reduction rate of up to 98% at ambient temperature. This strain could reduce 100 mg L−1 of selenite to elemental Se within 48 h at pH 4.5–6.0, a temperature of 30–40 °C, and a salinity of 1.0–6.5%. The produced SeNPs were purified, freeze-dried, and subsequently systematically characterised using FTIR, DSL, SEM-EDS, and TEM techniques. SEM-EDS analysis proved the presence of selenium as the foremost constituent of SeNPs. The strain was able to form spherical SeNPs, as determined by TEM. In addition, DLS analysis confirmed that SeNPs were negatively charged (−26.9 mV) with an average particle size of 239.6 nm. FTIR analysis of the SeNPs indicated proteins and polysaccharides as capping agents on the SeNPs. The SeNPs synthesised by P. acidilactici showed remarkable antibacterial activity against E. coli, B. subtilis, S. aureus, and K. pneumoniae with inhibition zones of 17.5 mm, 13.4 mm, 27.9 mm, and 16.2 mm, respectively; they also showed varied MIC values in the range of 15–120 μg mL−1. The DPPH, ABTS, and hydroxyl, and superoxide scavenging activities of the SeNPs were 70.3%, 72.8%, 95.2%, and 85.7%, respectively. The SeNPs synthesised by the probiotic Lactococcus lactis have the potential for safe use in biomedical and nutritional applications.

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Section: Particle Size Zeta Potential Measurements and Tem Analysissupporting

confidence: 86%

Section: Selenite Reduction and Se Nanoparticle Production By Strain ...mentioning

confidence: 54%

Section: Particle Size Zeta Potential Measurements and Tem Analysismentioning

confidence: 65%

Section: Bacterial Isolationmentioning

confidence: 99%

Section: Selenite Reduction and Se Nanoparticle Production By Strain ...mentioning

confidence: 99%

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Biological Selenite Reduction, Characterization and Bioactivities of Selenium Nanoparticles Biosynthesised by Pediococcus acidilactici DSM20284

Wang

Kuang

et al. 2023

Molecules

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Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Sinharoy,

Mahanty,

Behera

et al. 2023

J of Chemical Tech & Biotech

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BACKGROUNDA genetic algorithm (GA) optimized artificial neural network (ANN) model was developed to simulate selenite removal efficiency (SRe) and chemical oxygen demand (COD) removal efficiency (CRe) in a single‐stage inverse fluidized bed reactor (IFBR) operated under different hydraulic retention time (HRT) (12–48 h), and inlet selenite concentration (12.7–635 mg L−1). Multi‐objective optimization for SRe and CRe with the ANN model was adopted to estimate the pareto optimal solutions in the bioreactor system.RESULTSGA optimized ANN topology suggested five neurons in the hidden layer, and the optimal data partitioning between training, test, and validation subsets (in 70:15:15 ratio) using cross‐validation. The average fitness for SRe and CRe of the resulting ANN model over training (R2 = 0.885), test (R2 = 0.967) and validation (R2 = 0.972) subsets were reasonably good. Sensitivity analysis suggested an increase in SRe and CRe with HRT, while an increase in inlet selenite concentration had a negative effect on SRe. Local interpretable model‐agnostic explanations and SHapley Additive exPlanations for the developed model suggested a positive coefficient of the predictors on CRe. Pareto optimal solution for SRe (92.87%–95.26%), and CRe (91.33%–99.35%) were suggested for HRT and inlet selenite concentration varying between 35.67 h and 36 h, and 0.1 g L−1 and 0.884 g L−1, respectively.CONCLUSIONSThe GA optimized ANN model could be exploited for the prediction of contaminant removal efficiencies in the bioreactor system with a fair degree of complexity (i.e., IFBR). Multiple objective pareto optimal solutions can identify the best operating conditions relevant for wastewater treatment. © 2023 Society of Chemical Industry (SCI).

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A Nano-Based Approach Toward the Sustainable Recovery of Nutrients from the Food Industry Generated Wastewater

Ravikumar,

Barbhuiya,

Wroblewski

et al. 2024

Green Energy and Technology

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Biological selenite removal and recovery of selenium nanoparticles by haloalkaliphilic bacteria isolated from the Nakdong River

Cited by 11 publications

References 41 publications

Biological Selenite Reduction, Characterization and Bioactivities of Selenium Nanoparticles Biosynthesised by Pediococcus acidilactici DSM20284

Biological Selenite Reduction, Characterization and Bioactivities of Selenium Nanoparticles Biosynthesised by Pediococcus acidilactici DSM20284

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

A Nano-Based Approach Toward the Sustainable Recovery of Nutrients from the Food Industry Generated Wastewater

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