ArticleEnvironmental science and pollution research international2020
Use of adsorption-influencing parameters for designing the batch adsorber and neural network-based prediction modelling for the aqueous arsenate removal using combustion synthesised nano-alumina.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 18 citations in OpenAlex.
- Efficient loading of nano Mn particles on calcined laterite soil (Lt-nMn) for higher removal of As(III) ions from groundwater: adsorption and eco-scale analysis.Environmental science and pollution research international · 2022Article
- A comprehensive review on the decontamination of lead(ii) from water and wastewater by low-cost biosorbents.RSC advances · 2022Review
- Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems.Computational intelligence and neuroscience · 2022Article
- An experimental approach for the utilization of tannery sludge-derived Bacillus strain for biosorptive removal of Cr(VI)-contaminated wastewater.Environmental science and pollution research international · 2021Article
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2 authors at 1 institution in 1 country.
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Abstract
Removal of arsenic from water is of utmost priorities on a global scenario due to its ill effects. Therefore, in the present study, aluminium oxide nano-particles (nano-alumina) were synthesised via solution combustion method, which is self-propagating and eco-friendly in nature. Synthesised nano-alumina was further employed for arsenate removal from water. Usually, pre-oxidation of arsenite is performed for better removal of arsenic in its pentavalent form. Thus, arsenate removal as a function of influencing parameters such as initial concentration, dose, pH, temperature, and competing anions was the prime objective of the present study. The speciation analysis showed that H
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