ArticleEnvironmental science and pollution research international2021
Linearized and nonlinearized modellings for comparative uptake assessment of metal-organic framework-derived nanocomposite towards sulfonamide antibiotics.
Article in Environmental science and pollution research international, 2021. 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, 16 citations in OpenAlex.
- Synthesis and Characterization of Hybrid Bio-Adsorbents for the Biosorption of Chromium Ions from Aqueous Solutions.Polymers · 2025Article
- Adsorptive Optimization of Crystal Violet Dye Using Central Composite Rotatable Design and Response Surface Methodology: Statistical Analysis, Kinetic and Isotherm Studies.Arabian journal for science and engineering · 2022Article
- Facile Fabrication of Novel NiFeMaterials (Basel, Switzerland) · 2021Article
- Removal of aqueous Cr(VI) by magnetic biochar derived from bagasse.Scientific reports · 2020Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
The emergent occurrence of sulfonamide species involving sulfadiazine (SDZ) and sulfamethazine (SMZ) in aquatic systems can cause a wide range of potential risks; hence, remediation strategies need to be necessary. Here, we develop the novel metal-organic framework-derived nanocomposite, and apply for the adsorption of SDZ and SMZ antibiotics. To assess the best-fitting kinetic (pseudo first-order, pseudo second-order) and isotherm (Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Redlich-Peterson, Sips, Toth, and Khan) models, a series of numerous statistical analysis was performed. Numerous error functions including squares of the errors (SSE), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), and mean relative error (MRE) were also analyzed to assess the linear and nonlinear models. The results indicated that both linear and nonlinear kinetic models were mostly fitted well with pseudo second-order models (R
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