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Cited by 249

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Covalent organic frameworks as efficient adsorbent for sulfamerazine removal from aqueous solution

Zhuang, Shuting, Liu, Yong, Wang, Jianlong·2020·Journal of Hazardous Materials

Abstract

Here, TPB (triphenylbenzene) - DMTP (dimethoxyterephthaldehyde) -COF was prepared, characterized and used as effective adsorbent for the removal of sulfamerazine (SMT) from aqueous solution. Its adsorption characteristics and mechanism were explored. With large channel (∼3.3 nm), high specific surface area (2115 m2/g), and high crystallite, TPB-DMTP-COF showed high adsorption capacity (209 mg/g), fast adsorption equilibrium (80 min), and good reusability. Natural pH condition was optimal for its adsorption capacity, while electrostatic repulsion between TPB-DMTP-COF and SMT accounted for the low adsorption performance at acidic or alkaline conditions. According to the DFT method, SMT molecules adsorbed in the pore-sites of COFs via C-H···π interaction was the predominant and stable adsorption configuration accounting for the efficient removal of SMT in large quantity. This study revealed the great adsorption potential of COFs skeleton itself in the application of environmental remediation.

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Metadata

Authors
Zhuang, S., Liu, Y., & Wang, J.
Venue
Journal of Hazardous Materials
Publisher
Elsevier BV
Published
2020-02
Volume
383
Pages
121126
ISSN
0304-3894 (ISSN-L)
OpenAlex
W2970011960
Cited by
249
References
2
FWCI
11.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Covalent organic frameworks as efficient adsorbent for sulfamerazine removal from aqueous solution. https://app.cochat.ai/papers/10.1016/j.jhazmat.2019.121126
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Zhuang, S., Liu, Y., & Wang, J. (2020). Covalent organic frameworks as efficient adsorbent for sulfamerazine removal from aqueous solution. Journal of Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2019.121126

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.