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Cited by 680

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Stable Covalent Organic Frameworks for Exceptional Mercury Removal from Aqueous Solutions

Huang, Ning, Zhai, Lipeng, Xu, Hong, Jiang, Donglin·2017·Journal of the American Chemical Society

Abstract

The pre-designable porous structures found in covalent organic frameworks (COFs) render them attractive as a molecular platform for addressing environmental issues such as removal of toxic heavy metal ions from water. However, a rational structural design of COFs in this aspect has not been explored. Here we report the rational design of stable COFs for Hg(II) removal through elaborate structural design and control over skeleton, pore size, and pore walls. The resulting framework is stable under strong acid and base conditions, possesses high surface area, has large mesopores, and contains dense sulfide functional termini on the pore walls. These structural features work together in removing Hg(II) from water and achieve a benchmark system that combines capacity, efficiency, effectivity, applicability, selectivity, and reusability. These results suggest that COFs offer a powerful platform for tailor-made structural design to cope with various types of pollution.

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Crossref · 10.1021/jacs.6b12328

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Metadata

Authors
Huang, N., Zhai, L., Xu, H., & Jiang, D.
Venue
Journal of the American Chemical Society
Published
2017-02-07
Volume
139
Issue
6
Pages
2428-2434
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2580106504
Cited by
680
References
1
FWCI
28.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stable Covalent Organic Frameworks for Exceptional Mercury Removal from Aqueous Solutions. https://app.cochat.ai/papers/10.1021/jacs.6b12328
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Huang, N., Zhai, L., Xu, H., & Jiang, D. (2017). Stable Covalent Organic Frameworks for Exceptional Mercury Removal from Aqueous Solutions. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6b12328

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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.