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Two-dimensional covalent organic frameworks for carbon dioxide capture through channel-wall functionalization.

ABSTRACT: Ordered open channels found in two-dimensional covalent organic frameworks (2D COFs) could enable them to adsorb carbon dioxide. However, the frameworks' dense layer architecture results in low porosity that has thus far restricted their potential for carbon dioxide adsorption. Here we report a strategy for converting a conventional 2D COF into an outstanding platform for carbon dioxide capture through channel-wall functionalization. The dense layer structure enables the dense integration of functional groups on the channel walls, creating a new version of COFs with high capacity, reusability, selectivity, and separation productivity for flue gas. These results suggest that channel-wall functional engineering could be a facile and powerful strategy to develop 2D COFs for high-performance gas storage and separation.

SUBMITTER: Huang N

PROVIDER: S-EPMC4471552 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Two-dimensional covalent organic frameworks for carbon dioxide capture through channel-wall functionalization.

Huang Ning N Chen Xiong X Krishna Rajamani R Jiang Donglin D

Angewandte Chemie (International ed. in English) 20150122 10

Ordered open channels found in two-dimensional covalent organic frameworks (2D COFs) could enable them to adsorb carbon dioxide. However, the frameworks' dense layer architecture results in low porosity that has thus far restricted their potential for carbon dioxide adsorption. Here we report a strategy for converting a conventional 2D COF into an outstanding platform for carbon dioxide capture through channel-wall functionalization. The dense layer structure enables the dense integration of fun ...[more]

PMID: 25613010

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Project description:Covalent organic frameworks (COFs) are an emerging material family having several potential applications. Their porous framework and redox-active centers enable gas/ion adsorption, allowing them to function as safe, cheap, and tunable electrode materials in next-generation batteries, as well as CO2 adsorption materials for carbon-capture applications. Herein, we develop four polyimide COFs by combining aromatic triamines with aromatic dianhydrides and provide detailed structural and electrochemical characterization. Through density functional theory (DFT) calculations and powder X-ray diffraction, we achieve a detailed structural characterization, where DFT calculations reveal that the imide bonds prefer to form at an angle with one another, breaking the 2D symmetry, which shrinks the pore width and elongates the pore walls. The eclipsed perpendicular stacking is preferable, while sliding of the COF sheets is energetically accessible in a relatively flat energy landscape with a few metastable regions. We investigate the potential use of these COFs in CO2 adsorption and electrochemical applications. The adsorption and electrochemical properties are related to the structural and chemical characteristics of each COF, giving new insights for advanced material designs. For CO2 adsorption specifically, the two best performing COFs originated from the same triamine building block, which-in combination with force-field calculations-revealed unexpected structure-property relationships. Specific geometries provide a useful framework for Na-ion intercalation with retainable capacities and stable cycle life at a relatively high working potential (>1.5 V vs Na/Na+). Although this capacity is low compared to conventional inorganic Li-ion materials, we show as a proof of principle that these COFs are especially promising for sustainable, safe, and stable Na-aqueous batteries due to the combination of their working potentials and their insoluble nature in water.

Dataset Information

Two-dimensional covalent organic frameworks for carbon dioxide capture through channel-wall functionalization.

Publications

Two-dimensional covalent organic frameworks for carbon dioxide capture through channel-wall functionalization.

Similar Datasets

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