Identification of CO2 adsorption sites on MgO nanosheets by solid-state nuclear magnetic resonance spectroscopy
The detailed information on the surface structure and binding sites of oxide nanomaterials is crucial to understand the adsorption and catalytic processes and thus the key to develop better materials for related applications. However, experimental methods to reveal this information remain scarce. He...
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Published in: | Nature communications Vol. 13; no. 1; pp. 707 - 6 |
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Main Authors: | , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
London
Nature Publishing Group UK
04-02-2022
Nature Publishing Group Nature Portfolio |
Subjects: | |
Online Access: | Get full text |
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Summary: | The detailed information on the surface structure and binding sites of oxide nanomaterials is crucial to understand the adsorption and catalytic processes and thus the key to develop better materials for related applications. However, experimental methods to reveal this information remain scarce. Here we show that
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O solid-state nuclear magnetic resonance (NMR) spectroscopy can be used to identify specific surface sites active for CO
2
adsorption on MgO nanosheets. Two 3-coordinated bare surface oxygen sites, resonating at 39 and 42 ppm, are observed, but only the latter is involved in CO
2
adsorption. Double resonance NMR and density functional theory (DFT) calculations results prove that the difference between the two species is the close proximity to H, and CO
2
does not bind to the oxygen ions with a shorter O···H distance of approx. 3.0 Å. Extensions of this approach to explore adsorption processes on other oxide materials can be readily envisaged.
The characterization of the surface structure and binding sites of materials is crucial for designing advanced materials for adsorption processes. Here, the authors use
17
O solid-state nuclear magnetic resonance spectroscopy to identify specific CO
2
adsorption sites on MgO nanosheets. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-022-28405-6 |