Distinguishing faceted oxide nanocrystals with 17O solid-state NMR spectroscopy

Yuhong Li, Xin Ping Wu, Ningxin Jiang, Ming Lin, Li Shen, Haicheng Sun, Yongzheng Wang, Meng Wang, Xiaokang Ke, Zhiwu Yu, Fei Gao, Lin Dong, Xuefeng Guo, Wenhua Hou, Weiping Ding, Xue Qing Gong, Clare P. Grey, Luming Peng

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Facet engineering of oxide nanocrystals represents a powerful method for generating diverse properties for practical and innovative applications. Therefore, it is crucial to determine the nature of the exposed facets of oxides in order to develop the facet/morphology-property relationships and rationally design nanostructures with desired properties. Despite the extensive applications of electron microscopy for visualizing the facet structure of nanocrystals, the volumes sampled by such techniques are very small and may not be representative of the whole sample. Here, we develop a convenient 17O nuclear magnetic resonance (NMR) strategy to distinguish oxide nanocrystals exposing different facets. In combination with density functional theory calculations, we show that the oxygen ions on the exposed (001) and (101) facets of anatase titania nanocrystals have distinct 17O NMR shifts, which are sensitive to surface reconstruction and the nature of the steps on the surface. The results presented here open up methods for characterizing faceted nanocrystalline oxides and related materials.

Original languageEnglish (US)
Article number581
JournalNature communications
Issue number1
StatePublished - Dec 1 2017

Bibliographical note

Funding Information:
This work was supported by the National Basic Research Program of China (2013CB934800), the National Natural Science Foundation of China (NSFC) (21573103, 21421004, 21222302, and 20903056), NSFC—Royal Society Joint Program (21661130149 and 21111130201), Program for New Century Excellent Talents in University (NCET-10-0483), the Fundamental Research Funds for the Central Universities (1124020512), and National Science Fund for Talent Training in Basic Science (J1103310). The ECUST group also thanks the Programme of Introducing Talents of Discipline to Universities (B16017) and National Super Computing Centre in Jinan for computing time. L.P. thanks Royal Society and Newton Fund for Royal Society—Newton Advanced Fellowship. C.P.G. thanks the European Research Council for an Advanced Fellowship. This work was also supported by a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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