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Characterizing Nitrogen Sites in Nitrogen-Doped Reduced Graphene Oxide: A Combined Solid-State 15N NMR, XPS, and DFT Approach

Accepted version
Peer-reviewed

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Abstract

Despite the potential applications in energy storage and conversion systems such as Lioxygen batteries and fuel cells, the nature and distribution of doped nitrogen sites in reduced graphene oxides are still not well understood. In this work, we report a combined approach of 15N solid-state nuclear magnetic resonance (NMR) spectroscopy alongside the predominantly used Xray photoelectron spectroscopy (XPS) to characterize the nitrogen environments in reduced graphene oxides. Application of 1H-15N low-power double quantum cross polarization under fast magic angle spinning with Carr-Purcell-Meiboom-Gill scheme shows selective detection of protonated sites with low-power radiofrequency irradiation. NMR shift calculations of a series of N-containing molecules and a graphene nanoflake model were performed to help interpret the experimental data. This work demonstrates a powerful approach to identify and quantify the different nitrogen environments in doped graphene materials and can also be widely applied to similar graphitic carbon-based materials with other dopants.

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Keywords

40 Engineering, 34 Chemical Sciences, 3406 Physical Chemistry, 7 Affordable and Clean Energy

Journal Title

Journal of Physical Chemistry C

Conference Name

Journal ISSN

1932-7447
1932-7455

Volume Title

125

Publisher

American Chemical Society (ACS)

Rights

All rights reserved
Sponsorship
G.K. and C.P.G. thank EUHorizon 2020 GrapheneCore1-No.696656 for research funding. T.L. thanks Darwin College for Schlumberger Fellowship and Innovate UK for research funding. All authors thank Dr. Robert Weatherup and Dr. Chao Xu for fruitful discussions about XPS data analysis, Mr. Chris Amey for XPS measurements, and Dr. Jingyu Lu for TEM measurements. This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DESC0012704.