B, N co-doped carbon nanosheets derived from graphene quantum dots: Improving the pseudocapacitive performance by efficient trapping nitrogen
Graphical abstract
B, N co-doped carbon nanosheets prepared by boric acid-assisted carbonization show an increased nitrogen content than the precursor graphene quantum dots, enabling high pseudocapacitance for supercapacitor electrodes.
Introduction
Supercapacitors have attracted great attention as next-generation energy storage devices because of the high power density, superior lifetime, and good safety. Carbon materials are widely used for supercapacitors due to the large surface area and good conductivity [1], [2]. But the unsatisfied capacitance mainly coming from the electrical double-layer behavior still hinders their practical applications. To improve the capacitance, various pseudocapacitive components such as metal oxides and conducting polymers have been composited with carbon materials [2], [3], [4]. However, the poor rate performance and cycle stability of most pseudocapacitive materials severely deviate from the fast and stable energy storage feature of supercapacitors. By contrast, heteroatom doping of carbon materials can provide additional pseudocapacitance but without sacrificing the fast and stable energy storage ability, thus has attracted great research attention. Particularly, nitrogen-doping has been mostly investigated because of the high electronegativity and excellent compatibility towards carbon skeleton [2], [5], [6], [7].
Two main routes are commonly used for preparing nitrogen-doped carbons: direct pyrolysis of nitrogen-containing precursors [8], [9], [10] and post-treatment with nitrogen-containing reagents [11], [12], [13]. But both of them confront with a severe issue of low nitrogen content because of the unstable feature of nitrogen-containing functional groups at high treating temperature. For example, nitrogen-containing polymers such as polyacrylonitrile [9], [14], [15], polydopamine [10], [16], [17], and polyaniline [8], [18], [19], [20] are commonly used for preparing nitrogen-doped carbons. But gradual decomposition and release of nitrogen species during pyrolysis result in a much lower nitrogen content (<4 at.%) in the final productions than that in the precursors (>20 at.%). In addition, pyrolytic carbon usually has a low specific surface area [20], [21]. Thus physical or chemical activation is usually needed to increase the surface area, further resulting in the loss of nitrogen during severe chemical etching [22], [23], [24]. Therefore, effective preserving of nitrogen species in doped carbon materials still remains a big challenge.
To increase the nitrogen content, several novel strategies have been developed to trap nitrogen into doped carbon materials, such as pyrolysis nitrogen-containing precursors in a confined space [25], [26], [27]; co-pyrolysis with nitrogen-containing molecules [28], [29], [30], [31]; and introduce active sites to facilitate nitrogen-doping [32], [33]. For example, through pyrolyzing polyacrylonitrile in confined SiO2 shells, nitrogen-doped hollow carbon spheres have been prepared with a high nitrogen content of 8.08 wt% [25]. Liu et al. have developed a general fluorination and subsequent post-treatment approach to achieve heteroatom (nitrogen, sulfur, or boron) “super-doping” for graphene, graphene quantum dots, or single-walled carbon nanotubes with ultrahigh heteroatom contents [32]. However, most of the methods are usually tedious and time-consuming, thus may not suitable for large-scale application.
Aiming at the above mentioned issues, herein, we developed a simple boric acid-assisted strategy to effectively trap nitrogen in B, N co-doped carbon nanosheets (BN-CNSs). Graphene quantum dots (GQDs) have attracted great attention in the fields of biosensors, catalysis, and energy storage materials owing to their small particle sizes, abundant edge sites, and various functional groups [34], [35], [36], [37], [38]. Using GQDs with an initial nitrogen content of 5.0 at.% as precursors, BN-CNSs show an increased nitrogen content of 7.2 at.% and a boron content of 4.4 at.% due to the balancing of the positive and negative charges in carbon skeleton (Fig. 1). This phenomenon is much different from the previous reports that nitrogen content is drastically reduced after thermal treatment of the precursors. As an exemplified application for supercapacitors, BN-CNSs with a surface area of 817 m2 g−1 perform a specific capacitance of 257 F g−1 at 1 A g−1 with high pseudocapacitive contribution owing to abundant active sites by highly doped heteroatoms. They also present a good rate capability of 150 F g−1 at 50 A g−1 as well as remarkable cycle stability. Our work broadens the avenue to design highly-doped functional carbon materials.
Section snippets
Experimental section
GQDs were prepared by a chemical oxidation method using bituminous coal powder (Heishan, Xinjiang, China) as the precursor according to our previous works [39], [40].
Result and discussion
In this work, boric acid acts as both boron source and template agent [6]. From the SEM image given in Fig. 2a, BN-CNSs show a thin-layer lamellar morphology with the lateral size of 2–5 μm. The high magnification image shows a transparent silk-like structure with a uniform thickness of ca. 6 nm (Fig. 2b) [6]. For comparison, we recall our previous work that the GQD-derived CNSs have been prepared using Mg(OH)2 platelets as inert templates at the same carbonization condition [39]. Despite the
Conclusion
In summary, we have prepared BN-CNSs with a nitrogen content of 7.2 at.%, a boron content of 4.4 at.%, and a high surface area of 817 m2 g−1 using GQDs with a nitrogen content of 5.0 at.% as the precursors through a boric acid assisted carbonization. Interestingly, BN-CNSs show a higher nitrogen content than the precursor GQDs even after high temperature treatment. The trapping of nitrogen is ascribed to the stabilization of carbon skeleton by balancing the positive and negative charges. Owing
CRediT authorship contribution statement
Jing Li: Writing - original draft, Methodology, Formal analysis. Yue Dong: Writing - original draft, Methodology, Formal analysis. Jiayao Zhu: Methodology, Formal analysis. Luxiang Wang: Methodology, Formal analysis. Wenhui Tian: Formal analysis. Jing Zhao: Formal analysis. He Lin: Methodology. Su Zhang: Conceptualization, Formal analysis, Supervision. Yali Cao: Conceptualization, Formal analysis, Supervision. Huaihe Song: Conceptualization, Formal analysis, Supervision. Dianzeng Jia:
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 51702275, 21771157), the National Natural Science Foundation of Xinjiang Autonomous Region (2020D01C019), the Scientific Research Program of the Higher Education Institution of Xinjiang (XJEDU2017S003), Xinjiang Tianshan Xuesong Project (2018XS28), and Xinjiang Tianchi Doctoral Project.
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These authors contributed equally to this work.