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The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Three-dimensional porous g-C3N4 for highly efficient photocatalytic overall water splitting

Chen, Xianjie, Shi, Run, Chen, Qian, Zhang, Zijian, Jiang, Wenjun, Zhu, Yongfa, Zhang, Tierui·2019·Nano Energy

Abstract

Abstract Herein, this work constructs a three-dimensional porous graphitic carbon nitride assembled by highly crystalline and ultrathin nanosheets (3D g-C3N4 NS). 3D g-C3N4 NS could directly split pure water into H2 and O2 with high evolution rate up to 101.4 and 49.1 μmol g−1 h−1 under visible light, respectively, approximately 11.8 and 5.1 times higher than bulk g-C3N4 and g-C3N4 NS. Besides, it achieves a notable apparent quantum yield of 1.4% at 420 nm, significantly superior to previously reported Pt/g-C3N4. The efficient activity of 3D g-C3N4 NS is mainly attributed to its 3D interconnected open-framework, assembled by highly crystalline ultrathin nanosheet unit, provides a pathway for faster charge carrier transport. Moreover, benefitting from its 3D structure for preventing agglomeration of nanosheets, 3D g-C3N4 NS is stable for more than 100 h of overall water splitting reaction.

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Metadata

Authors
Chen, X., Shi, R., Chen, Q., Zhang, Z., Jiang, W., Zhu, Y., & Zhang, T.
Venue
Nano Energy
Publisher
Elsevier BV
Published
2019-05
Volume
59
Pages
644-650
ISSN
2211-2855 (ISSN-L)
OpenAlex
W2921331107
Cited by
716
References
5
FWCI
22.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Three-dimensional porous g-C3N4 for highly efficient photocatalytic overall water splitting. https://app.cochat.ai/papers/10.1016/j.nanoen.2019.03.010
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Chen, X., Shi, R., Chen, Q., Zhang, Z., Jiang, W., Zhu, Y., & Zhang, T. (2019). Three-dimensional porous g-C3N4 for highly efficient photocatalytic overall water splitting. Nano Energy. https://doi.org/10.1016/j.nanoen.2019.03.010

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.