The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Three-dimensionally hierarchical MoS2/graphene architecture for high-performance hydrogen evolution reaction
Abstract
Abstract Molybdenum disulfide (MoS2) has been considered as a potential alternative to precious metal catalysts for the hydrogen evolution reaction (HER). However, the performance of MoS2 is still limited due to the poor electron conductivity, scarce active sites and low structural stability. A multiscale design of MoS2 in the structure and atomic composition is needed but still a great challenge. Herein, we report a well-organized three-dimensionally (3D) mesoporous hybrid structure of Co-doped MoS2 and graphene for highly efficient electrocatalytic HER. The mesoporous morphology ensures the well-dispersion of MoS2 layers and exposure of abundant edge sites. Doping of Co atoms into the MoS2 lattice improves the intrinsic HER activity of in-plane sulfur sites. The highly conductive and robust graphene network enhances the conduction of electrons and simultaneously improves the stability of the hybrid structure. The catalyst achieves a current density of 10 mA cm−2 at an overpotential of 143 mV versus the reversible hydrogen electrode (RHE) which is 200 mV lower compared with pure 2D MoS2, and maintains the activity for over 5000 cyclic voltammetry sweeps. This work provides a promising strategy for efficiently enhancing both the activity and stability of MoS2-based catalyst for HER.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kibsgaard, J., Chen, Z., Reinecke, B. N., & Jaramillo, T. F. (2012) · Nature Materials · doi:10.1038/nmat3439 · Cited by 3,234
Nanostructured hydrotreating catalysts for electrochemical hydrogen evolution
Morales-Guio, C. G., Stern, L.-A., & Hu, X. (2014) · Chemical Society Reviews · doi:10.1039/c3cs60468c · Cited by 2,437
Xie, J., Zhang, J., Li, S., Grote, F., Zhang, X., Zhang, H., Wang, R., Lei, Y., Pan, B., & Xie, Y. (2013) · Journal of the American Chemical Society · doi:10.1021/ja408329q · Cited by 2,350
Two-dimensional transition metal dichalcogenide nanosheet-based composites
Tan, C., & Zhang, H. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00182f · Cited by 1,605
Merki, D., Vrubel, H., Rovelli, L., Fierro, S., & Hu, X. (2012) · Chemical Science · doi:10.1039/c2sc20539d · Cited by 941
Highly porous non-precious bimetallic electrocatalysts for efficient hydrogen evolution
Lu, Q., Hutchings, G. S., Yu, W., Zhou, Y., Forest, R. V., Tao, R., Rosen, J., Yonemoto, B. T., Cao, Z., Zheng, H., et al. (2015) · Nature Communications · doi:10.1038/ncomms7567 · Cited by 543
(0D/3D) MoS2 on porous graphene as catalysts for enhanced electrochemical hydrogen evolution
Liu, Y., Zhu, Y., Fan, X., Wang, S., Li, Y., Zhang, F., Zhang, G., & Peng, W. (2017) · Carbon · doi:10.1016/j.carbon.2017.05.092 · Cited by 60
Cited by
Wang, J., Fang, W., Hu, Y., Zhang, Y., Dang, J., Wu, Y., Chen, B., Zhao, H., & Li, Z. (2021) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2021.120490 · Cited by 259
Ali, A., & Shen, P. K. (2020) · Carbon Energy · doi:10.1002/cey2.26 · Cited by 199
Distance Synergy of MoS2‐Confined Rhodium Atoms for Highly Efficient Hydrogen Evolution
Meng, X., Ma, C., Jiang, L., Si, R., Meng, X., Tu, Y., Yu, L., Bao, X., & Deng, D. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202003484 · Cited by 198
Self-citationLiu, Z., Zeng, L., Yu, J., Yang, L., Zhang, J., Zhang, X., Han, F., Zhao, L., Li, X., Liu, H., et al. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.105940 · Cited by 157
Zhang, X., Jia, F., & Song, S. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.127013 · Cited by 145
Metadata
- Authors
- Meng, X., Yu, L., Ma, C., Nan, B., Si, R., Tu, Y., Deng, J., Deng, D., & Bao, X.
- Venue
- Nano Energy
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 61
- Pages
- 611-616
- ISSN
- 2211-2855 (ISSN-L)
- OpenAlex
- W2938637830
- Cited by
- 163
- References
- 7
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Three-dimensionally hierarchical MoS2/graphene architecture for high-performance hydrogen evolution reaction. https://app.cochat.ai/papers/10.1016/j.nanoen.2019.04.049
- The paper
- Meng, X., Yu, L., Ma, C., Nan, B., Si, R., Tu, Y., Deng, J., Deng, D., & Bao, X. (2019). Three-dimensionally hierarchical MoS2/graphene architecture for high-performance hydrogen evolution reaction. Nano Energy. https://doi.org/10.1016/j.nanoen.2019.04.049
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
