The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Transition metal–nitrogen–carbon nanostructured catalysts for the oxygen reduction reaction: From mechanistic insights to structural optimization
Abstract
Accelerating the rate-limiting oxygen reduction reaction (ORR) at the cathode remains the foremost issue for the commercialization of fuel cells. Transition metal–nitrogen–carbon (M–N/C, M = Fe, Co, etc.) nanostructures are the most promising class of non-precious metal catalysts (NPMCs) with satisfactory activities and stabilities in practical fuel cell applications. However, the long-debated nature of the active sites and the elusive structure-performance correlation impede further developments of M–N/C materials. In this review, we present recent endeavors to elucidate the actual structures of active sites by adopting a variety of physicochemical techniques that may provide a profound mechanistic understanding of M–N/C catalysts. Then, we focus on the spectacular progress in structural optimization strategies for M–N/C materials with tailored precursor architectures and modified synthetic routes for controlling the structural uniformity and maximizing the number of active sites in catalytic materials. The recognition of the right active centers and site-specific engineering of the nanostructures provides future directions for designing advantageous M–N/C catalysts.
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
Electrocatalyst approaches and challenges for automotive fuel cells
Debe, M. K. (2012) · Nature · doi:10.1038/nature11115 · Cited by 5,885
High-Performance Electrocatalysts for Oxygen Reduction Derived from Polyaniline, Iron, and Cobalt
Wu, G., More, K. L., Johnston, C. M., & Zelenay, P. (2011) · Science · doi:10.1126/science.1200832 · Cited by 3,984
Recent advancements in Pt and Pt-free catalysts for oxygen reduction reaction
Nie, Y., Li, L., & Wei, Z. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00484a · Cited by 2,156
Zhong, H., Wang, J., Zhang, Y., Xu, W., Xing, W., Xu, D., Zhang, Y., & Zhang, X. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201408990 · Cited by 938
Liang, H.-W., Wu, Z.-Y., Chen, L.-F., Li, C., & Yu, S.-H. (2015) · Nano Energy · doi:10.1016/j.nanoen.2014.11.008 · Cited by 440
He, W., Jiang, C., Wang, J., & Lu, L. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201404333 · Cited by 386
Self-citation
Cited by
Single-Atom Catalysts: Synthetic Strategies and Electrochemical Applications
Chen, Y., Ji, S., Chen, C., Peng, Q., Wang, D., & Li, Y. (2018) · Joule · doi:10.1016/j.joule.2018.06.019 · Cited by 2,258
Zhang, J., Zhao, Y., Chen, C., Huang, Y.-C., Dong, C.-L., Chen, C.-J., Liu, R.-S., Wang, C., Yan, K., Li, Y., et al. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b09352 · Cited by 989
Defect engineering in earth-abundant electrocatalysts for CO2 and N2 reduction
Wang, Q., Lei, Y., Wang, D., & Li, Y. (2019) · Energy & Environmental Science · doi:10.1039/c8ee03781g · Cited by 574
Porphyrin-based frameworks for oxygen electrocatalysis and catalytic reduction of carbon dioxide
Liang, Z., Wang, H.-Y., Zheng, H., Zhang, W., & Cao, R. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01482f · Cited by 506
Atomic interface effect of a single atom copper catalyst for enhanced oxygen reduction reactions
Jiang, Z., Sun, W., Shang, H., Chen, W., Sun, T., Li, H., Dong, J., Zhou, J., Li, Z., Wang, Y., et al. (2019) · Energy & Environmental Science · doi:10.1039/c9ee02974e · Cited by 380
Metadata
- Authors
- Shen, M., Wei, C., Ai, K., & Lu, L.
- Venue
- Nano Research
- Publisher
- Tsinghua University Press
- Published
- 2017-01-18
- Volume
- 10
- Issue
- 5
- Pages
- 1449-1470
- ISSN
- 1998-0000 (ISSN-L)
- 1998-0124
- OpenAlex
- W2572403440
- Cited by
- 162
- References
- 6
- FWCI
- 6.12
- 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: Transition metal–nitrogen–carbon nanostructured catalysts for the oxygen reduction reaction: From mechanistic insights to structural optimization. https://app.cochat.ai/papers/10.1007/s12274-016-1400-7
- The paper
- Shen, M., Wei, C., Ai, K., & Lu, L. (2017). Transition metal–nitrogen–carbon nanostructured catalysts for the oxygen reduction reaction: From mechanistic insights to structural optimization. Nano Research. https://doi.org/10.1007/s12274-016-1400-7
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.
