The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Design Principle of Fe–N–C Electrocatalysts: How to Optimize Multimodal Porous Structures?
Abstract
The effect of porous structures on the electrocatalytic activity of N-doped carbon is studied by using electrochemical analysis techniques and the result is applied to synthesize highly active and stable Fe-N-C catalyst for oxygen reduction reaction (ORR). We developed synthetic procedures to prepare three types of N-doped carbon model catalysts that are designed for systematic comparison of the porous structures. The difference in their catalytic activity is investigated in relation to the surface area and the electrochemical parameters. We found that macro- and mesoporous structures contribute to different stages of the reaction kinetics. The catalytic activity is further enhanced by loading the optimized amount of Fe to prepare Fe-N-C catalyst. In both N-doped carbon and Fe-N-C catalysts, the hierarchical porous structure improved electrocatalytic performance in acidic and alkaline media. The optimized catalyst exhibits one of the best ORR performance in alkaline medium with excellent long-term stability in anion exchange membrane fuel cell and accelerated durability test. Our study establishes a basis for rationale design of the porous carbon structure for electrocatalytic applications.
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
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
Highly Crystalline Multimetallic Nanoframes with Three-Dimensional Electrocatalytic Surfaces
Chen, C., Kang, Y., Huo, Z., Zhu, Z., Huang, W., Xin, H. L., Snyder, J. D., Li, D., Herron, J. A., Mavrikakis, M., et al. (2014) · Science · doi:10.1126/science.1249061 · Cited by 2,765
Wei, J., Zhou, D., Sun, Z., Deng, Y., Xia, Y., & Zhao, D. (2013) · Advanced Functional Materials · doi:10.1002/adfm.201202764 · Cited by 931
Boosting Fuel Cell Performance with Accessible Carbon Mesopores
Yarlagadda, V., Carpenter, M. K., Moylan, T. E., Kukreja, R. S., Koestner, R., Gu, W., Thompson, L., & Kongkanand, A. (2018) · ACS Energy Letters · doi:10.1021/acsenergylett.8b00186 · Cited by 686
Tang, J., Liu, J., Li, C., Li, Y., Tade, M. O., Dai, S., & Yamauchi, Y. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201407629 · Cited by 630
Cited by
Li, C., Li, Q., Kaneti, Y. V., Hou, D., Yamauchi, Y., & Mai, Y. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00021c · Cited by 513
Wu, Y., Li, X., Wei, Y., Fu, Z., Wei, W., Wu, X., Zhu, Q., & Xu, Q. (2021) · Advanced Materials · doi:10.1002/adma.202006965 · Cited by 378
Xie, X., Shang, L., Xiong, X., Shi, R., & Zhang, T. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202102688 · Cited by 291
Bioinspired and Bioderived Aqueous Electrocatalysis
Barrio, J., Pedersen, A., Favero, S., Luo, H., Wang, M., Sarma, S. C., Feng, J., Ngoc, L. T. T., Kellner, S., Li, A. Y., et al. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00429 · Cited by 80
Biomimetic Fe–Cu Porphyrrole Aerogel Electrocatalyst for Oxygen Reduction Reaction
Persky, Y., Kielesiński, Ł., Reddy, S. N., Zion, N., Friedman, A., Honig, H. C., Koszarna, B., Zachman, M. J., Grinberg, I., Gryko, D. T., et al. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c01972 · Cited by 36
Metadata
- Authors
- Lee, S. H., Kim, J., Chung, D. Y., Yoo, J. M., Lee, H. S., Kim, M. J., Mun, B. S., Kwon, S. G., Sung, Y.-E., & Hyeon, T.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-01-08
- Volume
- 141
- Issue
- 5
- Pages
- 2035-2045
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2908790313
- Cited by
- 541
- References
- 5
- FWCI
- 15.46
- 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: Design Principle of Fe–N–C Electrocatalysts: How to Optimize Multimodal Porous Structures? https://app.cochat.ai/papers/10.1021/jacs.8b11129
- The paper
- Lee, S. H., Kim, J., Chung, D. Y., Yoo, J. M., Lee, H. S., Kim, M. J., Mun, B. S., Kwon, S. G., Sung, Y.-E., & Hyeon, T. (2019). Design Principle of Fe–N–C Electrocatalysts: How to Optimize Multimodal Porous Structures? Journal of the American Chemical Society. https://doi.org/10.1021/jacs.8b11129
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.