The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Carbonized Nanoscale Metal–Organic Frameworks as High Performance Electrocatalyst for Oxygen Reduction Reaction
Abstract
The oxygen reduction reaction (ORR) is one of the key steps in clean and efficient energy conversion techniques such as in fuel cells and metal-air batteries; however, several disadvantages of current ORRs including the kinetically sluggish process and expensive catalysts hinder mass production of these devices. Herein, we develop carbonized nanoparticles, which are derived from monodisperse nanoscale metal organic frameworks (MIL-88B-NH3), as the high performance ORR catalysts. The onset potential and the half-wave potential for the ORR at these carbonized nanoparticles is up to 1.03 and 0.92 V (vs RHE) in 0.1 M KOH solution, respectively, which represents the best ORR activity of all the non-noble metal catalysts reported so far. Furthermore, when used as the cathode of the alkaline direct fuel cell, the power density obtained with the carbonized nanoparticles reaches 22.7 mW/cm2, 1.7 times higher than the commercial Pt/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
Armand, M., & Tarascon, J.-M. (2008) · Nature · doi:10.1038/451652a · Cited by 19,612
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
Tian, N., Zhou, Z.-Y., Sun, S.-G., Ding, Y., & Wang, Z. L. (2007) · Science · doi:10.1126/science.1140484 · Cited by 3,073
Hydrogen evolution by a metal-free electrocatalyst
Zheng, Y., Jiao, Y., Zhu, Y., Li, L. H., Han, Y., Chen, Y., Du, A., Jaroniec, M., & Qiao, S. Z. (2014) · Nature Communications · doi:10.1038/ncomms4783 · Cited by 2,236
High oxygen-reduction activity and durability of nitrogen-doped graphene
Geng, D., Chen, Y., Chen, Y., Li, Y., Li, R., Sun, X., Ye, S., & Knights, S. (2011) · Energy & Environmental Science · doi:10.1039/c0ee00326c · Cited by 1,219
Yang, S., Feng, X., Wang, X., & Müllen, K. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201100170 · Cited by 1,109
Metal–organic frameworks as platforms for clean energy
Li, S.-L., & Xu, Q. (2013) · Energy & Environmental Science · doi:10.1039/c3ee40507a · Cited by 970
Cited by
Oxygen Reduction in Alkaline Media: From Mechanisms to Recent Advances of Catalysts
Ge, X., Sumboja, A., Wuu, D., An, T., Li, B., Goh, F. W. T., Hor, T. S. A., Zong, Y., & Liu, Z. (2015) · ACS Catalysis · doi:10.1021/acscatal.5b00524 · Cited by 1,341
Jiao, L., Zhou, Y.-X., & Jiang, H.-L. (2016) · Chemical Science · doi:10.1039/c5sc04425a · Cited by 798
Recent developments of carbon-based electrocatalysts for hydrogen evolution reaction
Zhou, W., Jia, J., Lu, J., Yang, L., Hou, D., Li, G., & Chen, S. (2016) · Nano Energy · doi:10.1016/j.nanoen.2016.08.027 · Cited by 744
Electrochemical oxygen reduction catalysed by Ni3(hexaiminotriphenylene)2
Miner, E. M., Fukushima, T., Sheberla, D., Sun, L., Surendranath, Y., & Dincă, M. (2016) · Nature Communications · doi:10.1038/ncomms10942 · Cited by 710
Design Strategies toward Advanced MOF‐Derived Electrocatalysts for Energy‐Conversion Reactions
Liu, J., Zhu, D., Guo, C., Vasileff, A., & Qiao, S. (2017) · Advanced Energy Materials · doi:10.1002/aenm.201700518 · Cited by 668
Metadata
- Authors
- Zhao, S., Yin, H., Du, L., He, L., Zhao, K., Chang, L., Yin, G., Zhao, H., Liu, S., & Tang, Z.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-12-03
- Volume
- 8
- Issue
- 12
- Pages
- 12660-12668
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W1986186070
- Cited by
- 532
- References
- 8
- FWCI
- 21.39
- 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: Carbonized Nanoscale Metal–Organic Frameworks as High Performance Electrocatalyst for Oxygen Reduction Reaction. https://app.cochat.ai/papers/10.1021/nn505582e
- The paper
- Zhao, S., Yin, H., Du, L., He, L., Zhao, K., Chang, L., Yin, G., Zhao, H., Liu, S., & Tang, Z. (2014). Carbonized Nanoscale Metal–Organic Frameworks as High Performance Electrocatalyst for Oxygen Reduction Reaction. ACS Nano. https://doi.org/10.1021/nn505582e
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.
