Cited by 111Electrocatalysts for Energy ConversionElectrochemical sensors and biosensorsCO2 Reduction Techniques and Catalysts

KOH-treated reduced graphene oxide: 100% selectivity for H2O2 electroproduction

Zhu, Jingyi, Xiao, Xue, Zheng, Kai, Li, Fumin, Ma, Ge, Yao, Hong-Chang, Wang, Xin, Chen, Yu·2019·Carbon

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Crossref · 10.1016/j.carbon.2019.07.009

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42 of 42 references verified0 citation errors · 0 not checkable

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Authors
Zhu, J., Xiao, X., Zheng, K., Li, F., Ma, G., Yao, H.-C., Wang, X., & Chen, Y.
Affiliations
  • Shaanxi Normal University (CN)
  • University of Newcastle Australia (AU)
  • South China Normal University (CN)
  • Zhengzhou University (CN)
Corresponding author
  • Wang, Xin
  • Chen, Yu
Venue
Carbon
Publisher
Elsevier BV
Published
2019-11
Volume
153
Pages
6-11
ISSN
0008-6223 (ISSN-L)
OpenAlex
W2954486617
Cited by
111
References
42
Citations by year
2026: 9 · 2025: 13 · 2024: 22 · 2023: 17 · 2022: 18 · 2021: 20 · 2020: 10 · 2019: 2
FWCI
2.66
Type
journal article
Language
en
Open access
closed
Locations
  • Carbon (publishedVersion)
  • NOVA (University of Newcastle Australia) (submittedVersion)
Funders
  • Fundamental Research Funds for the Central Universities
  • National Natural Science Foundation of China
Topics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
Keywords
reduced graphene oxide, KOH treatment, oxygen reduction reaction, two-electron pathway, electrocatalyst selectivity, alkaline solution, electrochemically active surface area, ether bonds, KBH4 reduction, electrocatalyst durability

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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.

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