Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 294

The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.

Strategies for Designing High-Performance Hydrogen Evolution Reaction Electrocatalysts at Large Current Densities above 1000 mA cm–2

Jin, Mengtian, Zhang, Xian, Niu, Shuzhang, Wang, Qun, Huang, Runqing, Ling, Ruihua, Huang, Jiaqi, Shi, Run, Amini, Abbas, Cheng, Chun·2022·ACS Nano

Abstract

The depletion of fossil fuels and rapidly increasing environmental concerns have urgently called for the utilization of clean and sustainable sources for future energy supplies. Hydrogen (H 2 ) is recognized as a prioritized green resource with little environmental impact to replace traditional fossil fuels. Electrochemical water splitting has become an important method for large-scale green production of hydrogen. The hydrogen evolution reaction (HER) is the cathodic half-reaction of water splitting that can be promoted to produce pure H 2 in large quantities by active electrocatalysts. However, the unsatisfactory performance of HER electrocatalysts cannot follow the extensive requirements of industrial-scale applications, including working efficiently and stably over long periods of time at high current densities (⩾1000 mA cm –2 ). In this review, we study the crucial issues when electrocatalysts work at high current densities and summarize several categories of strategies for the design of high-performance HER electrocatalysts. We also discuss the future challenges and opportunities for the development of HER 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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acsnano.2c02820

Cites

112 of 112 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Research works printed but not linked: 1

  1. 10.1016/j.jcis.2019.03.026 · doi:10.1016/j.jcis.2019.03.026

Cited by

See all 294

Metadata

Authors
Jin, M., Zhang, X., Niu, S., Wang, Q., Huang, R., Ling, R., Huang, J., Shi, R., Amini, A., & Cheng, C.
Venue
ACS Nano
Published
2022-08-11
Volume
16
Issue
8
Pages
11577-11597
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W4293257129
Cited by
294
References
112
FWCI
10.52
Type
journal article
Language
en
Open access
closed
Funders
  • Guangdong Science and Technology Department (2019A1515011805)
  • Shenzhen Fundamental Research Program (JCYJ20190809115407617)
  • National Natural Science Foundation of China (51972161, 91963129)
  • Guangdong Provincial Key Laboratory of Energy Materials for Electric Power, Southern University of Science and Technology (2018B030322001)

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

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.