Evidence map›Paper›PMID 37047832›Full record

ReviewInternational journal of molecular sciences2023

Recent Advances in Manganese-Based Materials for Electrolytic Water Splitting.

Jing Hu, Yuru Zhou, Yinan Liu, Zhichao Xu, Haijin Li

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. High-performance spinel NiMnScientific reports · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jing HuSchool of Energy and Environment, Anhui University of Technology, Ma'anshan 243002, China.ORCID 0000-0003-4381-615X
Yuru ZhouSchool of Energy and Environment, Anhui University of Technology, Ma'anshan 243002, China.
Yinan LiuSchool of Energy and Environment, Anhui University of Technology, Ma'anshan 243002, China.
Zhichao XuSchool of Energy and Environment, Anhui University of Technology, Ma'anshan 243002, China.
Haijin LiSchool of Energy and Environment, Anhui University of Technology, Ma'anshan 243002, China.

Funding

National Natural Science Foundation of China 21972065National Natural Science Foundation of China 22205007
6 · The paper itself

Abstract

Developing earth-abundant and highly effective electrocatalysts for electrocatalytic water splitting is a prerequisite for the upcoming hydrogen energy society. Recently, manganese-based materials have been one of the most promising candidates to replace noble metal catalysts due to their natural abundance, low cost, adjustable electronic properties, and excellent chemical stability. Although some achievements have been made in the past decades, their performance is still far lower than that of Pt. Therefore, further research is needed to improve the performance of manganese-based catalytic materials. In this review, we summarize the research progress on the application of manganese-based materials as catalysts for electrolytic water splitting. We first introduce the mechanism of electrocatalytic water decomposition using a manganese-based electrocatalyst. We then thoroughly discuss the optimization strategy used to enhance the catalytic activity of manganese-based electrocatalysts, including doping and defect engineering, interface engineering, and phase engineering. Finally, we present several future design opportunities for highly efficient manganese-based electrocatalysts.

Indexed as

Earth, PlanetManganeseCatalysisElectrolysisElectrolytesWaterElectrolytesManganeseWaterelectrocatalysishydrogen evolution reactionmanganese-basedoptimization strategyoxygen evolution reaction

Identifiers

PMID37047832
PMCPMC10095233

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.