Evidence map›Paper›PMID 42657278›Full record

ArticlePrecision chemistry2026

Atomically Dispersed Cu-N

Xijun Wang, Ke Wang, Shihang Lu, Ziyang Liu, Chenlei Li, Miaosen Yang, Junfeng Liu

Abstract read
In one paragraph

Article in Precision chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Xijun WangJibei Electric Power Research Institute, State Grid Jibei Electric Power Company Limited, (North China Electric Power Research Institute Company Limited), Beijing 100045, China.
Ke WangState Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang, Beijing 100029, China.
Shihang LuState Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang, Beijing 100029, China.
Ziyang LiuState Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang, Beijing 100029, China.
Chenlei LiJibei Electric Power Research Institute, State Grid Jibei Electric Power Company Limited, (North China Electric Power Research Institute Company Limited), Beijing 100045, China.
Miaosen YangCollege of Chemical Engineering, Northeast Electric Power University, 169 Changchun Road, Chuanying, Jilin 132001, PR China.
Junfeng LiuState Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang, Beijing 100029, China.ORCID https://orcid.org/0000-0002-7455-6314

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vanadium flow batteries (VFBs), although promising for large-scale energy storage, are limited by sluggish redox kinetics and parasitic side reactions, resulting in substantial efficiency loss and poor cycling stability. To overcome these intrinsic limitations, an ideal electrocatalyst should simultaneously accelerate vanadium ion conversion and suppress undesirable side reactions, yet such catalysts have rarely been realized in VFB systems. Herein, we report the development of a copper-based single-atom catalyst featuring atomically dispersed Cu sites embedded in a nitrogen-doped porous carbon matrix (Cu SAs/NC) grown in situ on graphite felt (GF) as a highly active and cost-effective electrode for VFBs. The obtained Cu SAs/NC provides abundant exposed active sites, improved electrolyte wettability, and accelerated interfacial charge transfer. Notably, the single-atom Cu sites with a Cu-N

Indexed as

coordination environment engineeringcugraphite feltsingle-atom catalystvanadium flow battery

Identifiers

PMID42657278
PMCPMC13508506

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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.