Evidence map›Paper›PMID 35780505›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Ultrathin and Ultrastrong Kevlar Aramid Nanofiber Membranes for Highly Stable Osmotic Energy Conversion.

Li Ding, Dan Xiao, Zihao Zhao, Yanying Wei, Jian Xue, Haihui Wang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

6 authors.

Li DingSchool of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, P. R. China.
Dan XiaoSchool of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, P. R. China.
Zihao ZhaoSchool of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, P. R. China.
Yanying WeiSchool of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, P. R. China.
Jian XueSchool of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510640, P. R. China.
Haihui WangBeijing Key Laboratory for Membrane Materials and Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, P. R. China.ORCID https://orcid.org/0000-0002-2917-4739

Funding

China Postdoctoral Science Foundation 2019M662920China Postdoctoral Science Foundation 2019TQ0101Guang Dong Basic and Applied Basic Research Foundation 2019A1515110958National Natural Science Foundation of China 21861132013National Natural Science Foundation of China 22008077National Natural Science Foundation of China 22138005National Natural Science Foundation of China 22141001
6 · The paper itself

Abstract

An ion-selective membrane can directly convert the osmotic energy to electricity through reverse electrodialysis. However, developing an advanced membrane that simultaneously possesses high power density, excellent mechanical strength, and convenient large-scale production for practical osmotic energy conversion, remains challenging. Here, the fabrication of ultrathin and ultrastrong Kevlar aramid nanofiber (KANF) membranes with interconnected three-dimensional (3D) nanofluidic channels via a simple blade coating method is reported. The negatively charged 3D nanochannels show typical surface-charge-governed nanofluidic ion transport and exhibit excellent cation selectivity. When applied to osmotic energy conversion, the power density of the KANF membrane-based generator reaches 4.8 W m

Indexed as

ion selectiveKevlar aramid nanofibermembranenanofluidicosmotic energy

Identifiers

PMID35780505
PMCPMC9443462

What OpenQuestion holds

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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.