Evidence map›Paper›PMID 35853838›Full record

ArticleChemSusChem2022

Two-Dimensional Membranes with Highly Charged Nanochannels for Osmotic Energy Conversion.

Yijun Qian, Dan Liu, Guoliang Yang, Jinqiu Chen, Yuxi Ma, Lifeng Wang, Xungai Wang, Weiwei Lei

Open access · hybridAbstract read
In one paragraph

Article in ChemSusChem, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
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

6 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
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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

8 authors at 1 institution in 1 country.

Yijun QianInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Dan LiuInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Guoliang YangInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Jinqiu ChenInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Yuxi MaInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Lifeng WangInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Xungai WangInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.
Weiwei LeiInstitute for Frontier Materials, Deakin University, Locked Bag 20000, Geelong, Victoria, 3220, Australia.ORCID http://orcid.org/0000-0003-2698-299X
Deakin University · AU

Funding

Australian Research Council Discovery Program DP190103290Australian Research Council Future Fellowship FT200100730
6 · The paper itself

Abstract

Inadequate mass transportation of semipermeable membranes causes poor osmotic energy conversion from salinity-gradient. Here, the lamellar graphene oxide membranes (GOMs) constructed with numerous fusiform-like nanochannels, that are pre-filled with negatively charged polyanion electrolytes, to both enhance the ion permeability and ion selectivity of the membrane for energy harvest from the salinty gradient, were developed. The as-prepared membrane achieved the maximum output power density of ∼4.94 W m

Indexed as

Membranes, ArtificialSalinityCations, MonovalentOsmosisWaterCations, MonovalentMembranes, ArtificialWater2D lamellar membraneenergy conversiongraphene oxideion transportosmotic power generation

Identifiers

PMID35853838
PMCPMC9804272
OpenAlexW4286214376

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.