Evidence map›Paper›PMID 42311062›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

Efficient Osmotic Energy Conversion Enabled by Self-Standing COF Membranes With Varied Sulfonic Acid Group Density.

Xi Ma, Xiaoxiao Cheng, Tamara Fischer, Jürgen Senker, Qi Sun, Seema Agarwal

Abstract read
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

6 authors.

Xi MaAdvanced Sustainable Polymers, Macromolecular Chemistry 2 and Bavarian Polymer Institute, University of Bayreuth, Bayreuth, Germany.
Xiaoxiao ChengZhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Tamara FischerDepartment of Chemistry, Inorganic Chemistry III, and Northern Bavarian NMR Centre, University of Bayreuth, Bayreuth, Germany.
Jürgen SenkerDepartment of Chemistry, Inorganic Chemistry III, and Northern Bavarian NMR Centre, University of Bayreuth, Bayreuth, Germany.ORCID https://orcid.org/0000-0002-7278-7952
Qi SunZhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-1698-8741
Seema AgarwalAdvanced Sustainable Polymers, Macromolecular Chemistry 2 and Bavarian Polymer Institute, University of Bayreuth, Bayreuth, Germany.

Funding

Collaborative Research Centre 492723217
6 · The paper itself

Abstract

The Gibbs free energy generated from the mixing of seawater and freshwater across a salinity gradient is considered one of the most significant yet underutilized renewable energy sources. Membrane-based reverse electrodialysis (RED) enables direct electricity generation from osmotic energy by harnessing the net ion flux driven by concentration gradients across ion-selective membranes. However, entropy generation caused by non-selective ion mixing significantly limits the power density of RED systems. Therefore, enhancing membrane ion selectivity is critical. 2D covalent organic frameworks (COFs) demonstrate remarkable potential for osmotic energy conversion due to their aligned 1D nanochannel, high porosity, and organized ionic groups. Herein, we present a strategy leveraging electrostatic repulsion to controllably fabricate TpPa-(SO

Indexed as

covalent organic frameworksionic membraneion separationosmotic energy

Identifiers

PMID42311062
PMCPMC13393992

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.