Evidence map›Paper›PMID 42390840›Full record

ArticleAngewandte Chemie (International ed. in English)2026

Scalable Art-Inspired Tessellated Covalent Organic Framework Membranes Enable Highly Selective Ion Separation.

Zhenyi Zhao, Rui Guo, Tianxiang Yang, Zhi Wang, Tiezheng Tong, Yawei Du, Song Zhao

Abstract read
In one paragraph

Article in Angewandte Chemie (International ed. in English), 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

7 authors.

Zhenyi ZhaoSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.ORCID https://orcid.org/0009-0001-7550-8912
Rui GuoSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.
Tianxiang YangSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.
Zhi WangSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.ORCID https://orcid.org/0000-0001-7524-0322
Tiezheng TongSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA.ORCID https://orcid.org/0000-0002-9289-3330
Yawei DuSchool of Chemical Engineering and Technology, Engineering Research Center of Seawater Utilization of Ministry of Education, Hebei University of Technology, Tianjin, P. R. China.ORCID https://orcid.org/0000-0001-8396-9949
Song ZhaoSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.ORCID https://orcid.org/0000-0002-7192-5540

Funding

National Natural Science Foundation of China 22278304State Key Laboratory of Chemical Engineering and Low-Carbon Technology SKL-ChE-23T07
6 · The paper itself

Abstract

Covalent organic frameworks (COFs), distinguished by their periodic and tunable network structures, exhibit great potential for molecular and ionic separation. Nevertheless, fabricating COF membranes with angstrom-scale pores faces challenges in precisely controlling channel dimensions and achieving seamless integration of frameworks. Here, we report the synthesis of a substoichiometric aminal-linked COF with a pore size of ∼5 Å and develop a covalent tessellation strategy inspired by Escher's art and derived from interfacial polymerization, that successfully fabricates defect-free tessellated COF (tCOF) membranes. The resulting tCOF membrane exhibits ultra-microporous structures, achieving high water permeance of 10.2 L m

Indexed as

aminal‐linked covalent organic frameworkion separationmembranenanofiltrationtessellated pattern

Identifiers

PMID42390840
PMCPMC13528465

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.