Evidence map›Paper›PMID 41169545›Full record

ArticleJACS Au2025

Turing COF Membranes with Tunable Patterns for Antibiotic Desalination.

Jingcheng Du, Qian Sun, Dong Cao, Ayan Yao, Weiwang Lim, Ji Ma, Ali A Al-Thuraya, Yumo Fan, Pengjia Dou, Jian Guan and 1 more

Abstract read
In one paragraph

Article in JACS Au, 2025. 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

11 authors.

Jingcheng DuState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Qian SunState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Dong CaoState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Ayan YaoState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Weiwang LimState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Ji MaState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Ali A Al-ThurayaState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.ORCID https://orcid.org/0009-0009-9251-508X
Yumo FanState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Pengjia DouState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Jian GuanState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Jiangtao LiuState Key Laboratory of Advanced Environmental Technology, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China.ORCID https://orcid.org/0000-0002-4569-7169

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tailorable membrane surface architectures are crucial for efficient and precise separation. The nanoemulsion regulation strategy, an emerging technique for constructing nanostructured membranes with tunable architectures, has been scarcely explored for the fabrication of covalent organic framework (COF) membranes. In this work, inspired by the vesicle storage, transport, and release mechanism in human cells and the natural self-assembly of emulsions at interfaces, we proposed a nanoemulsion-directed interfacial polymerization (NDIP) strategy to precisely construct COF membranes with tunable Turing patterns. The introduction of these Turing structures leads to COF membranes with fewer defects and an enlarged surface area, attributable to the nanoemulsion templating effect and the controlled monomer transport mechanism. Short-range van der Waals forces and electrostatic interactions between the nanoemulsions and monomers were elucidated through molecular dynamics simulations and experimental results. Moreover, we systematically explored the effects of variable nanoemulsification space, different internal microenvironments, and emulsifier chain length on membrane separation performance. The designed Turing COF membranes exhibit excellent antibiotic separation ability with a molecular weight cutoff of 289 g/mol and ultrahigh antibiotic/salt selectivity (124.1 for TC/NaCl). This study provides a new design perspective for patterned regulation in COF membranes and highlights the potential of emulsion-guided strategies for advanced molecular separations.

Indexed as

antibiotic desalinationcovalent−organic frameworkfree-standing membranenanoemulsion regulationTuring structures

Identifiers

PMID41169545
PMCPMC12569678

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.