Evidence map›Paper›PMID 41602954›Full record

ArticleChem & bio engineering2026

In Situ Incorporation of Macromolecules in Zr-MOF Membranes for Separation of Organic Azeotropic Mixtures.

Lianhao Li, Binyu Mo, Renjie Miao, Wenqi Ji, Haonan Yang, Guozhen Liu, Gongping Liu, Wanqin Jin

Abstract read
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Article in Chem & bio engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 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

8 authors.

Lianhao LiState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Binyu MoState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Renjie MiaoState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Wenqi JiState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Haonan YangState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Guozhen LiuState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.
Gongping LiuState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.ORCID https://orcid.org/0000-0002-3859-1278
Wanqin JinState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, 30 Puzhu Road (S), Nanjing 211816, China.ORCID https://orcid.org/0000-0001-8103-4883

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metal-organic framework (MOF) membranes with adjustable angstrom-scale channels and versatile topological structures are promising for molecular separation. However, it remains a challenge to construct MOF membranes with precisely tuned pore sizes and tailored molecular affinity for achieving high-efficiency liquid molecular separation, particularly for organic azeotropic mixtures. Herein, we proposed an in situ macromolecule incorporation strategy to fabricate a Zr-MOF membrane with a tunable pore microenvironment for pervaporation separation of butanol-water and ethanol-ethyl acetate azeotropic mixtures. The membranes were prepared by an in situ interfacial growth process. The amino macromolecules (poly-(ether imide), PEI) were dispersed into a metal cluster solution, followed by coordination with ligands on the substrate surface for membrane synthesis, wherein the macromolecule was in situ incorporated within MOF pore apertures. The incorporated macromolecule narrowed the membrane pore size and provided molecularly accelerated transport sites, thus facilitating the fast and selective transport of water or ethanol molecules. When incorporating PEI (

Indexed as

amino moleculeazeotropic mixture separationin situ macromolecule incorporationpervaporationZr-MOF membrane

Identifiers

PMID41602954
PMCPMC12833625

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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.