Evidence map›Paper›PMID 42589729›Full record

ArticlePolymers2026

Loofah-Inspired Hierarchical Omniphobic Membrane for Efficient Dissolved Gas Extraction.

Wei Zhang, Haifeng Gao, Xuran Zhu, Yanzong Meng, Leyu Shen, Zhongyao Jiang, Hongjian Gao

Abstract read
In one paragraph

Article in Polymers, 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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0citing papers in PubMed
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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

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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.

Wei ZhangChina Electric Power Research Institute, Beijing 100192, China.ORCID 0000-0002-0248-4286
Haifeng GaoChina Electric Power Research Institute, Beijing 100192, China.
Xuran ZhuChina Electric Power Research Institute, Beijing 100192, China.ORCID 0000-0003-1877-6141
Yanzong MengChina Electric Power Research Institute, Beijing 100192, China.
Leyu ShenChina Electric Power Research Institute, Beijing 100192, China.
Zhongyao JiangChina Electric Power Research Institute, Beijing 100192, China.
Hongjian GaoChina Electric Power Research Institute, Beijing 100192, China.

Funding

State Grid Corporation of China (China) 5500-202455420A-3-5-YS
6 · The paper itself

Abstract

To address the persistent challenge of membrane wetting during oil-gas separation in transformer condition monitoring, an omniphobic composite membrane was developed to facilitate the reliable online detection of dissolved gases. An F-CNTs/Teflon AF/PVDF composite membrane, featuring a loofah-like hierarchical structure and omniphobic properties, was fabricated via spraying-deposition strategy on the polyvinylidene fluoride (PVDF) substrate. The morphology, surface chemical composition, wettability and stability of the F-CNTs/Teflon AF/PVDF composite membrane were systematically characterized. Subsequently, the oil-gas separation performance of the composite membrane was evaluated using standard transformer oil containing dissolved gases as the feed solution. The results indicated that fluorinated carbon nanotubes (F-CNTs) were successfully modified onto the membrane surface, creating a re-entrant morphology composed of an intersecting nanotube network that mimics the hierarchical architecture of a loofah. The F-CNTs/Teflon AF/PVDF composite membrane exhibited exceptional omniphobicity, achieving contact angles of 168.2 ± 1.5° and 127.5 ± 1.0° towards DI water and mineral insulating oil, respectively. Additionally, the loofah-inspired composite membrane demonstrated robust thermal and ultrasonic stability. In oil-gas separation tests, the omniphobic membrane displayed a rapid response and high efficiency for dissolved gas extraction, achieving dynamic equilibrium within 64 min. Furthermore, the modification improved permeation efficiency by 25.6%. These results suggest that the developed omniphobic membrane is a promising alternative for oil-gas separation in the condition monitoring of oil-filled electrical equipment.

Indexed as

dissolved gas extractionhierarchical structureloofah-inspiredoil-gas separationomniphobic membranespraying-deposition

Identifiers

PMID42589729
PMCPMC13468637

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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.