Evidence map›Paper›PMID 41935033›Full record

ArticleNature communications2026

High-performance caffeic acid-based superhydrophilic membranes for efficient removal of microplastics-oil co-contaminants.

Qin Chen, Riri Liu, Wenyi Zhang, Jiale Du, Zijian Yu, Jinhan Chen, Quan Chen, Mengni Ge, Yue Wang, Dong Han Seo and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Nano Carbon-mesh with Excellent Bonding Performance via Hydro-cage De-shielding Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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

15 authors.

Qin ChenJiangxi Province Key Laboratory of Water Ecological Conservation in Headwater Regions, Jiangxi University of Science and Technology, 1958 Ke-jia Road, Ganzhou, China.
Riri LiuDepartment of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, Belgium.
Wenyi ZhangKey Laboratory of Rare Earths, Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, China.
Jiale DuDepartment of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, Belgium.
Zijian YuInstitute of Mechanics, Materials and Civil Engineering-Materials & Process Engineering, UC Louvain, Louvain-la-Neuve, Belgium.
Jinhan ChenFujian Key Laboratory of Aptamers Technology, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China.
Quan ChenDepartment of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, Belgium.
Mengni GeCollege of Materials Science and Engineering, Hohai University, Nanjing, China.
Yue WangDepartment of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, Belgium.ORCID http://orcid.org/0000-0002-2818-9787
Dong Han SeoInstitute of Energy Materials & Devices, Korea Institute of Energy Technology (KENTECH), Naju, Republic of Korea.ORCID http://orcid.org/0000-0003-4235-0095
Ming XieDepartment of Chemical Engineering, University of Bath, Bath, United Kingdom.ORCID http://orcid.org/0000-0003-2729-9746
Raf DewilDepartment of Chemical Engineering, Process and Environmental Technology Lab, KU Leuven, J. De Nayerlaan 5, Sint-Katelijne-Waver, Belgium.ORCID http://orcid.org/0000-0003-4717-5484
Wenyuan YeJiangxi Province Key Laboratory of Water Ecological Conservation in Headwater Regions, Jiangxi University of Science and Technology, 1958 Ke-jia Road, Ganzhou, China. ye0508@126.com.ORCID http://orcid.org/0000-0003-3832-3705
Jiuyang LinKey Laboratory of Rare Earths, Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, China. jylin@gia.cas.cn.ORCID http://orcid.org/0000-0003-4054-948X
Bart Van der BruggenDepartment of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, Belgium. bart.vanderbruggen@kuleuven.be.ORCID http://orcid.org/0000-0001-9913-2932

Funding

China Scholarship Council (CSC) 202206650006National Natural Science Foundation of China (National Science Foundation of China) 22378389
6 · The paper itself

Abstract

Microplastic-oil co-contaminants have raised high environmental concerns due to their persistence and synergistic toxicity. However, conventional separation membranes, typically designed for single-pollutant removal, are ineffective against such complex systems. Moreover, the strong interfacial interactions between microplastics and oil promote aggregate formation, leading to severe membrane fouling and compromised separation performance. Herein, we present a robust, antifouling, and superhydrophilic membrane fabricated via sodium periodate-assisted co-deposition of caffeic acid and ε-polylysine for simultaneously removing both oil droplets and microplastic particles from contaminated water. The resulting caffeic acid/ε-polylysine composite superhydrophilic membrane achieves an impressive separation efficacy for various oil-in-water emulsions and polystyrene microplastics of different sizes. Notably, it exhibits superior separation performance for challenging microplastic-oil co-contaminants, with rejection of 99.89% for oil and 96.99% for 100-nm polystyrene microplastics over 300-min continuous filtration. It also sustains a high flux recovery, reflecting robust antifouling properties and thus highlighting a great potential for practical application in complex wastewater treatment. Additionally, the membrane shows a remarkable antibacterial activity even after 30 days of immersion. This study demonstrates a scalable and sustainable strategy for fabricating high-performance polyphenol-based antifouling membranes, offering an effective solution to manage these emerging co-contaminants.

Identifiers

PMID41935033
PMCPMC13376387

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.