Evidence map›Paper›PMID 42188502›Full record

ArticleNanomaterials (Basel, Switzerland)2026

Preparation and Performance Study of Three-Layer Composite Filter Media for Channel-Type Ultra-Low Penetration Air Filters.

Mingyu Li, Desheng Wang, Yuhan Wang, Jinhao Xie, Yuqiu Liu, Yun Liang, Jian Kang, Hao Wang

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 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

8 authors.

Mingyu LiSchool of Light Industry and Engineering, South China University of Technology, Guangzhou 510641, China.
Desheng WangState Key Laboratory of Chemistry for NBC Hazards Protection, Beijing 102205, China.
Yuhan WangSchool of Light Industry and Engineering, South China University of Technology, Guangzhou 510641, China.
Jinhao XieState Key Laboratory of Chemistry for NBC Hazards Protection, Beijing 102205, China.
Yuqiu LiuSchool of Light Industry and Engineering, South China University of Technology, Guangzhou 510641, China.
Yun LiangSchool of Light Industry and Engineering, South China University of Technology, Guangzhou 510641, China.
Jian KangState Key Laboratory of Chemistry for NBC Hazards Protection, Beijing 102205, China.
Hao WangState Key Laboratory of Chemistry for NBC Hazards Protection, Beijing 102205, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To satisfy the requirements of channel-type ultra-low penetration air (ULPA) filters for high filtration efficiency, low pressure drop, and good corrugation processability, a three-layer composite filter medium with a bast-fiber surface layer/glass wool-lyocell blended core layer/bast-fiber surface layer structure was designed and prepared. The effects of surface-layer material, core-layer fiber composition, surface-layer basis weight, and processing conditions on the overall performance of the medium were systematically investigated. Bast-fiber paper exhibited the best corrugation processability and mechanical performance and was selected as the surface layer. The optimal core-layer composition was 25 wt.% 475-79 glass wool fibers, 30 wt.% 475-59 glass wool fibers, and 45 wt.% lyocell fibers, yielding an original-sheet filtration efficiency of 99.9996% and a pressure drop of 381 Pa. Further optimization showed that a bast-fiber surface layer with a basis weight of 15 g/m

Indexed as

channel-type filterglass wool fiberpressure dropthree-layer composite filter mediumultra-low penetration air filtration

Identifiers

PMID42188502
PMCPMC13209818

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.