Evidence map›Paper›PMID 36770339›Full record

ArticleNanomaterials (Basel, Switzerland)2023

Evaluation of Mask Performances in Filtration and Comfort in Fabric Combinations.

Ji Wang, Renhai Zhao, Yintao Zhao, Xin Ning

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2023. 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

4 authors.

Ji WangIndustrial Research Institute of Nonwovens & Technical Textiles, College of Textiles & Clothing, Shandong Center for Engineered Nonwovens, Qingdao University, Qingdao 266071, China.
Renhai ZhaoIndustrial Research Institute of Nonwovens & Technical Textiles, College of Textiles & Clothing, Shandong Center for Engineered Nonwovens, Qingdao University, Qingdao 266071, China.
Yintao ZhaoIndustrial Research Institute of Nonwovens & Technical Textiles, College of Textiles & Clothing, Shandong Center for Engineered Nonwovens, Qingdao University, Qingdao 266071, China.
Xin NingIndustrial Research Institute of Nonwovens & Technical Textiles, College of Textiles & Clothing, Shandong Center for Engineered Nonwovens, Qingdao University, Qingdao 266071, China.ORCID 0000-0002-1476-0989

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A systemic study on improving particulate pollutant filtration efficiency through the combination of conventional fabrics is presented with the objective of finding comfortable, yet effective airway mask materials and products. Fabrics, nonwovens, and their combinations made of cotton, silk, wool, and synthetic fibers are examined on their filtration efficiency for aerosol particles with diameters ranging from 0.225 μm to 3.750 μm under industry-standard testing conditions. It is found that composite fabrics can improve filtration efficiency more than just layers of the same fabric, and the filtration quality factor of some of the fabric combinations can exceed that of the standard melt-blown materials. In addition, fabric friction and charging between the combined layers also improve filtration efficiency substantially. With a broader understanding of the fabric characteristics, we may design mask products with reduced facial skin discomfort, better aesthetics, as well as the ability to alleviate the environmental impact of discarded protective masks in the extended period of controlling the transmission of pollutants and viruses, such as during the COVID-19 pandemic.

Indexed as

aerosolfabric combinationfabric maskfiltration efficiencyfiltration quality factor

Identifiers

PMID36770339
PMCPMC9919832

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.