Evidence map›Paper›PMID 37513082›Full record

ArticleNanomaterials (Basel, Switzerland)2023

Development of Nano-Sized Copper-Deposited Antimicrobial Air Filters Using a Mixed Melt-Blown Process.

Kyung Hwan Lee, Jun Young Yoo, Chan Jung Park, Kang Ho Ahn

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.

Kyung Hwan LeeDepartment of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gup, Seoul 04763, Republic of Korea.ORCID 0009-0002-7906-9352
Jun Young YooCoway Environmental Technology Research Institute, Coway R&D Center, Seoul National University Research Park 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.ORCID 0000-0001-6427-5319
Chan Jung ParkCoway Environmental Technology Research Institute, Coway R&D Center, Seoul National University Research Park 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
Kang Ho AhnDepartment of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gup, Seoul 04763, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Air purification devices, such as air purifiers, provide fresh air by filtering out airborne pollutants, dust, and other harmful substances using various filter systems. While air filters are generally effective in filtering pollutants such as dust, they encounter a challenge when filtering harmful microorganisms such as mites, bacteria, mold, and viruses. These microorganisms, which are present in public transport and public indoor spaces, tend to proliferate on the surface of the filter media, eventually reintroducing themselves into the air or causing unpleasant odors. To address this issue, herein, copper particles were prepared as one masterbatch and deposited on polypropylene (PP) pellets through plasma vacuum deposition to effectively filter dust and microorganisms and prevent their growth on the surface of the filter media. After adding 3-10 wt.% of the masterbatch to conventional PP pellets to fabricate a filter media, the distribution of copper on the surface of the filter media was observed through a scanning electron microscope. To verify the safety and effectiveness of the antimicrobial material, the filter media containing antimicrobial particles was tested using

Indexed as

air filterantimicrobialcoppermasterbatchmelt-blown process

Identifiers

PMID37513082
PMCPMC10386529

What OpenQuestion holds

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LicenceCC BY
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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.