Evidence map›Paper›PMID 36106007›Full record

ArticleRSC advances2022

Superhydrophobicity and conductivity of polyester-conductive fabrics using alkaline hydrolysis.

Suhyun Lee

Abstract read
In one paragraph

Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

1 author.

Suhyun LeeDepartment of Fashion Design, Jeonbuk National University Jeonju 54896 Republic of Korea suhyun14@jbnu.ac.kr.ORCID https://orcid.org/0000-0002-1446-4995

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study attempted to develop a superhydrophobic conductive fabric to solve the problem of functional deterioration due to oxidation of conductive fabrics. To this end, a superhydrophobic surface was achieved by introducing nano-roughness to the surface of the polyester-conductive fabric by using alkaline hydrolysis, and then lowering the surface energy through hydrophobic coating. In order to derive optimal processing conditions with excellent superhydrophobicity while maintaining conductivity and mechanical properties, changes in surface structure, conductivity, superhydrophobicity and durability of conductivity against air and water were evaluated according to alkaline hydrolysis duration. As the alkaline hydrolysis duration increased, the polyester surface was etched and the silver oxide particles formed on the conductive yarns, creating non-uniform nano-roughness. The weight, tensile strength, and strain of the conductive fabric decreased due to alkaline hydrolysis, and the change was noticeable after 30 min of alkaline hydrolysis. In addition, as the alkaline hydrolysis duration increased, the surface resistance of the conductive fabric slightly increased and the surface temperature by electric heating performance decreased, but it still showed excellent conductivity. After 30 min of alkaline hydrolysis, a contact angle of more than 150° and a shedding angle of less than 10° achieved a superhydrophobic surface. This superhydrophobic surface prevented the reaction of silver with air or water in spite of prolonged exposure to air and repeated contact with water to maintain electrical properties, thereby improving the durability of conductivity. This study is significant in that it achieved dual roughness for superhydrophobicity on the surface of conductive fabrics using a relatively easy and simple method by applying alkaline hydrolysis, which is commonly used with polyester fabric. Therefore, it is expected to solve the problem of conductivity loss of the use of conductive fabric in various environments.

Identifiers

PMID36106007
PMCPMC9377211

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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.