Evidence map›Paper›PMID 34811631›Full record

ArticleJournal of fluorescence2022

Polyester Fabric with Fluorescent Properties Using Microwave Technology for Anti-Counterfeiting Applications.

Fredj Saad, Ayda Baffoun, Boris Mahltig, Mohamed Hamdaoui

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of fluorescence, 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
2.3field-weighted citation impact, top 13% of its field
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, 21 citations in OpenAlex.

  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

4 authors at 2 institutions in 2 countries.

Fredj SaadTextile Materials and Processes Research Unit, National School of Engineering of Monastir, University of Monastir, Monastir, Tunisia. fredj93.saad@gmail.com.ORCID http://orcid.org/0000-0001-6112-2616
Ayda BaffounTextile Materials and Processes Research Unit, National School of Engineering of Monastir, University of Monastir, Monastir, Tunisia.
Boris MahltigFaculty of Textile and Clothing Technology, Niederrhein University of Applied Sciences, Monchengladbach, Germany.
Mohamed HamdaouiTextile Materials and Processes Research Unit, National School of Engineering of Monastir, University of Monastir, Monastir, Tunisia.
University of Monastir · TNHochschule Niederrhein · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The article presented concerns the application of fluorescein as a fluorescent material for anti-counterfeiting technology which will allow the labeling and identification of legitimate articles in the textile field. Fluorescein has been applied to polyester fabrics by microwave irradiation technique in the presence of a UV absorber. Thus, its presence in the textile substrate is detectable following an excitation at a specific wavelength belonging to the Ultra-violet zone, which makes this material very effective for tracking and detecting counterfeit articles. Fluorescent samples are characterized morphologically by scanning electron microscopy (SEM) and quantitatively by optical spectroscopy such as reflectance and transmission measurements. The treated samples show under UV light a yellowish green emission with a slight yellow coloration of the polyester fiber. The UV absorber applied to the fluorescent solution improves the light resistance of the treated samples by 25%. Their addition to the bath can also ensure the production of a protective fabric against UV.

Indexed as

FluoresceinFluorescenceMicrowave techniquePolyester fabricUV absorber

Identifiers

PMID34811631
OpenAlexW3192415510

What OpenQuestion holds

Textmetadata
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.