Evidence map›Paper›PMID 41723174›Full record

ArticleScientific reports2026

Investigation of direct welding of organza textile and PET mesh for designing free-form lighting.

Eunhyeok Bae, Hyeonwoo Jeon, Hoyeon Chung, Changkyoo Park

Abstract read
In one paragraph

Article in Scientific reports, 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

4 authors.

Eunhyeok Bae *Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
Hyeonwoo Jeon *Department of Metal Art and Design, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
Hoyeon ChungDepartment of Metal Art and Design, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea. hoyeon1024@seoultech.ac.kr.
Changkyoo ParkDepartment of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea. ck0421@seoultech.ac.kr.

Funding

Ministry of Trade, Industry and Energy RS-2024-00441861
6 · The paper itself

Abstract

Organza textiles exhibit a wide variety of colors, excellent flexibility, and good light transmittance. Accordingly, polyethylene terephthalate (PET) organza textile is a promising candidate for use as a lighting material with diverse and intricate geometries. However, organza cannot be used alone owing to its extremely low stiffness as a lighting material. In this study, a PET mesh was employed to enhance the stiffness of organza textiles. Organza textiles and PET mesh were directly welded by thermal welding at different welding temperatures of 320, 375, 425, and 480 °C. A tensile shear test of the organza-PET mesh lap joint was conducted to evaluate the joint strength. Stronger joint strengths were achieved with increasing welding temperature, as severe entanglement developed between the organza textile and the PET mesh at higher welding temperatures. Transmittance and color combination tests were performed with varying numbers and colors of organza textiles. The transmittance decreased as the number of organza textiles increased. Moreover, multiple new colors were generated by combining two different organza textiles. These results demonstrate that the organza-PET mesh joint can serve as a lighting material with free-form geometry, owing to its good mechanical strength, controllable transmittance, and broad color spectrum.

Indexed as

Color combinationDirect weldingPET meshPET organza textileTransmittance

Identifiers

PMID41723174
PMCPMC13022390

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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.