Evidence map›Paper›PMID 41374287›Full record

ArticleMaterials (Basel, Switzerland)2025

Study on the Structure and Antioxidant Properties of Seamless Knitted Fabrics with Antioxidant Fibers.

Lei Yan, Lu Chang, Shuhan Shen, Zimin Jin, Mingtao Zhao

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. 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

5 authors.

Lei YanCollege of Textile Science and Engineering (International Silk College), Zhejiang Sci-Tech University, Hangzhou 310018, China.
Lu ChangShanghai Yijin Testing Technology Co., Ltd., Shanghai 200001, China.
Shuhan ShenCollege of Textile Science and Engineering (International Silk College), Zhejiang Sci-Tech University, Hangzhou 310018, China.
Zimin JinCollege of Textile Science and Engineering (International Silk College), Zhejiang Sci-Tech University, Hangzhou 310018, China.ORCID 0000-0003-2882-8267
Mingtao ZhaoZhejiang Bangjie Holding Group Co., Ltd., Yiwu 322000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The antioxidant properties of seamless knitted fabrics with antioxidant fibers determine their antioxidant effects. In this paper, we used five kinds of raw yarn materials, namely mint nylon filament, tea carbon nylon filament, coffee carbon nylon filament, collagen nylon filament, and nylon filament, and chose three kinds of fabric microstructure, namely weft flat knit, 1+1 false rib, and 1+3 false rib, to obtain 15 seamless knitted samples with antioxidant fiber by establishing the sample scheme through the full factorial experimental method and weaving them on the seamless loom. DPPH and ABTS free radical scavenging tests were performed on the 15 seamless knitted samples of antioxidant fiber according to the standards, and the results showed that, in terms of the type of yarns, the antioxidant performance of tea carbon nylon filament was the best, followed by coffee carbon nylon filament and mint nylon filament, and the antioxidant performances of collagen nylon and ordinary nylon yarn were relatively weak; in terms of the fabric structure, the 1+1 false rib structure was slightly better than the weft flat knit structure, while the 1+3 false rib structure was relatively poor. Overall, the antioxidant performance of sample No. 5, with the 1+1 false rib structure and tea carbon nylon thread, was the best.

Indexed as

antioxidantantioxidant fiberfree radicalsseamless knitting

Identifiers

PMID41374287
PMCPMC12692856

What OpenQuestion holds

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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.