Evidence map›Paper›PMID 36850127›Full record

ArticlePolymers2023

A Rapid Quantitative Analysis of Bicomponent Fibers Based on Cross-Sectional In-Situ Observation.

Jieyao Qin, Mingxi Lu, Bin Li, Xiaorui Li, Guangming You, Linjian Tan, Yikui Zhai, Meilin Huang, Yingzhu Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 50% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

9 authors at 2 institutions in 1 country.

Jieyao QinSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.ORCID 0000-0002-1146-1510
Mingxi LuSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Bin LiCollege of Innovation and Entrepreneurship, Wuyi University, Jiangmen 529020, China.
Xiaorui LiSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Guangming YouSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Linjian TanSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Yikui ZhaiCollege of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.
Meilin HuangSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Yingzhu WuSchool of Textile Materials and Engineering, Wuyi University, Jiangmen 529020, China.
Wuyi University · CNWuyi University · CN

Funding

Jiangmen Basic and Applied Basic Research Project 2021030103890006796Jieyang Key Scientific and Technological Projects 2022DZX027Wuyi University 2022CX43Wuyi University 2022CY61Wuyi University pdjh2023 b0533
6 · The paper itself

Abstract

To accelerate the industrialization of bicomponent fibers, fiber-based flexible devices, and other technical fibers and to protect the property rights of inventors, it is necessary to develop fast, economical, and easy-to-test methods to provide some guidance for formulating relevant testing standards. A quantitative method based on cross-sectional in-situ observation and image processing was developed in this study. First, the cross-sections of the fibers were rapidly prepared by the non-embedding method. Then, transmission and reflection metallographic microscopes were used for in-situ observation and to capture the cross-section images of fibers. This in-situ observation allows for the rapid identification of the type and spatial distribution structure of the bicomponent fiber. Finally, the mass percentage content of each component was calculated rapidly by AI software according to its density, cross-section area, and total test samples of each component. By comparing the ultra-depth of field microscope, differential scanning calorimetry (DSC), and chemical dissolution method, the quantitative analysis was fast, accurate, economical, simple to operate, energy-saving, and environmentally friendly. This method will be widely used in the intelligent qualitative identification and quantitative analysis of bicomponent fibers, fiber-based flexible devices, and blended textiles.

Indexed as

artificial intelligence (AI)bicomponent fiberscross-sectional in-situ observationimage processingmelting and dissolvingquantitative analysis

Identifiers

PMID36850127
PMCPMC9964497
OpenAlexW4319596964

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.