Evidence map›Paper›PMID 33304755›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020

Ultraelastic Yarns from Curcumin-Assisted ELD toward Wearable Human-Machine Interface Textiles.

Chuang Zhu, Ruohao Li, Xue Chen, Evelyn Chalmers, Xiaoteng Liu, Yuqi Wang, Ben Bin Xu, Xuqing Liu

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  3. Elastic Fibers/Fabrics for Wearables and Bioelectronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. 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

8 authors.

Chuang ZhuDepartment of Materials, School of Natural Sciences University of Manchester Manchester M13 9PL UK.
Ruohao LiSchool of Science, Technology, Engineering and Mathematics University of Washington Bothell WA 98011 USA.
Xue ChenDepartment of Mechanical and Construction Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne NE1 8ST UK.
Evelyn ChalmersDepartment of Materials, School of Natural Sciences University of Manchester Manchester M13 9PL UK.
Xiaoteng LiuDepartment of Mechanical and Construction Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne NE1 8ST UK.
Yuqi WangDepartment of Materials, School of Natural Sciences University of Manchester Manchester M13 9PL UK.
Ben Bin XuDepartment of Mechanical and Construction Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne NE1 8ST UK.
Xuqing LiuDepartment of Materials, School of Natural Sciences University of Manchester Manchester M13 9PL UK.ORCID https://orcid.org/0000-0001-5998-6546

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intelligent human-machine interfaces (HMIs) integrated wearable electronics are essential to promote the Internet of Things (IoT). Herein, a curcumin-assisted electroless deposition technology is developed for the first time to achieve stretchable strain sensing yarns (SSSYs) with high conductivity (0.2 Ω cm

Indexed as

curcuminelectroless depositionhuman–machine interfacestextileswearable electronics

Identifiers

PMID33304755
PMCPMC7709996

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.