Evidence map›Paper›PMID 40234571›Full record

ArticleScientific reports2025

Piezoelectric properties improvement in soft membrane with wet-spinning prepared barium titanate/polyvinylidenefluoride composites fiber.

Xiong-Wei Lin, Prasad Gajula, Xiao-Shan Luo, Mingrui Zhao, Xiao-Bo Zhao, Ye Fan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Xiong-Wei LinSchool of Microelectronics, Shenzhen Institute of Information Technology, Shenzhen, 518000, China. linxw_gdut_edu@126.com.
Prasad GajulaGuangdong Provincial Research Center on Smart Materials and Energy Conversion Devices, Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, School of Materials and Energy, Guangdong University of Technology, Guangzhou, 510006, China. gajula@khu.ac.kr.
Xiao-Shan LuoSchool of Materials and Environmental Engineering, Shenzhen Polytechnic University, Shenzhen, China.
Mingrui ZhaoGuangdong Provincial Research Center on Smart Materials and Energy Conversion Devices, Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, School of Materials and Energy, Guangdong University of Technology, Guangzhou, 510006, China.
Xiao-Bo ZhaoGuangdong Provincial Research Center on Smart Materials and Energy Conversion Devices, Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter, School of Materials and Energy, Guangdong University of Technology, Guangzhou, 510006, China.
Ye FanInstitute for Frontier Materials, Deakin University, 75 Pigdons Road, Waurn Ponds, VIC, Melbourne, 3216, Australia. y.fan@deakin.edu.au.

Funding

National Natural Science Foundation of China 51372042National Natural Science Foundation of China-Guangdong Joint Fund U1501246Natural Science Foundation of Guangdong Province 2015A030308044
6 · The paper itself

Abstract

Piezoelectric composite materials have demonstrated significant potential for developing high-performance wearable sensors. However, optimizing the piezoelectric output performance in polymer-based devices remains challenging due to the suboptimal synergy between the piezoelectric reinforcement phase and substrate materials. Moreover, the instability of response signals further hampers the sensor's practical utility. In this investigation, wet-spinning technology was applied to fabricate a novel Barium Titanate (BaTiO

Indexed as

Lattice deformationsPiezoelectricSensitivityWet-Spinning

Identifiers

PMID40234571
PMCPMC12000348

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.