Evidence map›Paper›PMID 42590598›Full record

ArticleSensors (Basel, Switzerland)2026

Double-Layer Graphene Mesh/PEDOT:PSS Conductive-Network-Reinforced PDMS Nanocomposites for Temperature-Insensitive Strain Sensing.

Lei Wang, Zhiqiang Bai, Ruijie Han, Chaoxia Wu, Shengwei Mu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

5 authors.

Lei WangHenan Key Laboratory of Advanced Cable Materials and Intelligent Manufacturing, Henan Institute of Technology, Xinxiang 453003, China.ORCID 0000-0001-8068-9685
Zhiqiang BaiHenan Key Laboratory of Advanced Cable Materials and Intelligent Manufacturing, Henan Institute of Technology, Xinxiang 453003, China.
Ruijie HanHenan Key Laboratory of Advanced Cable Materials and Intelligent Manufacturing, Henan Institute of Technology, Xinxiang 453003, China.
Chaoxia WuHenan Key Laboratory of Advanced Cable Materials and Intelligent Manufacturing, Henan Institute of Technology, Xinxiang 453003, China.
Shengwei MuHenan Key Laboratory of Advanced Cable Materials and Intelligent Manufacturing, Henan Institute of Technology, Xinxiang 453003, China.

Funding

Henan Key Laboratory of Cable Advanced Materials and Intelligent Manufacturing CAMIM202504Natural Science Foundation of He'nan Province 262300420046
6 · The paper itself

Abstract

Conductive polymer composite (CPC)-based wearable electronics and flexible strain sensors work in different environments, which require CPCs to show stable electrical performance at a wide range of temperatures. However, the resistance of most CPCs is generally temperature-dependent. In this work, a double-layer conductive framework was fabricated by coating highly conductive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on a graphene mesh. The resulting composite, PEDOT:PSS/GM/PDMS-0.75, exhibited outstanding conductivity (8.1 S/cm), a high gauge factor (~42), excellent reliability (1000 cycles) and stable sensing performance within -30~140 °C. This work highlights the importance of PEDOT:PSS in improving the conductive stability of graphene-based strain sensors at different temperatures. Moreover, applications of sensing for human joint movement in different environments open new opportunities for temperature-insensitive CPCs.

Indexed as

conductive polymerelectromechanical behaviorgraphene meshtemperature-insensitive strain sensor

Identifiers

PMID42590598
PMCPMC13468704

What OpenQuestion holds

Textmetadata
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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.