Evidence map›Paper›PMID 41526372›Full record

ArticleScientific reports2026

Customized batch fabrication of highly sensitive thin capacitive soft sensors based on high dielectric constant composite polymers.

Ali Ghanbari, Ahmet Arif Colakoglu, Ahmad Serjouei, Samit Chakrabarty, Ronaldo M Ichiyama, Peter R Culmer, Ali Alazmani

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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
–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

1 citing paper in PubMed.

  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

7 authors.

Ali GhanbariSchool of Engineering, University of Lancashire, Preston, PR1 2HE, UK.
Ahmet Arif ColakogluSchool of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Ahmad SerjoueiDepartment of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK.
Samit ChakrabartySchool of Biomedical Sciences, University of Leeds, Leeds, LS2 9JT, UK.
Ronaldo M IchiyamaSchool of Biomedical Sciences, University of Leeds, Leeds, LS2 9JT, UK.
Peter R CulmerSchool of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Ali AlazmaniSchool of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK. a.alazmani@leeds.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft capacitive sensors are essential for emerging technologies in soft robotics, wearable electronics, and human-machine interfaces. However, scalable, customizable, and cost-effective fabrication methods remain a challenge. In this work, we present a novel batch fabrication approach for producing thin, highly sensitive soft capacitive sensors using high-dielectric-constant composite elastomers. Our method enables the rapid production of large-area sensor sheets that can be precisely patterned into arbitrary geometries using laser cutting, eliminating the need for complex microstructuring or lithography. These sensors are composed entirely of soft layers, including carbon nanofiber-based electrodes and dielectric layers enhanced with barium titanate (BTO) or titanium dioxide (TiO2). We demonstrate a gauge factor of 2.83 and a sensitivity of 0.55 kPa⁻¹, with low hysteresis and broad pressure range. Furthermore, we present the integration of these sensors into a tactile array for wearable pressure sensing, highlighting their potential in soft robotics and health monitoring systems. This work offers a scalable and versatile platform for next-generation soft sensing technologies.

Indexed as

Batch fabricationCapacitive sensorsHigh dielectric constantSoft composite polymersSoft functional materialsSoft sensors

Identifiers

PMID41526372
PMCPMC12800149

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.