Evidence map›Paper›PMID 38544122›Full record

ArticleSensors (Basel, Switzerland)2024

Flexible 3D Force Sensor Based on Polymer Nanocomposite for Soft Robotics and Medical Applications.

Ahmed Alotaibi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

1 author.

Ahmed AlotaibiDepartment of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia.ORCID 0000-0001-6237-7151

Funding

The Deanship of Scientific Research at Taif University Grant number 20224
6 · The paper itself

Abstract

The three-dimensional (3D) force sensor has become essential in industrial and medical applications. The existing conventional 3D force sensors quantify the three-direction force components at a point of interest or extended contact area. However, they are typically made of rigid, complex structures and expensive materials, making them hard to implement in different soft or fixable industrial and medical applications. In this work, a new flexible 3D force sensor based on polymer nanocomposite (PNC) sensing elements was proposed and tested for its sensitivity to forces in the 3D space. Multi-walled carbon nanotube/polyvinylidene fluoride (MWCNT/PVDF) sensing element films were fabricated using the spray coating technique. The MWCNTs play an essential role in strain sensitivity in the sensing elements. They have been utilized for internal strain measurements of the fixable 3D force sensor's structure in response to 3D forces. The MWCNT/PVDF was selected for its high sensitivity and capability to measure high and low-frequency forces. Four sensing elements were distributed into a cross-beam structure configuration, the most typically used solid 3D force sensor. Then, the sensing elements were inserted between two silicone rubber layers to enhance the sensor's flexibility. The developed sensor was tested under different static and dynamic loading scenarios and exhibited excellent sensitivity and ability to distinguish between tension and compression force directions. The proposed sensor can be implemented in vast applications, including soft robotics and prostheses' internal forces of patients with limb amputations.

Indexed as

Fluorocarbon PolymersNanocompositesRoboticsHumansPolymersPolyvinylsFluorocarbon PolymersPolymerspolyvinylidene fluoridePolyvinyls3D force sensorflexiblemeasurementspolymer nanocompositestraintactile

Identifiers

PMID38544122
PMCPMC10974536

What OpenQuestion holds

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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.