Evidence map›Paper›PMID 39924556›Full record

ArticleScientific reports2025

Real-time detection of loosening torque in bolted joints using piezoresistive pressure-sensitive layer based on multi-walled carbon nanotubes reinforced epoxy nanocomposites.

Abdulkadir Sanli, Bilgehan Demirkale, Olfa Kanoun

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

3 authors.

Abdulkadir SanliChair for Electrical Measurements and Sensor Technology, Technische Universität Chemnitz, Reichenhainer Str. 70, 09126, Chemnitz, Germany. a.sanli@imperial.ac.uk.
Bilgehan DemirkaleChair for Assembly and Handling Technology, Technische Universität Chemnitz, Reichenhainer Str. 70, 09126, Chemnitz, Germany.
Olfa KanounChair for Electrical Measurements and Sensor Technology, Technische Universität Chemnitz, Reichenhainer Str. 70, 09126, Chemnitz, Germany.

Funding

Ministry of National Education of Turkey 1416
6 · The paper itself

Abstract

Bolted connections are extensively used in construction and machine design, playing critical roles in various industrial applications. Bolts and screws are, however, prone to loosening or separating due to factors such as shock, vibration, and temperature fluctuations. This self-loosening phenomenon poses a considerable challenge to the reliability of bolted connections, necessitating regular inspections to ensure their safety. In this study, we report a cost-effective technique for monitoring bolted joint loosening torque: BoltWISE (Bolt loosening detection with innovative sensors). BoltWISE employs an innovative sensor element consisting of a piezoresistive, pressure-sensitive layer made from multi-walled carbon nanotube/epoxy nanocomposites coated onto an FR4 substrate, functioning as a washer. Our approach provides high sensitivity, durability, linearity, and fast response times, with minimal hysteresis during both the tightening and loosening processes. Finite-element method simulations were conducted to determine the optimal sensor positions, ensuring fast response during bolt tightening and loosening. Our findings highlight BoltWISE as a promising, low-cost solution for efficiently detecting bolt loosening in industrial environments. Its ease of implementation and fabrication make it a valuable tool for improving safety and maintenance practices across various industries.

Indexed as

Bolt-looseningEpoxy resinMulti-walled carbon nanotubesPiezoresistivityPressure sensingSmart washerStructural health monitoring

Identifiers

PMID39924556
PMCPMC11808053

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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.