Evidence map›Paper›PMID 37374691›Full record

ArticleMicromachines2023

Laser-Formed Sensors with Electrically Conductive MWCNT Networks for Gesture Recognition Applications.

Natalia A Nikitina, Dmitry I Ryabkin, Victoria V Suchkova, Artem V Kuksin, Evgeny S Pyankov, Levan P Ichkitidze, Aleksey V Maksimkin, Evgeny P Kitsyuk, Ekaterina A Gerasimenko, Dmitry V Telyshev and 3 more

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Research on Flexible Sensors for Wearable Devices: A Review.Nanomaterials (Basel, Switzerland) · 2025
    Review
  4. Article
  5. 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

13 authors.

Natalia A NikitinaInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0003-1166-3670
Dmitry I RyabkinInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.
Victoria V SuchkovaInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0002-3207-7622
Artem V KuksinInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0001-8406-9977
Evgeny S PyankovInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.
Levan P IchkitidzeInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.
Aleksey V MaksimkinInstitute for Bionic Technologies and Engineering, I.M. Sechenov First Moscow State Medical University, Bolshaya Pirogovskaya Street 2-4, 119991 Moscow, Russia.ORCID 0000-0002-8027-8453
Evgeny P KitsyukScientific-Manufacturing Complex "Technological Centre", Shokin Square 1, bld. 7 off. 7237, 124498 Moscow, Russia.ORCID 0000-0002-4166-8408
Ekaterina A GerasimenkoInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.
Dmitry V TelyshevInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0002-4221-9882
Ivan BobrinetskiyCenter for Probe Microscopy and Nanotechnology, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0003-2380-2594
Sergey V SelishchevInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.
Alexander Yu GerasimenkoInstitute of Biomedical Systems, National Research University of Electronic Technology, 124498 Moscow, Russia.ORCID 0000-0001-6514-2411

Funding

075-03- 2020-216 22-75-00089
6 · The paper itself

Abstract

Currently, an urgent need in the field of wearable electronics is the development of flexible sensors that can be attached to the human body to monitor various physiological indicators and movements. In this work, we propose a method for forming an electrically conductive network of multi-walled carbon nanotubes (MWCNT) in a matrix of silicone elastomer to make stretchable sensors sensitive to mechanical strain. The electrical conductivity and sensitivity characteristics of the sensor were improved by using laser exposure, through the effect of forming strong carbon nanotube (CNT) networks. The initial electrical resistance of the sensors obtained using laser technology was ~3 kOhm (in the absence of deformation) at a low concentration of nanotubes of 3 wt% in composition. For comparison, in a similar manufacturing process, but without laser exposure, the active material had significantly higher values of electrical resistance, which was ~19 kOhm in this case. The laser-fabricated sensors have a high tensile sensitivity (gauge factor ~10), linearity of >0.97, a low hysteresis of 2.4%, tensile strength of 963 kPa, and a fast strain response of 1 ms. The low Young's modulus values of ~47 kPa and the high electrical and sensitivity characteristics of the sensors made it possible to fabricate a smart gesture recognition sensor system based on them, with a recognition accuracy of ~94%. Data reading and visualization were performed using the developed electronic unit based on the ATXMEGA8E5-AU microcontroller and software. The obtained results open great prospects for the application of flexible CNT sensors in intelligent wearable devices (IWDs) for medical and industrial applications.

Indexed as

carbon nanotubeselectrical conductive networksflexible electronicsgesture recognitionlaser structuringsensor glovesmart wearable electronicsstrain sensors

Identifiers

PMID37374691
PMCPMC10304869

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.