Evidence map›Paper›PMID 36616867›Full record

ArticleSensors (Basel, Switzerland)2022

Integrated Mechano-Electrochemical Harvesting Fiber and Thermally Responsive Artificial Muscle for Self-Powered Temperature-Strain Dual-Parameter Sensor.

Hyeon Jun Sim, Jun Ho Noh, Jin Hyeong Choi, Changsoon Choi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

4 authors.

Hyeon Jun SimDepartment of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Jun Ho NohDepartment of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Jin Hyeong ChoiDepartment of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Changsoon ChoiDepartment of Energy and Materials Engineering, Dongguk University, Seoul 04620, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant progress in healthcare fields around the world has inspired us to develop a wearable strain−temperature sensor that can monitor biomedical signals in daily life. This novel self-powered temperature−strain dual-parameter sensor comprises a mechano-electrochemical harvester (MEH) and a thermally responsive artificial muscle (TAM). The MEHTAM system generates electricity from strain and thermal fluctuations. In addition, the sensor is comfortable to wear, owing to its stretchability (>100%), softness (<3 MPa), and one-dimensional fibers (diameter 230 μm). The MEH induces a change in the electrochemical capacitance, resulting in an electrical signal under applied strain (34 μA/m) and stress (20 μA/(m·MPa)). The TAM can be used as a mechanical temperature sensor, because the tensile stroke responds linearly to changes in temperature. As the harvester and artificial muscle are combined, the MEHTAM system generates electricity, owing to external and internal mechanical stimuli caused by muscle contractions as a response to temperature changes. The MEHTAM system that we have developed—a self-powered, strain−temperature dual-parameter sensor that is soft, stretchable, and fiber-shaped—is an interesting candidate for the production of comfortable, wearable, dual-parameter sensors.

Indexed as

ElectricityMusclesElectric CapacitanceMuscle ContractionTemperatureartificial musclefibermechano-electrochemical energy harvesterself-poweredsoftstrain–temperature sensorstretchable

Identifiers

PMID36616867
PMCPMC9824410

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.