Cargando…
Structural effects of 3D printing resolution on the gauge factor of microcrack-based strain gauges for health care monitoring
Measurements of physiological parameters such as pulse rate, voice, and motion for precise health care monitoring requires highly sensitive sensors. Flexible strain gauges are useful sensors that can be used in human health care devices. In this study, we propose a crack-based strain gauge fabricate...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791987/ https://www.ncbi.nlm.nih.gov/pubmed/35136651 http://dx.doi.org/10.1038/s41378-021-00347-x |
Sumario: | Measurements of physiological parameters such as pulse rate, voice, and motion for precise health care monitoring requires highly sensitive sensors. Flexible strain gauges are useful sensors that can be used in human health care devices. In this study, we propose a crack-based strain gauge fabricated by fused deposition modeling (FDM)-based three-dimensional (3D)-printing. The strain gauge combined a 3D-printed thermoplastic polyurethane layer and a platinum layer as the flexible substrate and conductive layer, respectively. Through a layer-by-layer deposition process, self-aligned crack arrays were easily formed along the groove patterns resulting from stress concentration during stretching motions. Strain gauges with a 200-µm printing thickness exhibited the most sensitive performance (~442% increase in gauge factor compared with that of a flat sensor) and the fastest recovery time (~99% decrease in recovery time compared with that of a flat sensor). In addition, 500 cycling tests were conducted to demonstrate the reliability of the sensor. Finally, various applications of the strain gauge as wearable devices used to monitor human health and motion were demonstrated. These results support the facile fabrication of sensitive strain gauges for the development of smart devices by additive manufacturing. |
---|