A Highly Skin‐Conformal and Biodegradable Graphene‐Based Strain Sensor
For wearable sensors, realizing a strong and conformal contact with skin is essential to precisely detect human vital signals and enable human–machine interaction. Herein, a facile strategy is reported to fabricate a sensor with ultrahigh skin conformity by adopting a commercial make‐up accessory (n...
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Veröffentlicht in: | Small methods 2018-10, Vol.2 (10), p.n/a |
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Sprache: | eng |
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Zusammenfassung: | For wearable sensors, realizing a strong and conformal contact with skin is essential to precisely detect human vital signals and enable human–machine interaction. Herein, a facile strategy is reported to fabricate a sensor with ultrahigh skin conformity by adopting a commercial make‐up accessory (nose film) as the substrate and graphene as the active materials. The wearable strain sensor can be pasted on human skin at the dermatoglyph level, and the commercial make‐up shows a high skin adhesion with a peel‐off strength of ≈29.4 N m−1. The high adhesion as well as ultraconformity to skin results in a signal amplification of ≈14.2 times. Also, the sensor shows a similar stretchability (≈35%) to human skin (≈30%) in addition to maintaining an impressive performance with a high sensitivity and rapid response time. These characteristics are attractive for precisely monitoring human vital signals and in appliances with increased human–machine interaction. Besides, the sensor is biodegradable, being made from a nontoxic water‐soluble polymer. It is most likely that biodegradable materials will be increasingly attractive due to the potential environmental problem caused by traditional nonbiodegradable polymers.
A graphene‐based wearable strain sensor is fabricated by a novel strategy using a make‐up additive (liquid nose film) as a substrate to achieve ultraconformal contact with skin. The sensor is biodegradable, in being soluble in water. These characteristics together make the sensor attractive for monitoring human vital signals and in appliances with increased human–machine interaction. |
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ISSN: | 2366-9608 2366-9608 |
DOI: | 10.1002/smtd.201700374 |