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Abstract Despite the extensive developments of flexible capacitive pressure sensors, it is still elusive to simultaneously achieve excellent linearity over a broad pressure range, high sensitivity, and ultrahigh pressure resolution under large pressure preloads. Here, we present a programmable fabrication method for microstructures to integrate an ultrathin ionic layer. The resulting optimized sensor exhibits a sensitivity of 33.7 kPa −1 over a linear range of 1700 kPa, a detection limit of 0.36 Pa, and a pressure resolution of 0.00725% under the pressure of 2000 kPa. Taken together with rapid response/recovery and excellent repeatability, the sensor is applied to subtle pulse detection, interactive robotic hand, and ultrahigh-resolution smart weight scale/chair. The proposed fabrication approaches and design toolkit from this work can also be leveraged to easily tune the pressure sensor performance for varying target applications and open up opportunities to create other iontronic sensors.
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This content will become publicly available on December 1, 2027
Graphene Aerogel-Based Flexible Pressure Sensor for Physiological Signal Detection and Human–Machine Interaction
Abstract Despite extensive development of flexible pressure sensors, it is still difficult for them to simultaneously achieve high precision and a large response to subtle pressures. To address these challenges, this work demonstrates a flexible pressure sensing platform that features the reduced graphene oxide aerogel sandwiched between a polydimethylsiloxane encapsulation layer and a thin polyimide film with interdigital electrodes. The resulting pressure sensor exhibits a high sensitivity of 698.96 kPa−1and a low limit of detection (~ 1 Pa), and outstanding stability over 20,000 loading/unloading cycles. Besides monitoring various physiological signals and human motions, the flexible pressure sensors can be configured into an array layout as a smart artificial electronic skin to recognize the spatial pressure distribution. The flexible pressure sensor can also be integrated with signal processing and wireless communication modules as a teleoperation system for gesture recognition, force feedback control, and kitchen food recognition, highlighting future potential toward smart robotics and human–machine interfaces.
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- PAR ID:
- 10694986
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Nano-Micro Letters
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 2311-6706
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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