Superelastic and Ultra‐Soft MXene/CNF Aerogel@PDMS‐Based Dual‐Modal Pressure Sensor for Complex Stimuli Monitoring
Abstract In the face of complex pressure stimuli, pressure sensor is required to sense the magnitude of static force and sensitive to transient mechanical stimuli. However, an individual sensing mechanism has difficulty meeting practical needs simultaneously. In this work, an MXene/cellulose nanofib...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202502797 |
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| Summary: | Abstract In the face of complex pressure stimuli, pressure sensor is required to sense the magnitude of static force and sensitive to transient mechanical stimuli. However, an individual sensing mechanism has difficulty meeting practical needs simultaneously. In this work, an MXene/cellulose nanofiber (CNF) aerogel@PDMS‐based dual‐modal pressure sensor is reported for complex stimuli monitoring. The aerogel‐based sensing material is fabricated through MXene nanosheets and CNFs. Aerogel ice crystals sublimate and then form a 3D porous structure during vacuum freeze‐drying. After attaching PDMS dilution, aerogels achieve >200 reversible compressions, and hysteresis energy is reduced by 57.8%. By utilizing both triboelectric and piezoresistive properties of MXene/CNF aerogel@PDMS, a dual‐modal pressure sensor is achieved. The triboelectric effect acquires high sensitivity of 26.95 kPa−1 under low pressure (3.46 Pa–3.32 kPa) and responds to vibrations up to 1000 Hz. On the basis of variable resistances of aerogels, the piezoresistive effect can be used to identify static pressures stably (167 kPa−1, 1.56–26.64 kPa). Combining two effects broadens the lower limit of high‐sensitivity monitoring, realizing static‐dynamic detection simultaneously and breaking the frequency limit of piezoresistive materials. Finally, the dual‐modal pressure sensor is demonstrated to monitor complex physiological and physical signals, such as pronunciation, gestures, and tone recognition. |
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| ISSN: | 2198-3844 |