A crosslinked eutectogel for ultrasensitive pressure and temperature monitoring from nostril airflow

Tao Liu, Qinan Wu, Huansheng Liu, Xiyang Zhao, Xin Yi, Jing Liu, Zhenzhen Nong, Bingpu Zhou, Qingwen Wang and Zhenzhen Liu ()
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Tao Liu: Guangzhou Medical University
Qinan Wu: South China Agricultural University
Huansheng Liu: Guangzhou Medical University
Xiyang Zhao: Guangzhou Medical University
Xin Yi: South China Agricultural University
Jing Liu: South China Agricultural University
Zhenzhen Nong: South China Agricultural University
Bingpu Zhou: University of Macau
Qingwen Wang: South China Agricultural University
Zhenzhen Liu: Guangzhou Medical University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Accurate detection of nostril airflow is vital for real-time respiratory monitoring. However, the developed methods only rely on single stimulus sensing for nostril airflow, which is extremely susceptible to interference in the complex environment, and severely affects the accuracy of detection results. Here, a multimodal integrated eutectogel sensor is explored to simultaneously sense the pressure and temperature stimuli of nostril airflow, by independently outputting capacitance and resistance, respectively, without cross-coupling. The completely physical crosslinking and the synergistic interaction of hydroxyapatite and tannic acid within the network endow this eutectogel with extremely low modulus, remarkable self-healing efficiency, robust adhesion, good environmental stability, and bio-compatibility. A multimodal sensor is developed by integrating this synthetic eutectogel with circuit design, which exhibits superior pressure sensitivity compared to other reported gel-based sensors. As a proof of concept, this sensor is further explored to diagnose the traditional respiratory disease of obstructive sleep apnea syndrome by simultaneously detecting five kinds of stimuli in the sleeping process, greatly improving the accuracy and reliability of the detection results. This work provides a highly effective strategy for achieving ultrasensitive respiratory monitoring and forecasting respiratory diseases.

Date: 2025
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DOI: 10.1038/s41467-025-58631-7

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