@article{Song2025, 
author = {Yangyang Song and Yucheng Yan and Wanling Wang and Jingxin Wu and Kaibo Yu and Xiaodong Wu and Zhuqing Wang},
title = {Synergistic ionic and electronic transport pathways enabled strain sensors with ultra-high and modulable sensitivity within wide working range},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {12},
pages = {94908146},
keywords = {high sensitivity, physiological signals monitoring, wearable strain sensors, synergistic ion and electron pathways, wide working range},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94908146},
doi = {10.26599/NR.2025.94908146},
abstract = {Wearable and flexible strain sensors play a pivotal role in comprehensive health monitoring and exercise guidance applications. High sensitivity and wide sensing windows are necessary for effectively monitoring and capturing diverse physiological signals, whereas these features are challenging to achieve simultaneously with current sensors. To address this limitation, we developed an innovative sensor modality incorporating synergistic ionic and electronic pathways (SI&EP), enabling both high sensitivity and wide working range. The SI&EP sensor architecture incorporates: (1) a highly conductive wrinkle-crack electronic sensing layer to enhance and adjust sensitivity for detecting subtle physiological signals (e.g., pulse and pronunciation), and (2) a highly stretchable ionic sensing layer to extend the working range for large-scale joint movement monitoring. Through systematically optimizing the structure and conductivity of both layers, the SI&EP sensors simultaneously achieve a high sensitivity of 5805.3 and a wide working range up to 200% strain. This unique combination of high sensitivity and wide working range empowers the SI&EP sensor for comprehensive physiological signal monitoring, scientific exercise guidance, and machine learning-assisted physiological activity recognition. This research presents a new methodology to extend the working range without compromising sensitivity, with potential applications in comprehensive health monitoring and disease rehabilitation training.}
}