I. Research Introduction

Recently, the MEMS team at the School of Mechanical Engineering, Sichuan University, made significant progress in the field of flexible wearable sensor devices. They proposed a strain sensor based on a synergistic ionic-electronic transport channel mechanism, which effectively resolves the trade-off between sensitivity and sensing range by leveraging impedance mismatch between the electronic sensing layer and the ionic sensing layer, along with a unique sensing mechanism. This achievement simultaneously achieves ultra-high sensitivity and a wide sensing range. The research was published in the internationally renowned journal *Nano Research* (Sichuan University B-series, IF=9.0) under the title "Synergistic ionic and electronic transport pathways enabled strain sensors with ultra-high and modulable sensitivity within wide working range." The first author of the paper is doctoral student Song Yangyang (Class of 2022), while the corresponding authors are Professor Wang Zhuqing and Researcher Wu Xiaodong from Sichuan University.

II. Research Background

Wearable flexible strain sensors play a critical role in health monitoring, exercise guidance, and human-machine interaction. Sensitivity and sensing range are two key parameters of strain sensors. High sensitivity is beneficial for capturing subtle physiological signals such as pulse and vocalization, while a wide sensing range is used to monitor large joint movements. However, traditional strain sensors have long faced the bottleneck of being unable to balance "sensitivity-sensing range." Traditional electronic sensors rely on electrons as signal carriers, and microcracks form in the sensing layer under stretching, achieving high sensitivity. However, at large strains, the conductive pathways completely fracture, resulting in a narrow sensing range. Emerging ionic sensors (e.g., hydrogel and ionic gel sensors) depend on ionic transport mechanisms, offering high stretchability and a broad operational range but with extremely low sensitivity (GF typically below 4), limiting their ability to detect minute deformations.

III. Research Approach

To address the aforementioned challenges, Professor Wang Zhuqing and researcher Wu Xiaodong from the School of Mechanical Engineering at Sichuan University successfully developed a strain sensor based on Synergistic Ionic and Electronic Pathways (SI&EP). This sensor integrates two distinct sensing mechanisms: the electronic sensing layer employs a wrinkled-crack structure, achieving high sensitivity through crack propagation under low-strain conditions, enabling precise detection of subtle physiological signals; while the ion sensing layer (PVC/DBA/[EMIM][TFSI]) boasts a stretchability of up to 200% due to its internally stable physicochemical network, making it suitable for large-scale deformation monitoring.  

 Due to the significant impedance difference between the two sensing layers (the electronic layer's impedance is much lower than that of the ion layer) and their unique sensing mechanisms, the sensor primarily relies on the electronic layer for high-sensitivity responses under low strain. When large strain causes the electronic pathway to fracture, the ion layer seamlessly takes over signal conduction, thereby greatly expanding the sensing range.  

 The SI&EP strain sensor effectively combines the performance advantages of both layers, achieving breakthroughs in both "ultra-high sensitivity" and "extremely wide working range." Experiments demonstrate that the sensor exhibits a strain factor (GF) of 211.8 in the 0–20% strain range and surges to 5805.3 in the 20–24.3% strain range, while its sensing range extends up to 200%.

Based on the aforementioned performance, this sensor can achieve comprehensive monitoring ranging from weak physiological signals to large-amplitude joint movements, including subtle characteristics of wrist pulse (such as diastolic wave D, tidal wave T, and knock wave P), vocal vibrations, respiratory changes, as well as large-angle activities like elbow and knee movements. Additionally, the SI&EP sensor can accurately detect the frequency and intensity of movements and monitor changes in key physiological indicators (e.g., heart rate and respiratory rate) before and after exercise, providing significant guidance for daily workouts and professional athlete training. The research team further integrated a one-dimensional convolutional neural network (1D-CNN) to achieve high-precision recognition of 14 different vocal and handwritten gestures, achieving an accuracy rate of 94.6%, demonstrating the sensor's potential applications in human-computer interaction and intelligent sensing.

Source: Sensor Expert Network