Hangzhou University of Electronic Science and Technology: Inspired by Scorpion Legs! High performance flexible strain sensor for human motion monitoring and wearable devices

Wearable flexible strain sensors based on conductive thin films have shown great potential for application in many high-tech fields. They have high requirements for the synergistic performance of sensitivity, flexibility, stability, and durability. However, overcoming the trade-off between high sensitivity and high stretchability remains a significant challenge for this research. In this article, a team led by Assistant Researcher Lu Chenxi and Associate Professor Hu Liang from Hangzhou University of Electronic Science and Technology published a paper titled "Ultrasensitive, Highly Stretchable, and Multifunctional Strain Sensors Based on Scorpion Leg Inspired Gradient Crack Arrays" in the Chemical Engineering Journal. Inspired by the gradient cracks on scorpion legs, a simple preparation strategy for high-performance flexible strain sensors based on gradient crack arrays was developed.In the periodic thickness gradient metal film prepared by one-step masking technology on a flexible substrate, a gradient crack array with branching or layering characteristics is controllably formed. Under the action of mechanical strain, cracks gradually open from the thickest thin film area to the thinnest thin film area, forming a unique sensing mechanism.

 Therefore, strain sensors based on gradient cracks have excellent comprehensive performance, including high sensitivity (up to 21000), wide sensing range (about 70%), low detection limit (0.02%), fast response speed (less than 80 milliseconds), excellent stability and durability (can be cycled 15000 times under 5% strain). As an application demonstration, this sensor can be used to monitor various human activities and express and encrypt information through bidirectional bending. This work provides a new method for manufacturing highly sensitive, stretchable, durable, multifunctional, crack based flexible strain sensors, which will greatly expand the application fields of flexible electronic products and wearable devices.

Inspired by the gradient cracks on scorpion legs, we have successfully developed a high-performance flexible strain sensor based on gradient crack arrays, and conducted detailed research on its sensing characteristics and application scenarios. By using a combination of masking and sputtering techniques, periodic thickness gradient metal films were spontaneously formed on flexible PDMS substrates, resulting in a unique array of cracks with gradient and hierarchical features. The geometric shape of cracks is closely related to the thickness of the film and the applied strain, which is highly consistent with the predictions of material fracture theory. Under the action of mechanical strain, gradient characteristics will induce cracks to gradually open from the thickest film region to the thinnest film region, resulting in a slow and gradual increase in resistance.

In this study, a unique sensing mechanism ensured the optimal combination of high sensitivity (up to 21000) and wide sensing range (~70%), surpassing most previously reported crack based sensors. In addition, strain sensors based on gradient cracks have the characteristics of low detection limit (0.02%), fast response speed (80 milliseconds response time and 67 milliseconds recovery time), good stability, and strong durability (can be cycled 15000 times under 5% strain). High performance strain sensors are suitable for monitoring various human activities, including joint bending, pulse pulsation, and swallowing. The combination of three bidirectional bendable sensors has also successfully achieved information expression and encryption. This work provides a new approach for crack structure design by constructing heterogeneous thin film systems. A flexible strain sensor with excellent comprehensive performance will be beneficial for multiple application fields such as electronic skin, wearable devices, human-machine interfaces, and soft robots.

Source: Sensor Expert Network