Issue 17, 2024

Fatigue crack-based strain sensors achieving flow detection and motion monitoring for reconnaissance robot applications

Abstract

Crack-based flexible strain sensors with ultra-high sensitivity under tiny strain are highly desired for environmental perception and motion detection of novel flexible and miniature robots. However, previously reported methods for fabricating crack patterns have often sacrificed the cyclic stability of the sensor, leading to a trade-off relationship between the sensitivity and the cyclic stability. Here, a universal and simple strategy based on fatigue loading with an ultra-large cumulative strain of up to ∼1.2 × 107%, rather than the traditionally quasi-static pre-overloading methods, is proposed to introduce channel cracks in the sensing layer without sacrificing the cyclic stability. The developed flexible strain sensors exhibit high strain-sensitivity (gauge factor = 5798) under tiny strain (< 3%), high cyclic stability (15 000 cycles) and a low strain detecting limit (0.02%). Furthermore, a leaf-like mechanosensor is developed using the fatigue crack-based strain sensor for the realization of multifunctional applications in environment perception and micro-motion detection. Brilliant airflow sensing performance with a wide sensing range (0.93–11.93 m s−1) and a fast response time (0.28 s) for amphibious applications is demonstrated. This work provides a new strategy for overcoming limits of crack-based flexible strain sensors and the developed leaf-like mechanosensor shows great application potential in miniature and flexible reconnaissance robots.

Graphical abstract: Fatigue crack-based strain sensors achieving flow detection and motion monitoring for reconnaissance robot applications

Supplementary files

Article information

Article type
Communication
Submitted
11 Apr 2024
Accepted
18 Jun 2024
First published
19 Jun 2024

Mater. Horiz., 2024,11, 4207-4222

Fatigue crack-based strain sensors achieving flow detection and motion monitoring for reconnaissance robot applications

X. Wu, X. Luo, H. Chen, Y. Man, Y. Bai, T. Qin, B. Zhang and G. Zhang, Mater. Horiz., 2024, 11, 4207 DOI: 10.1039/D4MH00419A

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