A Natural Gradient Biological-Enabled Multimodal Triboelectric Nanogenerator for Driving Safety Monitoring

A key element to ensuring driving safety is to provide a sufficient braking distance. Inspired by the nature triply periodic minimal surface (TPMS), a gradient and multimodal triboelectric nanogenerator (GM-TENG) is proposed with high sensitivity and excellent multimodal monitoring. The gradient TPM...

Full description

Saved in:
Bibliographic Details
Published inACS nano Vol. 17; no. 21; pp. 21878 - 21892
Main Authors Pang, Yafeng, Zhu, Xingyi, Liu, Shuainian, Lee, Chengkuo
Format Journal Article
LanguageEnglish
Published American Chemical Society 14.11.2023
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:A key element to ensuring driving safety is to provide a sufficient braking distance. Inspired by the nature triply periodic minimal surface (TPMS), a gradient and multimodal triboelectric nanogenerator (GM-TENG) is proposed with high sensitivity and excellent multimodal monitoring. The gradient TPMS structure exhibits the multi-stage stress-strain properties of typical porous metamaterials. Significantly, the multimodal monitoring capability depends on the implicit function of the defined level constant c, which directly contributes to the multimodal driving safety monitoring. The mechanical and electrical responsive behavior of the GM-TENG is analyzed to identify the applied speed, load, and working mode. In addition, optimized peak open-circuit voltage (V oc) is demonstrated for self-awareness of the braking condition. The braking distance factor (L) is conceived to construct the self-aware equation of the friction coefficient based on the integration of V oc with respect to time. Importantly, R-squared up to 94.29 % can be obtained, which improves self-aware accuracy and real-time capabilities. This natural structure and self-aware device provide an effective strategy to improve driving safety, which contributes to the improvement of road safety and presents self-powered sensing with potential applications in an intelligent transportation system.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1936-0851
1936-086X
DOI:10.1021/acsnano.3c08102