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A Self‐Powered Biochemical Sensor for Intelligent Agriculture Enabled by Signal Enhanced Triboelectric Nanogenerator

Authors :
Along Gao
Qitao Zhou
Zhikang Cao
Wenxia Xu
Kang Zhou
Boyou Wang
Jing Pan
Caofeng Pan
Fan Xia
Source :
Advanced Science, Vol 11, Iss 22, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Precise agriculture based on intelligent agriculture plays a significant role in sustainable development. The agricultural Internet of Things (IoTs) is a crucial foundation for intelligent agriculture. However, the development of agricultural IoTs has led to exponential growth in various sensors, posing a major challenge in achieving long‐term stable power supply for these distributed sensors. Introducing a self‐powered active biochemical sensor can help, but current sensors have poor sensitivity and specificity making this application challenging. To overcome this limitation, a triboelectric nanogenerator (TENG)‐based self‐powered active urea sensor which demonstrates high sensitivity and specificity is developed. This device achieves signal enhancement by introducing a volume effect to enhance the utilization of charges through a novel dual‐electrode structure, and improves the specificity of urea detection by utilizing an enzyme‐catalyzed reaction. The device is successfully used to monitor the variation of urea concentration during crop growth with concentrations as low as 4 µm, without being significantly affected by common fertilizers such as potassium chloride or ammonium dihydrogen phosphate. This is the first self‐powered active biochemical sensor capable of highly specific and highly sensitive fertilizer detection, pointing toward a new direction for developing self‐powered active biochemical sensor systems within sustainable development‐oriented agricultural IoTs.

Details

Language :
English
ISSN :
21983844 and 20230982
Volume :
11
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Advanced Science
Publication Type :
Academic Journal
Accession number :
edsdoj.f91274c148274430ace494dad07b36cd
Document Type :
article
Full Text :
https://doi.org/10.1002/advs.202309824