期刊
ACS NANO
卷 16, 期 1, 页码 1511-1522出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsnano.1c10004
关键词
intelligent humidity sensors; high sensitivity and discriminability; excellent durability; artificial synaptic behaviors; water source-locating capability
类别
资金
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China [2021ZR115]
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences [E055AJ01]
- Natural Science Foundation of Fujian Province [E131AJ0101]
- National Natural Science foundation of China [51803214]
- Recruitment Program of Global Youth Experts
The study introduces a porous zwitterionic capacitive humidity sensor with high sensitivity and artificial intelligence features. It demonstrates applications in various fields such as differentiating leaf conditions, high-resolution touchless human-machine interaction, and real-time monitoring of industrial exhaust humidity levels.
Numerous emerging applications in modern society require humidity sensors that are not only sensitive and specific but also durable and intelligent. However, conventional humidity sensors do not have all of these simultaneously because they require very different or even contradictory design principles. Here, inspired by camel noses, we develop a porous zwitterionic capacitive humidity sensor. Relying on the synergistic effect of a porous structure and good chemical and thermal stabilities of hygroscopic zwitterions, this sensor simultaneously exhibits high sensitivity, discriminability, excellent durability, and, in particular, the highest respond speed among reported capacitive humidity sensors, with demonstrated applications in the fast discrimination between fresh, stale, and dry leaves, high-resolution touchless human-machine interactive input devices, and the real-time monitoring humidity level of a hot industrial exhaust. More importantly, this sensor exhibits typical synapse behaviors such as paired-pulse facilitation due to the strong binding interactions between water and zwitterions. This leads to learning and forgetting features with a tunable memory, thus giving the sensor artificial intelligence and enabling the location of water sources. This work offers a general design principle expected to be applied to develop other high-performance biochemical sensors and the nextgeneration intelligent sensors with much broader applications.
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