A Camel Nose-Inspired Highly Durable Neuromorphic Humidity Sensor with Water Source Locating Capability

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.

Automated summary

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.