Using smart photochromic and luminescent tissues in camouflage/cloaking of natural creatures has inspired efforts to develop synthetic stimuli-responsive materials for data encryption and anticounterfeiting. Although many optical data-encryption materials have been reported, they generally require only one or a simple combination of few stimuli for decryptions and rarely offer output corruptibility that prevents trial-and-error attacks. Here, we report a series of multiresponsive donor-acceptor Stenhouse adducts (DASAs) with unprecedented switching behavior and controlled reversibility via diamine conformational locking and substrate free-volume engineering and their capability of sequential logic encryption (SLE). Being analogous to the digital circuits, the output of DASA gel–based data-encryption system depends not only on the present input stimulus but also on the sequence of past inputs. Incorrect inputs/sequences generate substantial fake information and lead attackers to the point of no return. This work offers new design concepts for advanced data-encryption materials that operate via SLE, paving the path toward advanced encryptions beyond digital circuit approaches.
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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