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Abstract：
In this study, a self-sorting sensor was developed with the ability to distinguish between different pressure regimes and translate the pressure to electrical signals. Specifically, the self-sorting sensor can distinguish between soft and hard pressure like the human skin, without any software assistance and complicated circuits. To achieve the self-sorting property, Janus-like jagged structures were prepared via an all-solution process of spontaneous chemical patterning; they comprised electrically semi-insulating vertices and highly conductive valleys. This unique structure facilitates the detection and determination of the intensities and types of pressure by providing a significant gap between the current levels of two types of states, similar to the function of fibers in the human tactile system. The fabricated sensors also exhibit high sensitivity and durability as well as low power consumption, as demonstrated by the electronic skin and ternary Morse signal applications. Compared with conventional wearable pressure sensors, this sensor can detect signals without additional programming; thus, it is highly suitable for delay-sensitive, energy-efficient sensor applications such as driverless vehicles, autonomous artificial intelligence technology, and prosthetic devices.
摘要：
在这项研究中,开发了一种自分类传感器，能够区分不同的压力状态并将压力转换为电信号。具体来说,自分类传感器可以像人体皮肤一样区分软硬压力，没有任何软件帮助和复杂的电路。要实现自排序属性，通过自发化学图案化的全溶液过程制备了类似Janus的锯齿状结构；它们包括半绝缘顶点和高导电谷。这种独特的结构通过在两种状态的当前水平之间提供显著的差距，便于检测和确定压力的强度和类型。类似于纤维在人类触觉系统中的功能。制造的传感器还具有高灵敏度和耐用性以及低功耗，如电子皮肤和三元莫尔斯信号应用所证明的。与传统的可穿戴式压力传感器相比,该传感器无需额外编程即可检测信号；因此，它非常适合延迟敏感，节能传感器应用，如无人驾驶车辆，自主人工智能技术,和假肢装置。
