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Ultralow Quiescent Power-Consumption Wake-Up Technology Based on the Bionic Triboelectric Nanogenerator.
Advanced Science ( IF 14.3 ) Pub Date : 2020-05-11 , DOI: 10.1002/advs.202000254
Chenxi Zhang 1 , Keren Dai 2 , Di Liu 3 , Fang Yi 4 , Xiaofeng Wang 1, 5, 6 , Lianqing Zhu 7 , Zheng You 1, 5, 6
Affiliation  

Wake‐up circuits in smart microsystems make huge contributions to energy conservation of electronic networks in unmanned areas, which still require higher pressure‐triggering sensitivity and lower power consumption. In this work, a bionic triboelectric nanogenerator (bTENG) is developed to serve as a self‐powered motion sensor in the wake‐up circuit, which captures slight mechanical disturbances and overcomes the drawback of conventional self‐powered motion sensors in the wake‐up circuit that the circuit can only be triggered when a considerable pressure is applied on the sensor. The bTENG mimics the structure of plants and the addition of the leaf‐shaped tentacle structures can increase the electrical outputs by four times, which largely extends the detection range of the wake‐up circuit. The bTENG can detect both noncontact and contact mechanical disturbances; and voltages generated from both situations can trigger the wake‐up system. Moreover, the specially designed circuit that is compatible with the bTENG can help more accurately control the wake‐up system and prolong the battery life of the electronic networks to 12.4 times. An intrusion detection system is established in the wake‐up circuit to distinguish human motion and judge the scene. This work opens new horizons for wake‐up technologies, and provides new routes for persistent sensing.

中文翻译:


基于仿生摩擦纳米发电机的超低静态功耗唤醒技术。



智能微系统中的唤醒电路对无人区域电子网络的节能做出了巨大贡献,但无人区域仍然需要更高的压力触发灵敏度和更低的功耗。在这项工作中,开发了一种仿生摩擦纳米发电机(bTENG)作为唤醒电路中的自供电运动传感器,它可以捕获轻微的机械扰动并克服传统自供电运动传感器在唤醒电路中的缺点电路只有在传感器上施加相当大的压力时才能触发电路。 bTENG模仿植物的结构,叶状触手结构的加入可以将电输出增加四倍,这很大程度上扩展了唤醒电路的检测范围。 bTENG可以检测非接触式和接触式机械扰动;这两种情况产生的电压都可以触发唤醒系统。此外,专门设计的与bTENG兼容的电路可以帮助更精确地控制唤醒系统,并将电子网络的电池寿命延长至12.4倍。唤醒电路中建立了入侵检测系统,用于区分人体运动并判断场景。这项工作为唤醒技术开辟了新视野,并为持久传感提供了新途径。
更新日期:2020-06-24
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