Gou Guang-Yang, Li Xiao-Shi, Jian Jin-Ming, Tian He, Wu Fan, Ren Jie, Geng Xiang-Shun, Xu Jian-Dong, Qiao Yan-Cong, Yan Zhao-Yi, Dun Guanhua, Ahn Chi Won, Yang Yi, Ren Tian-Ling
Institute of Microelectronics, Tsinghua University, Beijing 100084, China.
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
Sci Adv. 2022 Apr;8(13):eabn2156. doi: 10.1126/sciadv.abn2156. Epub 2022 Mar 30.
We report an artificial eardrum using an acoustic sensor based on two-dimensional MXene (TiCT), which mimics the function of a human eardrum for realizing voice detection and recognition. Using MXene with a large interlayer distance and micropyramid polydimethylsiloxane arrays can enable a two-stage amplification of pressure and acoustic sensing. The MXene artificial eardrum shows an extremely high sensitivity of 62 kPa and a very low detection limit of 0.1 Pa. Notably, benefiting from the ultrasensitive MXene eardrum, the machine-learning algorithm for real-time voice classification can be realized with high accuracy. The 280 voice signals are successfully classified for seven categories, and a high accuracy of 96.4 and 95% can be achieved by the training dataset and the test dataset, respectively. The current results indicate that the MXene artificial intelligent eardrum shows great potential for applications in wearable acoustical health care devices.
我们报道了一种基于二维MXene（TiCT）声学传感器的人工耳膜，它模仿人类耳膜的功能以实现语音检测和识别。使用具有大层间距的MXene和微金字塔聚二甲基硅氧烷阵列能够实现压力和声学传感的两阶段放大。MXene人工耳膜显示出62 kPa的极高灵敏度和0.1 Pa的极低检测限。值得注意的是，受益于超灵敏的MXene耳膜，可高精度地实现用于实时语音分类的机器学习算法。280个语音信号成功分为七类，训练数据集和测试数据集分别可实现96.4%和95%的高精度。当前结果表明，MXene人工智能耳膜在可穿戴声学医疗保健设备应用中显示出巨大潜力。
