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Electronic nose system based on a functionalized capacitive micromachined ultrasonic transducer (CMUT) array for selective detection of plant volatiles

机译:基于官能化电容微机械超声换能器(CMUT)阵列的电子鼻系统,用于选择性检测植物挥发物

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摘要

Here, a small, low-power, wireless gas sensor platform for selective detection of volatile organic compounds (VOCs) released from plants under different abiotic or biotic stress conditions is described. This sensor platform is implemented based on a capacitive micromachined ultrasonic transducer (CMUT) array, in which elements were functionalized with a variety of materials including polymers, phthalocyanines, and metals to improve selectivity. Input impedance measurements of the functionalized CMUT array were compared to pre-coating measurements to analyze the mechanical loading. The CMUT arrays were then exposed to VOCs known to be emitted by plants with different concentrations under dry air flow at room temperature. The results demonstrated that 1-Octanol created the strongest response across different channels and a resolution of 3-ppb was calculated for the CMUT element functionalized using silver ink when exposed to 1-Octanol. The relative responses of different channels to tested volatiles were observed to be different. The k-nearest neighbor (k-NN) algorithm was used for the gas classification by dividing the data to training and test groups. The k-NN results showed that the gases at low concentrations were successfully classified with better than 97 % accuracy. Finally, to emulate the ambient atmosphere for plants, the gas tests were repeated by adding different levels of humidity to the gas flow. With a minimum 98 % accuracy, the k-NN classifier demonstrated that the functionalized CMUT array can be used for selective detection of the group of plant VOCs used in this study, even at different relative humidity levels in the ambient atmosphere.
机译:这里,描述了一种用于在不同的非生物或生物应力条件下从植物中释放的挥发性有机化合物(VOC)进行选择性检测的小,低功率的无线气体传感器平台。该传感器平台基于电容式微机械超声换能器(CMUT)阵列来实现,其中用多种材料具有各种材料,包括聚合物,酞菁和金属以改善选择性。将官能化CMUT阵列的输入阻抗测量与预涂测量进行比较,以分析机械负载。然后将CMUT阵列暴露于已知的VOC,以在室温下在干燥空气流下具有不同浓度的植物发射的VOC。结果表明,1-辛醇在暴露于1-辛醇时,计算使用银墨水官能化的CMUT元素计算出不同通道的最强响应,并计算使用银墨水的CMUT元件。观察到不同通道对测试挥发物的相对反应是不同的。通过将数据除以培训和测试组来用于气体分类的K最近邻居(K-NN)算法。 K-NN结果表明,低浓度下的气体成功分类,精度优于97%。最后,为了模拟植物的环境气氛,通过向气体流添加不同水平的湿度来重复气体测试。 k-nn分类器的精度至少为98%,所以官能化CMUT阵列可用于选择性检测本研究中使用的植物VOC组,即使在环境大气中的不同湿度水平。

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  • 来源
    《Sensors and Actuators》 |2021年第8期|130001.1-130001.12|共12页
  • 作者单位

    Dep. of Electrical and Computer Engineering NC State University Raleigh NC 27606 USA;

    Dep. of Electrical and Computer Engineering NC State University Raleigh NC 27606 USA;

    Dep. of Entomology and Plant Pathology NC State University Raleigh NC 27606 USA;

    Dep. of Entomology and Plant Pathology NC State University Raleigh NC 27606 USA;

    Dep. of Entomology and Plant Pathology NC State University Raleigh NC 27606 USA;

    Dep. of Electrical and Computer Engineering NC State University Raleigh NC 27606 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    CMUT; Electronic nose; Gas sensor; k-NN; Machine learning; Plant disease;

    机译:CMUT;电子鼻子;气体传感器;K-NN;机器学习;病害;

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