摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据纳米技术研究所B y NewsRx编辑的新闻报道，研究表明：“在这项工作中，研究了由六种不同的poly(3-methylthiophene)(P3MT)组成的石英晶体微天平(QCM)传感器的挥发性有机化合物(VOCs)传感性能。”我们的新闻编辑引用了纳米技术研究所的一篇文章:“传感器的制备是用三电极电池电化学沉积技术在镀金的QCM传感器上电化学沉积P3MT、P Py和P3MT-co-PPy,并用扫描电子显微镜对其结构特性进行了表征。”采用Langmuir、Freundlich、Temkin和Dubinin-Radushkevich等吸附等温线研究了共聚物基QCM传感器对邻苯二甲酸二甲酯和苯甲腈的敏感和选择性,包括二次判别(QD)、Neur Al判别网、K-最近邻判别网、K-最近邻判别法、K-最近邻判别法等。将支持向量机应用于12种不同分析物的传感器响应分类，利用机器学习算法成功地将被测物分类，QD法的分类精度达到97.34%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on artificial in telligence. According to news reporting out of the Institute of Nanotechnology b y NewsRx editors, research stated, “In this work, the volatile organic compounds (VOCs) sensing properties of a quartz crystal microbalance (QCM) transducer coa ted with six different poly(3-methylthiophene) (P3MT) copolymerized with polypyr role (PPy) are investigated.” Our news editors obtained a quote from the research from Institute of Nanotechno logy: “The sensor preparation involves the electrochemical deposition of P3MT, P Py, and P3MT-co-PPy on Au-coated QCM transducers by electrochemical deposition t echniques with a three-electrode cell. The structural properties of the copolyme r films are characterized using scanning electron microscopy, and their oxidatio n/reduction behavior is investigated through cyclic voltammetry. The copolymer-b ased QCM sensors exhibit high sensitivity and selectivity to dimethyl methyl pho sphonate and benzonitrile, even at low concentrations ( <1 p pm) at room temperature. Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich adsorption isotherms are studied to understand the VOCs sensing mechanism machin e learning classification algorithms including quadratic discriminant (QD), neur al nets, K-nearest neighbors, linear discriminant, and support vector machines a re applied to classify the sensor responses for the 12 different analytes. With the help of machine learning algorithms, tested analytes are successfully classi fied into their groups. The highest accuracy of 97.34% is achieved using the QD method.”
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