Kramer Kirsten E, Rose-Pehrsson Susan L, Hammond Mark H, Tillett Duane, Streckert Holger H
Naval Research Laboratory, Chemistry Division Code 6181, Washington, DC 20375-5342, USA.
Anal Chim Acta. 2007 Feb 12;584(1):78-88. doi: 10.1016/j.aca.2006.11.030. Epub 2006 Nov 12.
Electrochemical sensors composed of a ceramic-metallic (cermet) solid electrolyte are used for the detection of gaseous sulfur compounds SO(2), H(2)S, and CS(2) in a study involving 11 toxic industrial chemical (TIC) compounds. The study examines a sensor array containing four cermet sensors varying in electrode-electrolyte composition, designed to offer selectivity for multiple compounds. The sensors are driven by cyclic voltammetry to produce a current-voltage profile for each analyte. Raw voltammograms are processed by background subtraction of clean air, and the four sensor signals are concatenated to form one vector of points. The high-resolution signal is compressed by wavelet transformation and a probabilistic neural network is used for classification. In this study, training data from one sensor array was used to formulate models which were validated with data from a second sensor array. Of the 11 gases studied, 3 that contained sulfur produced the strongest responses and were successfully analyzed when the remaining compounds were treated as interferents. Analytes were measured from 10 to 200% of their threshold-limited value (TLV) according to the 8-h time weighted average (TWA) exposure limits defined by the National Institute of Occupational Safety and Health (NIOSH). True positive classification rates of 93.3, 96.7, and 76.7% for SO(2), H(2)S, and CS(2), respectively, were achieved for prediction of one sensor unit when a second sensor was used for modeling. True positive rates of 83.3, 90.0, and 90.0% for SO(2), H(2)S, and CS(2), respectively, were achieved for the second sensor unit when the first sensor unit was used for modeling. Most of the misclassifications were for low concentration levels (such 10-25% TLV) in which case the compound was classified as clean air. Between the two sensors, the false positive rates were 2.2% or lower for the three sulfur compounds, 0.9% or lower for the interferents (eight remaining analytes), and 5.8% or lower for clean air. The cermet sensor arrays used in this analysis are rugged, low cost, reusable, and show promise for multiple compound detection at parts-per-million (ppm) levels.
在一项涉及11种有毒工业化学品(TIC)化合物的研究中,由陶瓷 - 金属(金属陶瓷)固体电解质组成的电化学传感器用于检测气态硫化合物SO₂、H₂S和CS₂。该研究考察了一个包含四个金属陶瓷传感器的传感器阵列,这些传感器的电极 - 电解质组成各不相同,旨在对多种化合物具有选择性。通过循环伏安法驱动传感器,以产生每种分析物的电流 - 电压曲线。原始伏安图通过对清洁空气进行背景扣除来处理,并且将四个传感器信号连接起来形成一个点向量。通过小波变换对高分辨率信号进行压缩,并使用概率神经网络进行分类。在本研究中,来自一个传感器阵列的训练数据用于构建模型,并用来自第二个传感器阵列的数据进行验证。在所研究的11种气体中,3种含硫气体产生了最强的响应,并且当将其余化合物视为干扰物时能够成功分析。根据美国国家职业安全与健康研究所(NIOSH)定义的8小时时间加权平均(TWA)暴露限值,分析物的测量浓度为其阈限值(TLV)的10%至200%。当使用第二个传感器进行建模时,对于一个传感器单元的预测,SO₂、H₂S和CS₂的真阳性分类率分别达到93.3%、96.7%和76.7%。当使用第一个传感器单元进行建模时,第二个传感器单元的SO₂、H₂S和CS₂的真阳性率分别达到83.3%、90.0%和90.0%。大多数错误分类发生在低浓度水平(如10 - 25% TLV),在这种情况下,化合物被分类为清洁空气。在两个传感器之间,三种硫化合物的假阳性率为2.2%或更低,干扰物(其余八种分析物)的假阳性率为0.9%或更低,清洁空气的假阳性率为5.8%或更低。本分析中使用的金属陶瓷传感器阵列坚固耐用、成本低、可重复使用,并且在百万分之一(ppm)水平上对多种化合物检测显示出前景。