MICROSENSOR ARRAY FOR IDENTIFICATION OF ORGANIC VAPORS

我们建议研究涂层表面阵列的使用- 用于识别和定量的声波(SAW)微型传感器 来自几个不同化学类别的有机蒸气。这项工作是 需要改进直读中的传感器技术 工业卫生监测设备。目前可用的笔记本电脑 有机蒸气监测仪器既不能识别未知 蒸汽也不区分蒸汽混合物的成分。一个 微传感器阵列，提供独特的响应模式 给定的蒸气可以用来确定 蒸汽单独或混合在一起的蒸汽。体积小、功耗低 传感器阵列的要求将有助于将其并入 适用于实时个人监护的小型化仪器 以及呼吸器药筒的突破性应用。 18种不同的化学敏感材料将被测试为 暴露在50种有机蒸汽中的传感器涂层，代表13种 化学课。自制的158 MHz声表面波传感器(面积为-0.8 cm~2) 实验室将按顺序对每种涂层进行涂层 材料，并暴露在相关范围内的每个目标蒸汽中 浓度。传感器响应将被存储，然后进行分析 共同使用模式识别方法，从而模拟 传感器阵列。也能够单独识别蒸汽 就像在二元和三元混合物中一样，将被确定。实验 对从模式识别预测的结果的验证 对二元混合物的分析将在测试的子集上执行 水蒸气。 所使用的大多数涂层材料由聚合物或 根据以下条件选择低聚物以提供部分选择性 每个水蒸气的不同溶解度。几种常温液体 晶体也将作为涂层材料进行测试。各向异性 液晶的性质可以提供基于微妙的辨别 其他类似化合物的大小和形状特征。除了……之外 测量给定水蒸气的稳态传感器响应 集中，我们还将监控每个 涂层/蒸汽对以获得蒸汽扩散的估计值 系数。将此功能添加到模式识别分析中 应进一步提高阵列的选择性 基于不同的水蒸气扩散速率。

We propose to investigate the use of an array of coated surface- acoustic-wave (SAW) microsensors for the identification and quantitation of organic vapors from several different chemical classes. This work is motivated by the need for improved sensor technology in direct-reading industrial-hygiene monitoring equipment. Currently available portable monitoring instruments for organic vapors can neither identify unknown vapors nor discriminate between the components of vapor mixtures. An array of microsensors that provides a unique response pattern for a given vapor can be used to determine the identify and concentration of the vapor alone or in a mixture. The small size and low power requirements of the sensor array will facilitate incorporation into miniaturized instrumentation suitable for real-time personal monitoring and respirator-cartridge breakthrough applications. Eighteen different chemically sensitive materials will be tested as sensor coatings for exposure to 50 organic vapors representing 13 chemical classes. A 158MHz SAW sensor (-0.8cm2 area) fabricated in our laboratory will be coated sequentially with each of the coating materials and exposed to each target vapor over a relevant range of concentrations. The sensor responses will be stored and then analyzed collectively using pattern recognition methods thereby simulating an array of sensors. The ability to identify vapors individually as well as in binary and ternary mixtures will be determined. Experimental verification of the results predicted from the pattern recognition analysis for binary mixtures will be performed on a subset of the test vapors. Most of the coating materials to be used consist of polymers or oligomers selected to provide partial selectivity based on the differential solubility of each vapor. Several room-temperature liquid crystals will also be tested as coating materials. The anisotropic nature of the liquid crystals can provide discrimination based on subtle size and shape features of otherwise similar compounds. In addition to measuring the steady-state sensor response for a given vapor concentration, we will also monitor the response time for each coating/vapor pair to obtain a estimate of the vapor diffusion coefficient. Adding this feature to the pattern recognition analysis should result in further improvements in the selectivity of the array based on differential vapor diffusion rates.