MICROSENSOR ARRAY FOR IDENTIFICATION OF ORGANIC VAPORS

我们建议研究使用涂层表面阵列- 用于识别和定量的声波（SAW）微传感器 几种不同化学类别的有机蒸汽。 这项工作是 由于需要改进直接读取中的传感器技术， 工业卫生监测设备。 目前可用的便携式 有机蒸气的监测仪器既不能识别未知的 也不能区分蒸汽混合物的组分。 一个 微传感器阵列，为用户提供独特的响应模式 给定的蒸汽可用于确定 单独的蒸汽或混合物。 体积小、功耗低 传感器阵列的要求将有助于并入 适用于实时个人监测的小型化仪器 以及制冷剂盒的突破性应用。 将测试18种不同的化学敏感材料， 传感器涂层暴露于50种有机蒸气，代表13 化学类 研制了一种158 MHz声表面波传感器（面积约0.8cm ~ 2） 实验室将依次涂上每种涂料 材料，并暴露于每一个目标蒸汽在相关的范围内， 浓度的 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。 还能够单独识别蒸汽 将测定二元和三元混合物中的As。 实验 验证从模式识别预测的结果 二元混合物的分析将在测试的子集上进行 蒸汽 大多数待使用的涂层材料由聚合物或 选择低聚物以提供部分选择性， 每种蒸汽的不同溶解度。 几种室温液体 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区分 其他类似化合物的尺寸和形状特征。 除了 测量给定蒸汽的稳态传感器响应 浓度，我们也将监测响应时间为每个 涂层/蒸汽对，以获得蒸汽扩散的估计 系数 将此功能添加到模式识别分析中 应导致阵列的选择性的进一步改进 基于不同的蒸汽扩散速率。

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.