MICROANALYTICAL SYSTEM FOR INDOOR VOC MONITORING

用于室内 VOC 监测的微量分析系统

• 批准号: 2860941
• 负责人: EDWARD T ZELLERS
• 金额: $ 26.79万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2001-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2860941
• 关键词: air sampling /monitoring; biomedical equipment development; occupational health /safety; organic chemicals

DESCRIPTION: The development of a high-performance, hand-held microanalytical system capable of on-site identification and quantification of low-/sub-ppb concentrations of volatile organic compounds of anthropogenic and microbial origin (VOCs and MVOCs) encountered in nonindustrial indoor working environments is proposed. Advanced strategies for sample collection, component separation, and detection will be incorporated into a fieldable instrument about the size of a notebook computer. It will employ a thermally modulated dual-bed mini preconcentrator for vapor collection, a tunable, high-speed separation module, and an integrated array of micromachined flexural-plate-wave (FPW) sensors for vapor recognition and quantification. The preconcentrator will partition captured vapors onto two separate porous-polymer adsorbent beds in series, according to vapor pressure and affinity for the adsorbent, and then thermally desorb them sequentially in sharp, discrete pulses. A series of two short, high-resolution micro capillary columns, each coated with a different stationary phase, will be used for separating mixture components. The column temperature will be rapidly adjusted for each preconcentrated fraction. A pressure modulation valve at the juncture of the two columns will permit preprogramed "tuning" of retention and separation, with a complete multi-component mixture separation performed in <60 sec for each preconcentrated fraction. An integrated array of six partially selected polymer-coated FPW sensors will provide multi-channel detection capabilities. Artificial neural networks will be used to identify and discriminate among vapors from their response patterns and retention times. Concentration-time analytical profiles for mixtures of up to 30 VOCs will be obtainable at measurement intervals as short as 7-10 min and stored in an embedded computer for subsequent data extraction and archiving. Physicochemical and statistical models of preconcentration, separation, and sensor responses will be used to optimize system operating parameters and to predict performance for any potential exposure scenario. Laboratory testing will demonstrate the capability for continuous measurement of complex vapor mixtures. The analytical power and versatility embodied in this system represent a significant advancement over the current state-of-the-art in vapor monitoring instruments. Dramatic reductions in the time cost of analyses of indoor VOCs and MVOCs will be realized, obviating the need for conventional sorbent-tube/GC-MS sampling and analysis for routine monitoring. This, in turn, will facilitate the assessment of exposure distributions and the implementation of rational intervention strategies to address indoor environmental quality problems.

描述：开发一种高性能、手持式 能够现场鉴定和定量的微量分析系统 低/亚ppb浓度的挥发性有机化合物， 人为和微生物来源（VOCs和MVOCs）， 非工业室内工作环境。 先进的策略 用于样品收集、组分分离和检测的 整合到一个笔记本大小的野外仪器中 电脑 它将采用热调制双床迷你 用于蒸汽收集的预浓缩器，可调的高速分离 模块和集成阵列的微机械弯曲板波（FPW） 用于蒸汽识别和量化的传感器。 预浓缩器将 将捕获的蒸气分配到两个单独的多孔聚合物吸附剂床上， 系列，根据蒸汽压和吸附剂的亲和力，然后 以尖锐的离散脉冲顺序地热解吸它们。 一系列 两个短的、高分辨率的微毛细管柱，每个柱都涂有 不同的固定相将用于分离混合物组分。 将快速调整各预浓缩液的柱温 分数 两柱连接处的压力调节阀 将允许预编程的“调整”的保留和分离， 在<60秒内完成多组分混合物的分离， 预浓缩馏分。 六个部分选定的集成阵列 聚合物涂层FPW传感器将提供多通道检测 能力的 人工神经网络将用于识别和 根据响应模式和保留时间区分蒸汽。 将对多达30种挥发性有机化合物的混合物进行浓度-时间分析， 可在短至7-10分钟的测量间隔内获得，并储存在 嵌入式计算机用于随后的数据提取和存档。 预浓缩、分离和纯化的物理化学和统计模型 传感器响应将用于优化系统操作参数， 预测任何潜在暴露场景的性能。 实验室检测 将展示连续测量复杂蒸汽的能力 混合物。 该系统所体现的分析能力和多功能性 代表了对当前最先进技术的重大进步， 蒸汽监测仪器 大幅度减少时间成本， 室内VOCs和MVOCs的分析将得以实现， 常规吸附管/GC-MS采样和分析 监测. 这反过来又将有助于评估接触情况 分配和实施合理的干预战略， 解决室内环境质量问题。