Portable analytical system employing tunable separation and microsensor array detection for indoor air quality monitoring.

Exposure to air contaminants in non-industrial indoor workplaces has become a general public health concern. Among the contaminants found in such environments, volatile and semi-volatile organic compounds ((S)VOCs) have attracted the most attention because of their ubiquity and possible etiologic roles in sick building syndrome. Currently, determining the presence and concentrations of (S)VOCs rely on labor- and capital-intensive sample collection and laboratory analytical methods, which limit the quality and quantity of data collected. The primary goal of this research is to develop a portable indoor-air quality (IAQ analyzer for the determination of complex (S)VOC mixtures at low part-per-billion (ppb) concentrations. The key features of the instrument are a multi-adsorbent preconcentrator/focuser, a dual-column, high-speed separation module with tunable retention capabilities, and a detector comprising a microfabricated sensor array whose output provides a characteristic 'fingerprint' of each analyte. An emphasis is placed on the development of the preconcentrator/focuser. Results demonstrate that with proper design the tradeoffs between maintaining adequate capacity as well as efficient thermal desorption efficiency can be achieved. Variables affecting the performance of the preconcentrator/focuser are investigated and modeled, and it is shown that a 3-adsorbent bed containing only 12 mg of adsorbent material can quantitatively capture and thermally desorb more than 40 compounds at 100 ppb each in a 1 L sample volume. The detector utilizes an array of three polymer-coated surface acoustic wave (SAW) sensors in addition to characterizing detector performance, an investigation is made of the fundamental mechanisms governing responses in such sensors, which arise from changes in the polymer mass and modulus upon vapor sorption. A previously established model is revised based on the new finding of a partition coefficient bias and the consequent recognition of the importance of free volume effects on sensor responses. Through laboratory testing and modifications in the design of the prototype instrument, conditions are established for optimizing the separation, recognition, and quantification of the components of a mixture of 30 (S)VOCs at low-ppb concentrations. Chamber testing assesses performance as a function of environmental variables and concentration fluctuations and demonstrates that results are comparable to those obtained from standard methods.Ph.D.Applied SciencesEnvironmental engineeringHealth and Environmental SciencesOccupational safetyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/132477/2/3058005.pd