Vapor Recognition with Small Arrays of Polymer-Coated Microsensors. A Comprehensive Analysis

Details

• Personal Author:
  Groves WA ; Park J ; Zellers ET
• Description:
  A comprehensive analysis of vapor recognition as a function of the number of sensors in a vapor-sensor array is presented. Responses to 16 organic vapors collected from six polymer-coated surface acoustic wave (SAW) sensors were used in Monte Carlo simulations coupled with pattern recognition analyses to derive statistical estimates of vapor recognition rates as a function of the number of sensors in the array (< or = 6), the polymer sensor coatings employed, and the number and concentration of vapors being analyzed. Results indicate that as few as two sensors can recognize individual vapors from a set of 16 possibilities with < 6% average recognition error, as long as the vapor concentrations are > 5 x LOD for the array. At lower concentrations, a minimum of three sensors is required, but arrays of 3-6 sensors provide comparable results. Analyses also revealed that individual-vapor recognition hinges more on the similarity of the vapor response patterns than on the total number of possible vapors considered. Vapor mixtures were also analyzed for specific 2-, 3-, 4-, 5-, and 6-vapor subsets where all possible combinations of vapors within each subset were considered simultaneously. Excellent recognition rates were obtainable for mixtures of up to four vapors using the same number of sensors as vapors in the subset. Lower recognition rates were generally observed for mixtures that included structurally homologous vapors. Acceptable recognition rates could not be obtained for the 5- and 6-vapor subsets examined, due, apparently, to the large number of vapor combinations considered (i.e., 31 and 63, respectively). Importantly, increasing the number of sensors in the array did not improve performance significantly for any of the mixture analyses, suggesting that for SAW sensors and other sensors whose responses rely on equilibrium vapor-polymer partitioning, large arrays are not necessary for accurate vapor recognition and quantification. [Description provided by NIOSH]
• Subjects:
  Air Pollutants, Occupational Occupational Health Polymers Safety Solvents
• Keywords:
  Analytical-instruments Detectors Organic-vapors Polymer-fumes Solvent-vapors
• ISSN:
  0003-2700
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Maine ; Michigan ; OSHA Region 1 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  71
• Issue:
  17
• NIOSHTIC Number:
  nn:20031341
• Citation:
  Anal Chem 1999 Sep; 71(17):3877-3886
• Contact Point Address:
  Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 4801-2029
• Federal Fiscal Year:
  1999
• NORA Priority Area:
  Grants Other
• Performing Organization:
  University of Michigan at Ann Arbor, Ann Arbor, Michigan
• Peer Reviewed:
  True
• Start Date:
  19950930
• Source Full Name:
  Analytical Chemistry
• End Date:
  19990228
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:5edc3a688a0dc7af5145211e71c2ec1d3647ea4c15bbcc68c64126dd8c83ea9790c249ed8d3bbe573665c9410b06d7a7fdffc1d40f818af7d5f3934c537b55e2
• Download URL:
  https://stacks.cdc.gov/view/cdc/191609/cdc_191609_DS1.pdf
• File Type:
  [PDF - 116.02 KB ]