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Optimal coating selection for the analysis of organic vapor mixtures with polymer-coated surface acoustic wave sensor arrays



Details

  • Personal Author:
  • Description:
    An approach for the selection of optimal coatings with polymer coated surface acoustic wave (SAW) sensor arrays for the analysis of organic vapor mixtures was described. The consideration was limited to a four sensor array operated at constant temperature and humidity. Calibrated responses to each of the six vapors were used in an extended disjoint principal components regression (EDPCR) analysis to determine the relationship between each vapor's identity and the array response patterns. This procedure was repeated for all vapors using all possible subsets of four coated sensors from a larger set. The performance of each array was then modeled using a Monte Carlo simulation analysis of responses to each vapor and, assuming additivity of responses, binary and ternary mixture. Measures for accuracy were incorporated. The array with the lowest error rate was then selected as the optimal array. Sixteen polymer coatings were analyzed by depositing about 0.2% by weight in toluene to the sensors. Responses to the vapors were linear for the ranges examined, and the sensitivities of the coatings remained stable over several months. Errors occurred when individual vapors could not be identified due to mixture with a second vapor. The correct identification rate was 86%, close to the 87% predicted for the EDPCR Monte Carlo method. Results showed the additivity of responses for mixtures of these six vapors even for vapors with strong hydrogen bonding. Coating selection for ternary mixtures was also analyzed. The authors conclude that the EDPCR Monte Carlo method can be used for accurately predicting and optimizing the performance of polymer coated SAW sensor arrays. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • ISSN:
    0003-2700
  • Document Type:
    Text
  • Funding:
    Grant
  • Genre:
    Journal Article
  • Place as Subject:
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • Topic:
    Workplace Safety & Health
  • Location:
    North America
  • Volume:
    67
  • Issue:
    6
  • NIOSHTIC Number:
    nn:00225522
  • Citation:
    Anal Chem 1995 Mar; 67(6):1092-1106
  • Contact Point Address:
    Environmental & Indust Health U of Michigan Sch of Public Hl 109 South Observatory Ann Arbor, MI 48109-2029
  • Federal Fiscal Year:
    1995
  • NORA Priority Area:
  • Performing Organization:
    University of Michigan at Ann Arbor, Ann Arbor, Michigan
  • Peer Reviewed:
    True
  • Start Date:
    19890715
  • Source Full Name:
    Analytical Chemistry
  • End Date:
    19930314
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:5f248a4cd429c89a73bbca94b250eb7958fd33a92898cfe624f6d998ebda512cade2c19287c167c75703efa02e4c387a28547fa1b2aecbf4f2362d4dab00df7a
  • Download URL:
  • File Type:
    Filetype[PDF - 2.12 MB ]
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