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

Journal Article · Wed Mar 15 00:00:00 EST 1995 · Analytical Chemistry (Washington)

DOI:https://doi.org/10.1021/ac00102a012· OSTI ID:28709

Zellers, E T; Batterman, S A; Han, M; Patrash, S J ^[1]

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1. Univ. of Michigan, Ann Arbor, MI (United States)

A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysis of organic vapors is described. The method combines an extended disjoint principal components regression (EDPCR) pattern recognition analysis with Monte Carlo simulations of sensor responses to rank the various possible coating selections and to estimate the ability of the sensor array to identify any set of vapor analytes. A data base consisting of the calibrated responses of 10 polymer-coated SAW sensors to each of six organic solvent vapors from three chemical classes was generated to demonstrate the method. The predicted rate of vapor identification (87%) was experimentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in identification occurred when an individual vapor could not be differentiated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for several ternary vapor mixtures is also examined. Results demonstrate the capabilities of polymer-coated SAW sensor arrays for analyzing of solvent vapor mixtures and the advantages of the EDPCR-Monte Carlo method for predicting and optimizing performance. 30 refs., 5 figs., 11 tabs.

• Sponsoring Organization: USDOE
• OSTI ID: 28709
• Journal Information: Analytical Chemistry (Washington), Vol. 67, Issue 6; Other Information: PBD: 15 Mar 1995
• Country of Publication: United States
• Language: English

Related Subjects

40 CHEMISTRY
99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
36 MATERIALS SCIENCE
44 INSTRUMENTATION
INCLUDING NUCLEAR AND PARTICLE DETECTORS
66 PHYSICS
VAPORS
CHEMICAL ANALYSIS
ORGANIC COMPOUNDS
MEASURING INSTRUMENTS
CALIBRATION
EXPERIMENTAL DATA
NUMERICAL DATA
SOUND WAVES
POLYMERS
SURFACE COATING
ADSORPTION ISOTHERMS
PATTERN RECOGNITION
MONTE CARLO METHOD