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Use of Spatiotemporal Response Information from Sorption-Based Sensor Arrays to Identify and Quantify the Composition of Analyte Mixtures

Woodka, Marc D. and Brunschwig, Bruce S. and Lewis, Nathan S. (2007) Use of Spatiotemporal Response Information from Sorption-Based Sensor Arrays to Identify and Quantify the Composition of Analyte Mixtures. Langmuir, 23 (26). pp. 13232-13241. ISSN 0743-7463. doi:10.1021/la7026708. https://resolver.caltech.edu/CaltechAUTHORS:20170523-101528170

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Abstract

Linear sensor arrays made from small molecule/carbon black composite chemiresistors placed in a low-headspace volume chamber, with vapor delivered at low flow rates, allowed for the extraction of new chemical information that significantly increased the ability of the sensor arrays to identify vapor mixture components and to quantify their concentrations. Each sensor sorbed vapors from the gas stream and, thereby, as in gas chromatography, separated species having high vapor pressures from species having low vapor pressures. Instead of producing only equilibrium-based sensor responses that were representative of the thermodynamic equilibrium partitioning of analyte between each sensor and the initial vapor phase, the sensor responses varied depending on the position of the sensor in the chamber and the time since the beginning of the analyte exposure. The concomitant spatiotemporal (ST) sensor array response therefore provided information that was a function of time, as well as of the position of the sensor in the chamber. The responses to pure analytes and to multicomponent analyte mixtures comprised of hexane, decane, ethyl acetate, chlorobenzene, ethanol, and/or butanol were recorded along each of the sensor arrays. Use of a non-negative least-squares (NNLS) method for analysis of the ST data enabled the correct identification and quantification of the composition of two-, three-, four-, and five-component mixtures from arrays using only four chemically different sorbent films. In contrast, when traditional time- and position-independent sensor response information was used, these same mixtures could not be identified or quantified robustly. The work has also demonstrated that, for ST data, NNLS yielded significantly better results than analyses using extended disjoint principal components modeling. The ability to correctly identify and quantify constituent components of vapor mixtures through the use of such ST information significantly expands the capabilities of such broadly cross-reactive arrays of sensors.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1021/la7026708DOIArticle
http://pubs.acs.org/doi/abs/10.1021/la7026708PublisherArticle
ORCID:
AuthorORCID
Brunschwig, Bruce S.0000-0002-6135-6727
Lewis, Nathan S.0000-0001-5245-0538
Additional Information:© 2007 American Chemical Society. Received August 29, 2007. Publication Date (Web): November 15, 2007. The authors thank Dr. Michael C. Burl for valuable discussions regarding potential pattern recognition approaches. Research was carried out in the Molecular Materials Research Center of the Beckman Institute at Caltech. This work was supported by the ARO ICB, HSARPA, and Boeing.
Funders:
Funding AgencyGrant Number
Army Research Office (ARO)UNSPECIFIED
Homeland Security Advanced Research Projects AgencyUNSPECIFIED
Boeing CorporationUNSPECIFIED
Issue or Number:26
DOI:10.1021/la7026708
Record Number:CaltechAUTHORS:20170523-101528170
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170523-101528170
Official Citation:Use of Spatiotemporal Response Information from Sorption-Based Sensor Arrays to Identify and Quantify the Composition of Analyte Mixtures Marc D. Woodka, Bruce S. Brunschwig, and Nathan S. Lewis Langmuir 2007 23 (26), 13232-13241 DOI: 10.1021/la7026708
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:77655
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 May 2017 18:31
Last Modified:15 Nov 2021 17:32

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