Detecting Chemical Hazards with Temperature-Programmed Microsensors: Overcoming Analytical Problems of Increasing Complexity by Utilizing Multi-Dimensional Databases

Published

July 6, 2009

Author(s)

Douglas C. Meier, Baranidharan Raman, Stephen Semancik

Abstract

Complex analytical problems, such as detecting trace quantities of hazardous chemicals in ambient environments composed of varying levels of humidity and vapors of benign products, require solutions that most effectively extract the relevant information about a sample's composition. This review demonstrates how a chemiresistive microarray approach, which uses multiple temperature-programmed elements to generate voluminous datasets, and then applies advanced data reduction algorithms to identify analytes present in the system. Herein the chemical microsensor platform is described, and its ability to generate orthogonal data through materials selection and temperature programming is demonstrated. Visual inspection of datasets reveals device selectivity, but statistical analyses are required to perform more complex identification tasks involving multiple analytes and multiple background compositions. Practical considerations surrounding long-term deployment are discussed, specifically the identification and correction of signal drift and the challenges surrounding real-time, unsupervised operation. Additional advancements in both device and algorithm are discussed, including repeatable device manufacturability and hierarchical classification, wherein heretofore untested analyte species can be described by identifiable functional groups.

Citation

Annual Review of Analytical Chemistry

Pub Type

Others

Download Paper

https://doi.org/10.1146/annurev-anchem-060908-155127

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Keywords

chemiresistive, gas sensor, microarray, temperature dependence, toxic industrial chemicals

Citation

Meier, D. , Raman, B. and Semancik, S. (2009), Detecting Chemical Hazards with Temperature-Programmed Microsensors: Overcoming Analytical Problems of Increasing Complexity by Utilizing Multi-Dimensional Databases, Annual Review of Analytical Chemistry, [online], https://doi.org/10.1146/annurev-anchem-060908-155127, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=832410 (Accessed April 25, 2026)

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