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Related Experiment Video

Updated: Jun 10, 2026

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

Detecting chemical hazards with temperature-programmed microsensors: overcoming complex analytical problems with

Douglas C Meier¹, Baranidharan Raman, Steve Semancik

¹Chemical Science and Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8362, USA. dmeier@nist.gov

Annual Review of Analytical Chemistry (Palo Alto, Calif.)

|July 20, 2010

Summary

This summary is machine-generated.

This review details a chemiresistive microarray sensor system for hazardous chemical detection. It uses temperature programming and pattern recognition to identify chemical fingerprints, enabling complex environmental analysis.

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Area of Science:

Analytical Chemistry
Sensor Technology
Materials Science

Background:

Detecting trace hazardous chemicals in complex environments is challenging.
Advanced analytical methods are needed to extract detailed sample composition information.

Purpose of the Study:

To present a chemiresistive microarray-based approach for target identification.
To combine temperature-programmed sensing with statistical pattern recognition for chemical analysis.

Main Methods:

Utilizing a chemical-microsensor platform with materials selection and temperature programming.
Generating orthogonal data for enhanced selectivity.
Applying statistical pattern recognition for multivariate chemical fingerprint extraction.

Main Results:

The platform demonstrates device selectivity through visual inspection and statistical analysis.
Temperature programming and materials selection yield analytically rich datasets.
Statistical methods are crucial for complex identification tasks.

Conclusions:

The chemiresistive microarray approach offers a powerful tool for chemical detection.
Advances in device and algorithm design are addressing long-term deployment challenges.
Future work focuses on signal drift correction, unsupervised operation, manufacturability, and hierarchical classification.