The I/O transform of a chemical sensor

Published

March 14, 2016

Author(s)

Nalin Katta, Douglas C. Meier, Kurt D. Benkstein, Stephen Semancik, Baranidharan Raman

Abstract

A number of sensing technologies using a variety of transduction principles have been proposed for the purpose of non-invasive chemical sensing. A fundamental problem common to all sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the ana-lyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte is highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for robust recognition of the chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algo-rithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing.

Citation

Sensors and Actuators B-Chemical

Volume

232

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.snb.2016.03.019

Local Download

Keywords

gas-phase chemical sensor, signal processing, modeling, bio-inspired

Citation

Katta, N. , Meier, D. , Benkstein, K. , Semancik, S. and Raman, B. (2016), The I/O transform of a chemical sensor, Sensors and Actuators B-Chemical, [online], https://doi.org/10.1016/j.snb.2016.03.019, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=918992 (Accessed October 14, 2024)