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Title: Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks

Abstract

In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. An artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration)more » information with array-ANN-based sensor methodologies.« less

Authors:
 [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
989535
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: Analytica Chimica Acta; Journal Volume: 558; Journal Issue: 1-2
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; BENDING; BINARY MIXTURES; GAS CHROMATOGRAPHY; NEURAL NETWORKS; PATTERN RECOGNITION; SILICON; microcantilever array; pattern recognition; artificial neural networks; polymeric and gas chromatography phases

Citation Formats

Senesac, Larry R, Datskos, Panos G, and Sepaniak, Michael J. Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks. United States: N. p., 2006. Web. doi:10.1016/j.aca.2005.11.024.
Senesac, Larry R, Datskos, Panos G, & Sepaniak, Michael J. Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks. United States. doi:10.1016/j.aca.2005.11.024.
Senesac, Larry R, Datskos, Panos G, and Sepaniak, Michael J. Sun . "Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks". United States. doi:10.1016/j.aca.2005.11.024.
@article{osti_989535,
title = {Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks},
author = {Senesac, Larry R and Datskos, Panos G and Sepaniak, Michael J},
abstractNote = {In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. An artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration) information with array-ANN-based sensor methodologies.},
doi = {10.1016/j.aca.2005.11.024},
journal = {Analytica Chimica Acta},
number = 1-2,
volume = 558,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}