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

doi:10.1016/j.aca.2005.11.024

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

Full Record
Other Related Research

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 »« less

Authors:

Senesac, Larry R ^[1]; Datskos, Panos G ^[1]; Sepaniak, Michael J ^[1]

ORNL

Publication Date:: Sun Jan 01 00:00:00 EST 2006

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.

Copy to clipboard


                    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.

Copy to clipboard


                    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.

Copy to clipboard


                    
@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}

}

Copy to clipboard

Journal Article:

DOI: 10.1016/j.aca.2005.11.024

Other availability

Find in Google Scholar

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Identification And Determination Of Concentration Of Salts In Natural Waters By Raman Spectroscopy Using Artificial Neural Networks

Journal Article Burikov, S. ; Dolenko, T. ; Dolenko, S. ; ... - AIP Conference Proceedings

No abstract prepared.
DOI: 10.1063/1.3482792
Identification and control of induction machines using artificial neural networks

Journal Article Wishart, M.T. ; Harley, R.G. - IEEE Transactions on Industry Applications

This paper proposes the use of Artificial Neural Networks (ANN`s) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme.
DOI: 10.1109/28.382123
Identification of power system load dynamics using artificial neural networks

Journal Article Bostanci, M. ; Koplowitz, J. ; Taylor, C.W. - IEEE Transactions on Power Systems

Power system loads are important for planning and operation of an electric power system. Load characteristics can significantly influence the results of synchronous stability and voltage stability studies. This paper presents a methodology for identification of power system load dynamics using neural networks. Input-output data of a power system dynamic load is used to design a neural network model which comprises delayed inputs and feedback connections. The developed neural network model can predict the future power system dynamic load behavior for arbitrary inputs. In particular, a third-order induction motor load neural network model is developed to verify the methodology. Neuralmore »« less
DOI: 10.1109/59.627843
Identification and quantification of components in ternary vapor mixtures using a microcantilever sensor array and a neural network

Journal Article Zhao, W ; Pinnaduwage, Lal A ; Leis, J. W. ; ... - Journal of Applied Physics

We report the experimental details on the successful application of the electronic nose approach to identify and quantify components in ternary vapor mixtures. Preliminary results have recently been presented [L. A. Pinnaduwage et al., Appl. Phys. Lett. 91, 044105 (2007)]. Our microelectromechanical-system-based electronic nose is composed of a microcantilever sensor array with seven individual sensors used for vapor detection and an artificial neural network for pattern recognition. A set of custom vapor generators generated reproducible vapor mixtures in different compositions for training and testing of the neural network. The sensor array was selected to be capable of generating different responsemore »« less
DOI: 10.1063/1.2921866
Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks

Journal Article Chady, T. ; Lopato, P. - AIP Conference Proceedings

In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency componentsmore »« less
DOI: 10.1063/1.2184606

Similar Records