A Data Mining Procedure for Polymer Selection for Making Surface Acoustic Wave Sensor Array

Verma, Prabha; Yadava, R. D. S.

doi:10.1166/sl.2013.3047

Cited by 10 publications

(5 citation statements)

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“…To achieve this goal, the linear solvation energy relationships (LSER) modeling , was implemented. The LSER is an effective guide for determination of vapor responses of polymers, ,− fullerenes, ligand-capped metal nanoparticles, and DNA . The LSER method calculates material/vapor partition coefficients K as a linear combination of terms that represent several molecular types of interactions: log K = c + rR 2 + sπ 2 H + a ∑α 2 H + b ∑β 2 H + l log L 16 , where R 2 , π 2 H , α 2 H , β 2 H , and log L 16 are solvation parameters of the vapor in the sensing material, coefficients r , s , a , b , and l are the corresponding sensing material parameters, and c is the constant. , The partition coefficients K were calculated by using R 2 , π 2 H , α 2 H , β 2 H , and log L 16 solvation parameters of alcohol vapors , and r , s , a , b , and l parameters of different ligands .…”

Section: Multivariable Nonresonant and Resonant Impedance Sensorsmentioning

confidence: 99%

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet

2016

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Modern gas monitoring scenarios for medical diagnostics, environmental surveillance, industrial safety, and other applications demand new sensing capabilities. This Review provides analysis of development of new generation of gas sensors based on the multivariable response principles. Design criteria of these individual sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with independent outputs to recognize these different gas responses. These new sensors quantify individual components in mixtures, reject interferences, and offer more stable response over sensor arrays. Such performance is attractive when selectivity advantages of classic gas chromatography, ion mobility, and mass spectrometry instruments are canceled by requirements for no consumables, low power, low cost, and unobtrusive form factors for Internet of Things, Industrial Internet, and other applications. This Review is concluded with a perspective for future needs in fundamental and applied aspects of gas sensing and with the 2025 roadmap for ubiquitous gas monitoring.

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Section: Multivariable Nonresonant and Resonant Impedance Sensorsmentioning

confidence: 99%

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet

2016

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“…A detailed discussion on this is presented in [9]. Recently, in a series of papers from the authors' group, data mining procedures were applied to vapor/polymer partition coefficient data for making polymer selection [49][50][51]. The efficacy of this approach was demonstrated in a number of case studies involving detection of explosives, chemical weapon agents, narcotics, body odor, milk spoilage, fish freshness, etc.…”

Section: E-nose Sensor Array By Polymer Selectionmentioning

confidence: 99%

“…The approach consists of three steps: create a database for vapor/polymer partition coefficients, apply data mining methods for determining a maximally discriminating subset of polymers, and validate the selection by simulating prototype target detection experiments. The details are described in [9,[49][50][51]. Table 2 lists 39 prospective polymers and their solubility parameters.…”

Section: E-nose Sensor Array By Polymer Selectionmentioning

confidence: 99%

“…The application of data mining involves clustering these polymers according to some measure of similarity, and the selecting a representative polymer from six different clusters that are maximally separated. A number of clustering algorithms were analyzed earlier [9,[49][50][51]. Of these, the PCA, fuzzy c-means clustering (FCM), and fuzzy subtractive clustering (FSC) yielded the most consistent results.…”

Section: E-nose Sensor Array By Polymer Selectionmentioning

confidence: 99%

“…That is, a cold surface at −50 °C will be able to condense all VOCs except ethane. The data processing procedures are similar to those in [49][50][51]. It consists of preprocessing by logarithmic scaling, mean centering and scaling normalization, and processing by PCA.…”

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MEMS sensor array-based electronic nose for breath analysis—a simulation study

2018

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The paper presents a simulation study of breath analysis based on theoretical models of microelectromechanical structure (MEMS) cantilever sensor array. The purpose of this study is to suggest a methodology for the development of MEMS electronic nose (e-nose) for monitoring disease-specific volatiles in exhaled breath. Oxidative stress and diabetes are taken as case studies for the assessment of e-nose designs. The detection of ethane for general oxidative stress, isoprene for hypoxia, and acetone for diabetes are considered for targeted detection. A number of volatiles concurrently present in the exhaled breath are taken as interferents. The MEMS cantilevers are coated with volatile-selective polymers and are analyzed in both the static and dynamic modes. The sensor array is defined by polymer selections based on three data mining methods: principal component analysis (PCA), fuzzy c-means clustering (FCM), and fuzzy subtractive clustering (FSC). This utilizes vapor/polymer partition coefficients as a database. Analyses are carried out to find optimal combinations of the polymer selection method and cantilever sensing mode. Virtual breath analysis experiments are analyzed by PCA for target discrimination. It is found that no single combination works best in all conditions. The acetone (diabetes) detection is best in both sensing modes with the polymers selected by FSC; the isoprene (hypoxia) is detectable only in static sensing mode with polymers selected by FCM clustering; and the ethane (oxidative stress) detection is possible by all sensing modes and polymer selections, provided the breath samples are preconcentrated. This study suggests that it is difficult to realize a single general-purpose MEMS breath analyzer. The dedicated analyzers for specific disease indications can however be made with an optimal combination of sensing mode and polymer coatings.

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Fuzzy Subtractive Clustering for Polymer Data Mining for SAW Sensor Array Based Electronic Nose

Singh

Verma

Yadava

2017

Advances in Intelligent Systems and Computing

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A Data Mining Procedure for Polymer Selection for Making Surface Acoustic Wave Sensor Array

Cited by 10 publications

References 0 publications

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet

MEMS sensor array-based electronic nose for breath analysis—a simulation study

Fuzzy Subtractive Clustering for Polymer Data Mining for SAW Sensor Array Based Electronic Nose

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