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Miniaturized multisensor system with a thermal gradient : Performance beyond the calibration range

Tonezzer, Matteo (författare)
Department of Chemical and Geological Sciences, Università di Cagliari, Campus di Monserrato, 09042 Monserrato (CA), Italy.;Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele All’Adige, Italy.
Masera, Luca (författare)
DISI, University of Trento, Via Sommarive 9, Povo, Trento, Italy.
Thai, Nguyen Xuan (författare)
ITIMS, Hanoi University of Science and Technology, Hanoi, Viet Nam.;Vietnam Metrology Institute, 8 Hoang Quoc Viet Road, Hanoi, Viet Nam.
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Nguyen, Hugo, 1955- (författare)
Uppsala universitet,Institutionen för materialvetenskap
Duy, Nguyen Van (författare)
ITIMS, Hanoi University of Science and Technology, Hanoi, Viet Nam.
Hoa, Nguyen Duc (författare)
ITIMS, Hanoi University of Science and Technology, Hanoi, Viet Nam.
visa färre...
Department of Chemical and Geological Sciences, Università di Cagliari, Campus di Monserrato, 09042 Monserrato (CA), Italy;Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele All’Adige, Italy. DISI, University of Trento, Via Sommarive 9, Povo, Trento, Italy. (creator_code:org_t)
Elsevier BV, 2023
2023
Engelska.
Ingår i: Journal of Science: Advanced Materials and Devices. - : Elsevier BV. - 2468-2179. ; 8:3
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensingmaterial (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as anano-electronic nose to distinguish five different gases and estimate their concentrations. This innovativeapproach uses identical sensors working at different operating temperatures thanks to the thermalgradient created by an integrated microheater. A system with in-house developed hardware and softwarewas used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitativediscrimination of all gases after calibration. The system worked perfectly within the calibrated range(100% correct classification, 6.9% average error on concentration value). This work focuses on minimizingthe number of points needed for calibration while maintaining good sensor performance, both forclassification and error in estimating concentration. Therefore, the calibration range (in terms of gasconcentration) was gradually reduced and further tests were performed with concentrations outsidethese new reduced limits. Although with only a few training points, down to just two per gas, the systemperformed well with 96% correct classifications and 31.7% average error for the gases at concentrationsup to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower thanthe calibration range, the system worked less well, with 93% correct classifications and 38.6% averageerror, probably due to proximity to the limit of detection of the sensors.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Engineering (hsv//eng)

Nyckelord

Gas sensor
Nanowire
Tin oxide
Metal decoration
Selectivity
Calibration

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