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Low-cost chemical gas sensors for selective formaldehyde quantification at ppb-level in field tests
Saarland University, Lab for Measurement Technology, Germany.
Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering.
3S GmbH, Saarbrücken, Germany.
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Data from a silicon carbide based field-effect transistor were recorded over a period of nine days in a ventilated school room. For enhanced sensitivity and selectivity especially to formaldehyde, porous iridium on pulsed laser deposited tungsten trioxide was used as sensitive layer, in combination with temperature cycled operation and subsequent multivariate data processing techniques. The sensor signal was compared to reference measurements for formaldehyde concentration, CO2 concentration, temperature, and relative humidity. The results show a distinct pattern for the reference formaldehyde concentration, arising from the day/night cycle. Taking this into account, the projections of both principal component analysis and partial least squares regression lead to almost the same result concerning correlation to the reference. The sensor shows cross-sensitivity to an unidentified component of human activity, presumably breath, and, possibly, to other compounds appearing together with formaldehyde in indoor air. Nevertheless, the sensor is able to detect and partially quantify formaldehyde below 40 ppb with a correlation to the reference of 0.48 and negligible interference from ambient temperature or relative humidity.

Place, publisher, year, edition, pages
2017. p. 702-707
Keywords [en]
SiC-FET, multivariate data processing, indoor air quality, VOC, sick building syndrome
National Category
Engineering and Technology Other Chemical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-162246OAI: oai:DiVA.org:liu-162246DiVA, id: diva2:1372929
Conference
AMA Conferences 2017 - Sensor 2017 and IRS2 2017, Germany, 30 May - 1 June, 2017
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-12-06Bibliographically approved

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Puglisi, DonatellaMöller, PeterLloyd Spetz, AnitaAndersson, Mike

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Puglisi, DonatellaMöller, PeterLloyd Spetz, AnitaAndersson, Mike
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf