Characterization of ash particles with a microheater andgas-sensitive SiC field-effect transistors

Particle emission from traffic, power plants or, increasingly, stoves and fireplaces poses a serious risk for human health. The harmfulness of the particles depends not only on their size and shape but also on adsorbates. Particle detectors for size and concentration are available on the market; however, determining content and adsorbents is still a challenge. In this work, a measurement setup for the characterization of dust and ash particle content with regard to their adsorbates is presented. For the proof of concept, ammonia-contaminated fly ash samples from a coal-fired power plant equipped with a selective non-catalytic reduction (SNCR) system were used. The fly ash sample was placed on top of a heater substrate situated in a test chamber and heated up to several hundred degrees. A silicon carbide field-effect transistor (SiC-FET) gas sensor was used to detect desorbing species by transporting the headspace above the heater to the gas sensor with a small gas flow. Accumulation of desorbing species in the heater chamber followed by transfer to the gas sensor is also possible. A mass spectrometer was placed downstream of the sensor as a reference. A clear correlation between the SiC-FET response and the ammonia spectra of the mass spectrometer was observed. In addition, different levels of contamination can be distinguished. Thus, with the presented setup, chemical characterization of particles, especially of adsorbates which contribute significantly to the harmfulness of the particles, is possible

Discrimination and Quantification of Volatile Organic Compounds in the ppb-Range with Gas Sensitive SiC-Field Effect Transistors

Gas sensitive FETs based on SiC have been studied for the discrimination and quantification of hazardous volatile organiccompounds (VOCs) in the low ppb range. The sensor performance was increased by temperature cycled operation (TCO) anddata evaluation based on multivariate statistics, here Linear Discriminant Analysis (LDA). Discrimination of formaldehyde,naphthalene and benzene with varying concentrations in the ppb range is demonstrated. In addition, it is shown that naphthalenecan be quantified in the relevant concentration range independent of the relative humidity and against a high ethanol background.Hence, gas sensitive SiC-FETs are suitable sensors for determining indoor air quality

Discrimination and Quantification of Volatile Organic Compounds in the ppb-Range with Gas Sensitive SiC-FETs Using Multivariate Statistics

Gas sensitive field effect transistors based on silicon carbide, SiC-FETs, have been studied for indoor air quality applications. The selectivity of the sensors was increased by temperature cycled operation, TCO, and data evaluation based on multivariate statistics. Discrimination of benzene, naphthalene, and formaldehyde independent of the level of background humidity is possible by using shape describing features as input for Linear Discriminant Analysis, LDA, or Partial Least Squares – Discriminant Analysis, PLS-DA. Leave-one-out cross-validation leads to a correct classification rate of 90 % for LDA, and for PLS-DA a classification rate of 83 % is achieved. Quantification of naphthalene in the relevant concentration range, i.e. 0 ppb to 40 ppb, was performed by Partial Least Squares Regression and a combination of LDA with a second order polynomial fit function. The resolution of the model based on a calibration with three concentrations was approximately 8 ppb at 40 ppb naphthalene for both algorithms. Hence, the suggested strategy is suitable for on demand ventilation control in indoor air quality application systems.SENSIndoo