Chul Han Kwon

Multi-layered thick-film gas sensor array for selective sensing by catalytic filtering technology

Abstract A C3H8 gas sensor array with high sensitivity and good selectivity by the combination of the catalytic filtering and the gas diffusion control has been achieved. The gas sensor array consists of a couple of catalytic filter layers, one with a Pd and the other with a Pt, a SiO2 insulating layer and two sensing layers on an alumina substrate. The sensor array shows high sensitivity to 500 ppm of C3H8 at temperature above 400°C and good selectivity for the interfering gases such as CO and C2H5OH by using a simple signal processing technique.

Photoeffects in La3+‐doped BaTiO3 ceramic electrodes

Photoelectrochemical conversion characteristics of semiconducting, polycrystalline BaTiO3 electrodes have been investigated as a function of sintering atmosphere (air/H2) and the amount of dopant (lanthanum). The photocurrents for specimens sintered in air were greater than those of specimens sintered in hydrogen. Also, as the amount of dopant in BaTiO3 sintered in air increased, the photocurrent increased up to 0.2 at. % addition and then decreased. However, for BaTiO3 sintered in hydrogen, the photocurrents decreased consistently with the addition of the dopant. These results could be explained in terms of ceramic microstructure and electrical resistivity. Regardless of the amount of lanthanum, the photoresponse, as the wavelength of the induced light that varied, began to appear around 420–430 and 780–800 nm, which corresponds to the energy band of La‐doped BaTiO3 and the oxygen vacancy level within the gap, respectively.

Gas Identification Using Oxide Semiconductor Gas Sensor Array and Neural-network Pattern Recognition

In order to identify CH/sub 3/SH, (CH/sub 3/)/sub 3/, C/sub 2/H/sub 5/OH and CO gases in the concentration range of 0.1 to 100 ppm, a gas recognition system using a gas sensor array and neural network pattern recognition has been fabricated. The sensor array consists of such thin film oxide semiconductor sensing materials as 1 wt.% Pd-doped SnO/sub 2/, 6 wt.% AI/sub 2/O/sub 3/-doped ZnO, WO/sub 3/ and ZnO. The principal component analysis and the neural-network pattern recognition analysis were used for the discrimination of gas species and concentrations. Good separation among gases and concentrations was obtained using the principal component analysis. The recognition probability of the neural-network was 100% for each five-trial of twelve gas samples.