Sensor uses artificial intelligence for selective gas classification

Developed at KAUST (King Abdullah University of Science and Technology), machine learning differentiates the gases according to the way they induce slight temperature changes in the sensor as they interact with it.

Smart electronic sensors have applications from medical diagnostics to the detection of industrial gas leaks. The challenge is to accurately detect the target gas among the complex mixture of chemicals typically found in the air, said Usman Yaqoob, a postdoc in the labs of Mohammad Younis, who led the research. “Existing sensing technologies still suffer from cross-sensitivity,” Yaqoob said in a statement.

On the hardware side is a heated strip of silicon called a microbeam resonator. When the microbeam is clamped at both ends, so that it is bent almost to buckling point, the frequency at which the microbeam resonates is said to be very responsive to changes in temperature.

“When operated near buckling point, the heated microbeam shows significant sensitivity to different gases when they have a heat conductivity lower or higher than air,” Yaqoob said. Gases with a higher thermal conductivity than air, such as helium and hydrogen, cool the microbeam, which increases its stiffness and its resonance frequency. Gases such as argon, with a lower thermal conductivity, have the opposite effect. “The shift in resonance frequency is detected using a microsystem analyser vibrometer,” Yaqoob said.