Researchers in Russia and Germany have designed a 3D- printed ‘electronic nose’ that serves as a proof of concept for low-cost and sensitive devices to be used in portable electronics and healthcare.
The rapidly growing fields of the Internet of Things (IoT) and advanced medical diagnostics require small, cost-effective, low-powered yet reasonably sensitive, and selective gas-analytical systems. These can be used for noninvasive diagnostics of human breath, such as diagnosing chronic obstructive pulmonary disease (COPD).
Researchers at the Skolkovo Institute of Science and Technology in Russia and the Karlsruhe Institute of Technology in Germany used 3D printing to create a sensor that can detect small quantities of chemicals in the air and in beverages.
The sensor was developed by printing nanocrystalline films 70nm thick of eight different metal oxides onto a multielectrode chip using manganese, cerium, zirconium, zinc, chromium, cobalt, tin, and titanium).
“For this work, we used microplotter printing and true solution inks. There are a few things that make it valuable,” said senior research scientist Fedor Fedorov at Skoltech.
“First, the printing resolution is close to the distance between electrodes on the chip, which is optimized for more convenient measurements. We show these technologies are compatible. Second, we managed to use several different oxides, enabling more orthogonal signals from the chip resulting in improved selectivity,” he said. “We can also speculate that this technology is reproducible and easy to be implemented in industry to obtain chips with similar characteristics, and that is really important for the ‘e-nose’ industry,”
The sensor was able to sniff out the difference between different alcohol vapours such as methanol, ethanol, isopropanol, and n-butanol, which are chemically very similar and hard to tell apart, at concentrations in the air as low as 10ppm. Since methanol is extremely toxic, detecting it in drinks and differentiating between methanol and ethanol can save lives.
Next: Processing electronic nose sensor data
To process the data, the team used linear discriminant analysis (LDA), a pattern recognition algorithm, but other machine learning algorithms could also be used for this task.
So far, the elecronic nose operates at high temperatures of 200-400 ºC but the team believe that quasi-2D materials such as MXene and graphene could be used to increase the sensitivity of the array and ultimately allow it to operate at room temperature. The team will continue working in this direction, optimizing the materials used to lower power consumption.
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