4.6 Article

Miniaturized langasite MEMS micro-cantilever beam structured resonator for high temperature gas sensing

Journal

SMART MATERIALS AND STRUCTURES
Volume 29, Issue 5, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1361-665X/ab78b6

Keywords

langasite; ZnO nanorods; MEMS; gas sensing; harsh environment; PCA

Funding

  1. National Science Foundation [1508711, 1508862]
  2. Directorate For Engineering
  3. Div Of Electrical, Commun & Cyber Sys [1508711, 1508862] Funding Source: National Science Foundation

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In this paper, a miniaturized lanthanum gallium silicate (langasite, LGS, La3Ga5SiO14) micro-electromechanical systems (MEMS) micro-cantilever beam structured resonator is proposed for high temperature gas sensing applications. The proposed MEMS gas sensor is based on the thickness-shear vibration of the high temperature piezoelectric substrate, LGS, which is fabricated with bulk and surface micromachining technology. The fabrication process is optimized for better stability and integrity of the proposed LGS MEMS gas sensor. In order to improve the gas sensing sensitivity, the sample is further deposited with ZnO nanorods, where abundant gas molecules are expected to be adsorbed. Laboratory tests are subsequently carried out to validate the sensor's response to various gas species, verify the adsorption mechanism, and investigate the sensitivity of the proposed gas sensor. Results show that compared to several resonator-based gas sensors, the proposed gas sensor displays a better sensitivity for CO2 sensing. The cross-sensitivity of ZnO nanorods between various gas species can be further addressed by principal component analysis technology, where important features can be extracted to distinguish between different gas species and/or concentrations. The proposed MEMS LGS resonator can be utilized for harsh environment applications, such as turbine-engine combustion chambers, nuclear power plants, and hazardous chemical industries, where high temperature can hamper most room temperature gas sensors. Combined with appropriate machine learning technology, the proposed gas sensor can be a good alternative to expensive, bulky gas chromatography devices.

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