Prototyping and Evaluation of 1D Cylindrical and MEMS-Based Helmholtz Acoustic Resonators for Ultra-Sensitive CO2 Gas Sensing

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO2 detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N2-CO2. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO2 concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species.