Metal Oxide–Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms

Abstract

The increasing demand for gas molecule detection emphasizes the need for portable sensor devices possessing selectivity, a low limit of detection (LOD), and a large dynamic range. Despite substantial progress in developing nanostructured sensor materials with heightened sensitivity, achieving sufficient selectivity remains a challenge. Here, we introduce a strategy to enhance the performance of chemiresistive gas sensors by combining an advanced sensor design with machine learning (ML). Our sensor architecture consists of a tungsten oxide (WO₃) nanoparticle network, as the primary sensing layer, with an integrated zeolitic imidazolate framework (ZIF-8) membrane layer, used to induce a gas-specific delay to the diffusion of analytes, sharing conceptual similarities to gas chromatography. However, the miniaturized design and chemical activity of the ZIF-8 results in a nontrivial impact of the ZIF-8 membrane on the target analyte diffusivity and sensor response. An ML method was developed to evaluate the response dynamics with a panel of relevant analytes including acetone, ethanol, propane, and ethylbenzene. Our advanced sensor design and ML algorithm led to an excellent capability to determine the gas molecule type and its concentration, achieving accuracies of 97.22 and 86.11%, respectively, using a virtual array of 4 sensors. The proposed ML method can also reduce the necessary sensing time to only 5 s while maintaining an accuracy of 70.83%. When compared with other ML methods in the literature, our approach also gave superior performance in terms of sensitivity, specificity, precision, and F1-score. These findings show a promising approach to overcome a longstanding challenge of the highly miniaturized but poorly selective semiconductor sensor technology, with impact ranging from environmental monitoring to explosive detection and health care.