A1 Refereed original research article in a scientific journal
Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms
Authors: John, Alishba T.; Qian, Jing; Wang, Qi; Garay-Rairan, Fabian S.; Bandara, Y. M. Nuwan D. Y.; Lensky, Artem; Murugappan, Krishnan; Suominen, Hanna; Tricoli, Antonio
Publisher: AMER CHEMICAL SOC
Publishing place: WASHINGTON
Publication year: 2025
Journal: ACS Applied Materials and Interfaces
Journal name in source: ACS APPLIED MATERIALS & INTERFACES
Journal acronym: ACS APPL MATER INTER
Volume: 17
Issue: 18
First page : 27408
Last page: 27421
Number of pages: 14
ISSN: 1944-8244
eISSN: 1944-8252
DOI: https://doi.org/10.1021/acsami.5c02081
Web address : https://pubs.acs.org/doi/10.1021/acsami.5c02081
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/491848861
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.
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Funding information in the publication:
K.M., A.L., H.S., and A.T. acknowledge financial support from the Department of Defence and Australian Research Council for the National Intelligence and Security Discovery Research Grants (NISDRG) Program (NS210100083). This research was funded by and has been delivered in partnership with Our Health in Our Hands (OHIOH), a strategic initiative of the Australian National University, which aims to transform health care by developing new personalized health technologies and solutions in collaboration with patients, clinicians, and health care providers. The authors acknowledge the facilities and the scientific and technical assistance of Microscopy Australia at the Centre for Advanced Microscopy, Australian National University, a facility that is funded by the University and the Federal Government. J.Q. is funded by the National Intelligence and Security Discovery Research (NISDR) Grant ID: NS210100083. A.T. gratefully acknowledges the support of the Australian Research Council for a Future Fellowship (FT200100939) and Discovery grant DP190101864. A.T. also acknowledges financial support from the North Atlantic Treaty Organization Science for Peace and Security Programme project AMOXES (#G5634).
