Adaptable multi-pixel gas sensor platform for a wide range of appliance and consumer markets (AMUSENS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Industry
Destination: Increased Autonomy in Key Strategic Value Chains for Resilient Industry
Action type: Research and Innovation actions
Countries involved: Luxembourg,Austria,Spain,Belgium,Denmark,Germany,Italy,France,Greece
Call: Smart sensors for the Electronic Appliances market (RIA) (HORIZON-CL4-2023-RESILIENCE-01-TWO-STAGE, 2023)
Start date: 2024-06-01
End date: 2028-05-31
Duration (months): 48.0
Total project cost (EUR): 10495398.65
EU funding (EUR): 8596952.84
Participants: 11
Project status: SIGNED

Gas sensors are crucial in the personal and industrial monitoring to analyze personal exposure to air pollutants or to critical gases, to control product quality such as in the food industry, in health care by analyzing gases from human body and using breath analysis combined with wearable sensors for personal stress estimation. These applications require miniaturized low power and low-cost gas sensors with good gas selectivity to be integrated in personal devices, in product packaging or in widely distributed sensor networks. AMUSENS aims at developing a gas sensor platform with flexible selectivity to different gas environments by combining a multi-pixel approach and artificial intelligence to adapt the data analysis to the targeted applications. It is based on metal oxide sensing materials on micro-hotplate platform, which are already available on the market for low power applications, but suffer from a lack of selectivity. Gas-selective multi-pixel sensors based on different metal oxide materials have been demonstrated, but their industrialization is limited to few industrially available materials. By using original additive manufacturing approaches for local liquid-phase and gas-phase depositions, we aim at extending the choice of available materials and demonstrate their sustainability in wafer-scale processing. Artificial intelligence will be used to accelerate the choice of materials, for data fusion to determine specific patterns in the gas analysis and to optimize sensor calibration through calibration transfer models. Three specific applications targeting personal exposure and health care will demonstrate the adaptability of the platform, based on an analysis of the users' requirements. AI interpretation will be facilitated using model-agnostic interpretability analysis of the AI-based models for calibration transfer and on the use-case models. The proposed architecture will be adaptable to many applications (i) from the flexibility in choosing the materials, made possible by the local deposition techniques, and (ii) from the programming protocol of the artificial intelligence. This approach of products with on-demand properties will improve the resilience of the gas sensor industry by accelerating the time to market of products with enhanced performances.

Keywords: multi pixel gas sensor metal oxide gas sensor additive manufacturing inkjet printing local spatial atomic layer deposition mems environmental monitoring air quality health care