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

Project description

Innovative gas sensor platform to benefit air quality monitoring and healthcare

Gas sensors are crucial for monitoring air pollutants in personal and industrial settings. They help ensure product quality in the food industry and analyse gases in healthcare. Such applications demand miniaturised, low-power and low-cost sensors with high gas selectivity. The EU-funded AMUSENS project aims to develop a flexible gas sensor platform by combining multi-pixel sensors and artificial intelligence. This approach will enhance gas selectivity and adaptability for targeted applications. Using innovative additive manufacturing techniques, AMUSENS will expand the range of metal oxide materials available, demonstrating their sustainability in wafer-scale processing. The platform adaptability will be demonstrated through applications in personal exposure monitoring and healthcare.

Objective

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.

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL4-2023-RESILIENCE-01-TWO-STAGE

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Coordinator

LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 558 132,21
Address
5 AVENUE DES HAUTS FOURNEAUX
4362 Esch Sur Alzette
Luxembourg

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Luxembourg Luxembourg Luxembourg
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