Summary
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, and in health care by analyzing gases from human body. 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 both to accelerate the choice of materials and for data fusion to determine specific patterns in the gas analysis. Two specific applications targeting personal exposure and health care will demonstrate the adaptability of the platform, based on an analysis of the users' requirements.
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.
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 both to accelerate the choice of materials and for data fusion to determine specific patterns in the gas analysis. Two specific applications targeting personal exposure and health care will demonstrate the adaptability of the platform, based on an analysis of the users' requirements.
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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| Web resources: | https://cordis.europa.eu/project/id/101130159 |
| Start date: | 01-06-2024 |
| End date: | 31-05-2028 |
| Total budget - Public funding: | - 7 995 710,00 Euro |
Cordis data
Original description
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, and in health care by analyzing gases from human body. 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 both to accelerate the choice of materials and for data fusion to determine specific patterns in the gas analysis. Two specific applications targeting personal exposure and health care will demonstrate the adaptability of the platform, based on an analysis of the users' requirements.
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.
Status
SIGNEDCall topic
HORIZON-CL4-2023-RESILIENCE-01-33Update Date
23-12-2024
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Organisations
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| LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY (LIST) (Coördinator)
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| ATLANT HOLDING IVS (ATLANT Holding, ATLANT 3D Nanosystems, ATLANT)
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| ELLONA SAS
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| Institut Mines-Télécom (IMT)
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| JLM INNOVATION GMBH
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| SCIENCE FOR CHANGE, SL
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| SCIOSENSE GERMANY GMBH
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| TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH (TEC)
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| UNIVERSITE DE LIEGE (ULG)
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| University of Brescia