Horizon Europe (2021 - 2027)

Adaptable Multi-pixel Gas Sensor Platform for a Wide Range of Appliance and Consumer Markets: AMUSENS

Last update: Oct 11, 2024 Last update: Oct 11, 2024

Details

Locations:Austria, Belgium, Denmark, France, Germany, Italy, Luxembourg, Spain
Start Date:Jun 1, 2024
End Date:May 31, 2028
Contract value: EUR 7,995,710
Sectors:Information & Communication Technology, Science & ...
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Information & Communication Technology, Science & Innovation
Categories:Grants
Date posted:Oct 11, 2024

Associated funding

Associated experts

Description

Programme(s):
HORIZON.2.4 - Digital, Industry and Space
HORIZON.2.4.4 - Advanced Materials

Topic(s): HORIZON-CL4-2023-RESILIENCE-01-33 - Smart sensors for the Electronic Appliances market (RIA)

Call for proposal: HORIZON-CL4-2023-RESILIENCE-01-TWO-STAGE

Funding Scheme: HORIZON-RIA - HORIZON Research and Innovation Actions

Grant agreement ID: 101130159

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, 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.

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