Periodic Reporting for period 1 - AMUSENS (Adaptable multi-pixel gas sensor platform for a wide range of appliance and consumer markets)
Okres sprawozdawczy: 2024-06-01 do 2025-11-30
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. Three specific applications chosen in the fields of personal environmental monitoring and health care (metabolic states), as well as stress levels estimation, will demonstrate the adaptability of the platform, based on an analysis of the users‘ requirements.
The first period of the project was dedicated to the definition of the requirements for the targeted end-user case and the selection and development of the gas sensing materials to be integrated in the multi-pixel platform. This fine selection has started from a thorough analysis of the end-user requirements in terms of gases to be detected and related conditions, with a final selection of 12 target gases to be addressed during the project. A preliminary selection of the sensing materials to be developed was made from the literature. The materials (MO nanostructures) and material combinations (coated MO nanostructures) were analyzed in terms of sensitivity and potential selectivity to targeted gases, processability with the envisioned additive manufacturing approaches, and were finally screened by performing a safety analysis of the proposed materials, resulting in 6 MO nanostructures and 6 coatings to be investigated.
More than 24 materials and material combinations were then processed using laboratory-scale methods, i.e. standard drop casting and batch atomic layer deposition, including duplicates (2 to 4 samples for each) for statistical analysis. All devices have been exposed to up to 10 gases under the same protocol and various conditions (temperature, humidity). The main result from this first period is a database of sensing response for all devices that have been tested. In parallel, analysis algorithms were developed to analyze the response through machine learning and select the most appropriate material combinations for specific gas selectivity. The algorithm has been validated on data available in the literature and has demonstrated to be ready for analyzing the data produced during the project, although the current data are still not fully appropriate for a perfect selection, which will be investigated at the beginning of the next period.
In parallel, preparation steps for the next period include the design and fabrication of a new testing chamber that will allow a more accurate determination of the key sensing parameters, such as response and recovery time, and that will be used to enhance the statistics of gas sensing data to train the AI algorithms. All materials investigated in the first phase have also been transferred using local deposition methods, i.e. inkjet printing for MO nanostructures and DALP® for the coating. At last co-design workshops have been organized to better target the user needs for the end user cases to be tested at the end of the project.
More than 24 materials and material combinations were then processed using laboratory-scale methods, i.e. standard drop casting and batch atomic layer deposition, including duplicates (2 to 4 samples for each) for statistical analysis. All devices have been exposed to up to 10 gases under the same protocol and various conditions (temperature, humidity). The main result from this first period is a database of sensing response for all devices that have been tested. In parallel, analysis algorithms were developed to analyze the response through machine learning and select the most appropriate material combinations for specific gas selectivity. The algorithm has been validated on data available in the literature and has demonstrated to be ready for analyzing the data produced during the project, although the current data are still not fully appropriate for a perfect selection, which will be investigated at the beginning of the next period.
In parallel, preparation steps for the next period include the design and fabrication of a new testing chamber that will allow a more accurate determination of the key sensing parameters, such as response and recovery time, and that will be used to enhance the statistics of gas sensing data to train the AI algorithms. All materials investigated in the first phase have also been transferred using local deposition methods, i.e. inkjet printing for MO nanostructures and DALP® for the coating. At last co-design workshops have been organized to better target the user needs for the end user cases to be tested at the end of the project.
The first period of the project has been dedicated to the choice and the development of gas sensing materials to be integrated into the multi-pixel platform for the targeted user cases, namely monitoring personal exposure and breath analysis for fitness and stress estimation. The main results beyond the state of the art are:
- A literature review and the critical analysis using the expertise of the consortium conducted for the identification of 12 target gases and 12 most promising materials to be combined for a selective detection of the target gases. The materials are specifically selected with a fully integrated approach combining all aspects for an optimum future industrial transfer, from sensing properties to processability and safety requirements.
- A total number of 24 material combinations have been tested under exposure to 10 gases and various conditions (temperature, humidity) to build up a consistent database of gas sensor responses. Such a fully consistent set of measurement allowing the direct comparison of such a large number of materials and conditions is hardly described in the literature and will be a major output of the project.
- Ink formulation validated for the inkjet printing of metal oxide nanostructures with highly challenging aspect ratios (nanorods, nanobundles).
- Local deposition of 4 different materials using original Direct Atomic Layer Processing (DALP®) method.
- Definition of user needs in the domains of monitoring personal exposure to gases and breath analysis for fitness and stress estimation.
- A literature review and the critical analysis using the expertise of the consortium conducted for the identification of 12 target gases and 12 most promising materials to be combined for a selective detection of the target gases. The materials are specifically selected with a fully integrated approach combining all aspects for an optimum future industrial transfer, from sensing properties to processability and safety requirements.
- A total number of 24 material combinations have been tested under exposure to 10 gases and various conditions (temperature, humidity) to build up a consistent database of gas sensor responses. Such a fully consistent set of measurement allowing the direct comparison of such a large number of materials and conditions is hardly described in the literature and will be a major output of the project.
- Ink formulation validated for the inkjet printing of metal oxide nanostructures with highly challenging aspect ratios (nanorods, nanobundles).
- Local deposition of 4 different materials using original Direct Atomic Layer Processing (DALP®) method.
- Definition of user needs in the domains of monitoring personal exposure to gases and breath analysis for fitness and stress estimation.