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.
