The new partner (UNG) will bring fresh perspectives as well as invaluable expertise in device modelling and charge transport characterization that will significantly enhance the progress of the project. Their deep knowledge of advanced experimental characterization methods will enable us to explore innovative, environmentally friendly materials that are critical for our organic transistors. Moreover, they will provide essential information on the intrinsic material properties, such as charge transport characteristics and ferroelectric behaviour, which are vital for optimizing device performance in terms of response, stability and reproducibility. By collaborating closely, we will be able to leverage their cutting-edge research methodologies and access to state-of-the-art facilities, fostering a dynamic exchange of ideas that will accelerate our development timeline and secure the successful progress of our project. Developing a theoretical model for organic transistors with ferroelectric interlayers is set to surpass current knowledge by providing creative insights into charge transport and nonlinear dynamics in these systems. This innovative framework will drive experimental research in leveraging novel, environmentally friendly materials that minimize ecological impact while optimizing device performance. By facilitating the design of advanced multimodal applications, such as next-generation pressure sensing and mimicking synapses, this model will significantly advance the design of fabrication of artificial sensory neuron within this project. Broader, the model will advance development of devices that emulate biological processes. Additionally, it will encourage interdisciplinary collaboration among material scientists and computational physicists, enrich educational resources, and equip the next generation of researchers with cutting-edge skills, ultimately inspiring transformative concepts that could redefine the future of sustainable technologies.

The replication of the circle of information coming from the environment, to the skin, to an action mediated by the brain, requires a lot of advances in smart technology and materials development. Embedding sensors in smart architectures that record the stimulus from the environment and transform it into action is the objective of artificial skins. At the moment, different sensors have to be implemented in the artificial skin matrix for each stimulus. The goal of this project is to develop a single multi-stimuli responsive material, which would allow a simplification of the artificial skin and enable unprecedented spatial resolution. The material will be comprised of a smart core, responsive to temperature and humidity, and a piezoelectric shell for pressure sensing. The swelling of the smart core upon stimuli will be sensed by the piezoelectric shell and produce a measurable potential. This architecture will be achieved thanks to the use of novel vapor-based technologies for material processing that allow fabrication at the nanoscale. The advantage of using a dry, vapor-based, polymerization for the smart core is that it will be possible to cumulate different functionalities and engineered composition gradients, which are difficult to obtain by conventional synthesis. Nano-structuration of such materials in core-shell site-specific arrays will allow to create a sensing network with spatial resolution down to 1mm and lower. The network will respond to the stimuli coming from the environment and recognize them in terms of location and type of stimuli. The successful execution of the SmartCore project will have a strong impact in the design and production of future structures, with consequences in sensoring, biotechnology and tissue engineering.

Face recognition has become a key technology in our society, frequently used in multiple applications, while creating an impact in terms of privacy. As face recognition solutions based on machine learning are becoming popular it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society. In this project, we focus on face recognition technologies based on artificial intelligence and the analysis of the influencing factors relevant for the final decision as an essential step to understand and improve the underlying processes involved. The scientific approach pursued in the project is designed in such a way that it will be applicable to other use cases such as object detection and pattern recognition tasks in a wider set of applications. One of the original aspects of the proposed project is in its scientific approach which targets explaining how machine learning solutions reach effective face recognition by identifying and analyzing the influencing factors that play an important role in the performance of face recognition in the end-to-end workflow, and their impact on the system’s decisions. In fact, such performance largely depends on the acquisition, enhancement, compression, analysis and decision making processes adopted in the workflow of a face recognition solution. Machine learning is currently used in many of the stages of such a workflow and various factors such as the dataset used in the training process, the approach used for training itself, the architecture of machine learning, and the types of attacks and interferences are among influencing factors that contribute to the understanding and explainability of the complete system.

Diabetes is one of the main health risks today with near pandemic dimension, causing blindness, kidney failures, stroke, heart attack, giving rise to very high health care costs (25% in the US) and reducing the quality of life of around 500 million people worldwide. The level of hemoglobin A1c (HbA1c) is used to assess long-term glycemic control and is the best predictor for the risk of developing chronic complication of diabetes and appropriate follow up of the patients. The golden standard is an expert laboratory HPLC method focusing on HbA1c quantification, which has limitations when other relevant hemoglobin variants are to be detected. For approximately 7 % of the world’s population which are carriers of such hemoglobin variants current methods lead to under-, over- or non-estimation of the HbA1c fraction. VortexLC will not only improve the quality of the analysis to give an instant full picture of the health status of diabetes patients, it will also produce a cheap point of care device. The use of vortex flows renders the approach compatible with mass manufacturing of plastic pillar array columns, that are not only much cheaper than commonly used packed bed columns, but wherein also higher separation performances can be obtained. The polymer columns will be fabricated using UV-nanoimprint lithography, plasma technology to make them porous and add a chromatographic coating, and lamination to close the column, all processes that can be scaled to roll-to-roll industrial manufacturing. The columns will be embedded in an instrument that allows for integrated sample preparation and miniaturized UV absorption and SERS detection, allowing for both quantification and identification of analytes. In the project a low footprint demonstrator of the novel system and columns will be built and tested with first synthetic, next human blood samples to quantify Hb1Ac and its genetic variants.