Neuromorphic computing will revolutionize artificial intelligence for applications such as autonomous driving, smart diagnosis or natural-language understanding by emulating the operation of efficient biological neural networks. The main challenge in this field is the substitution of conventional transistors for synaptic transistors able to learn in ways similar to a neural synapse, i.e. the development of multistate non-volatile transistors. However, currently existing synaptic transistors have been developed using electrolytes that are by nature unstable and difficult to integrate such as ionic liquids or proton conducting polymers. TRANSIONICS will deliver highly stable (non-volatile), silicon-compatible and scalable solid state synaptic transistors by exploiting the first-ever room temperature oxide-ionic electrolyte developed in the ERC CoG grant (ULTRASOFC) held by the PI. TRANSIONICS transistors are able to modulate its channel properties with external stimulus like real neurons by reducing/oxidizing a mixed ionic-electronic conductor unveiled at ULTRASOFC. Additionally, TRANSIONICS is compatible with mainstream microelectronics fabrication technology, which makes it ideal for developing high density brain-like computer chips. The goals of the TRANSIONICS project are i) to evaluate the technical feasibility for the fabrication of unique all-solid-oxide synaptic transistors with lateral architecture; ii) to assess the silicon- compatibility and scalability of the TRANSIONICS transistors; iii) to define an IPR strategy for technology transfer; iv) to build a value proposition for a startup company and to identify customer segments with industrial partners. To achieve these goals, the PI has joined around the project team that combines applied research, technology transfer and market uptake expertise.

Microscale thin-film-based solid oxide fuel cells (μSOFCs) are an emerging alternative for portable power supply due to their high efficiency, fuel flexibility and high volumetric and specific power densities. Promising cathode materials for μSOFCs are perovskite lanthanum-based oxide materials which have improved oxygen transport properties and resistance to the high operating temperatures. However, the physicochemical factors influencing the performance of these materials are yet to be well understood. The SmartOptoelectronics project will develop machine learning (ML) methods to establish trends between the structural properties and the electrochemical performance of perovskite lanthanum-based oxides based on high-throughput experimental data. These techniques will be used to explore the chemical space of lanthanum-based oxides with the goal of undestanding and designing lanthanum-based materials with enhanced performances for μSOFC applications. Machine learning methods will be validated in three main steps: (1) deriving structure-property relationships in lanthanum-based oxides from spectroelectrochemical data of combinatorial ternary and quaternary maps; (2) demonstrating new lanthanum-based oxides with enhanced electrochemical properties and performance; (3) optimising the operation of devices based on top-performing materials with operando monitoring of spectroelectrochemical properties. The project will have a high impact on the work programme and on the candidate’s skills and future prospects by developping an expertise in machine learning and large scale clean energy conversion devices, which are Key Enabling Technologies in Horizon Europe and complement her background in spectroelectrochmistry of multi-redox catalytic materials. The project will also re-enforcing the candidate’s transferrable skills and technology transfer competence as part of the KIC Innoenergy community and the clean energy R&D&I sector.

The digitalization of the world and the rise of the Big-Data era continuously push towards improved computing capabilities able to sustain new and complex functionalities. Traditional computer architectures based on the Von Neumann model present limitations when dealing with rapidly growing technologies such artificial intelligence, since to low access speed between the separated memory and CPU inevitably leads to low processing capabilities and high energy consumptions. A possible solution to overcome these limitations is the development of non-volatile memories (NVMs) as element with both storage and logic capabilities, enabling the computation of tasks directly on the memory unit (in-memory computing). MagnOxy aims at developing a novel solid state battery-like NVM based on the control of magnetic properties of functional oxide thin films through a fast (de)intercalation of oxygen ions. The control of magnetism through the reversible (de)intercalation of ions into a target material, mediated by the application of an electrical bias through an ionic electrolyte (i.e. magneto-ionics, MI) is the most recent approach for controlling magnetism by voltage. MI-based magneto-electric random-access memories (MeRAM) have the potential to deliver robust, fast and energy efficient NVMs for in-memory computing. However, MI control is still at an early stage of the R&D process and many aspects still needs to be improved to deliver a competitive device. In essence, MagnOxy will provide: i) A solid state thin film device based on the electrochemical insertion of oxygen ions at RT ii) A large and analogically tuneable magnetic response of the cell ii) A fast (~100 ms) magnetic switching capabilities through the implementation of nanoionics concept for boosting ionic motion iii) An improved understanding of the oxygen-ion intercalation mechanisms in oxide perovskite thin films iv) Device scalability and miniaturization.

The main goal of the 3D-PRESS project is to advance in the 3D printing concepts for safer, cheaper and customizable all-solid state Li-ion batteries (LIB). More specifically, the project is focused on the design, production, characterization and testing of 3D printed NASICON-type glass-based electrolytes for 3D printed batteries. In 3D-PRESS, glass-based compositions will be designed and synthesized in order to obtain printable glass-based electrolytes with superior conductivity and functional properties. The produced glasses will be thermally and electrochemically characterized in order to investigate their sinter-crystallization behaviour (tailoring suitable sintering treatments) and electrochemical performances. The most promising electrolyte compositions will be selected to be printed in free-form robust self-standing structures in order to obtain 3D batteries with high active area (allowing high specific energy and power per unit volume). 3D-PRESS represents a cutting edge multidisciplinary approach for the development of reliable and customizable all-solid state 3D LIBs, especially interesting for micro-power applications such as the ones for Internet of Things (IoT). The project will provide a new family of printable materials increasing the short list of available compositions, especially solid electrolytes, opening the door to the development of a new generation of fully printable all-solid state 3D LIBs. A high impact on the future career of the candidate is expected by complementing his current background with new skills in one of the more relevant Key Enabling Technologies (KETs), 3D-printing, applied to the crucial field of the Energy Storage. Moreover, the host institute will offer unique opportunities to re-enforce the technology transfer competences of the candidate by carrying out an industrial secondment and by the involvement in the KIC Innoenergy community.