The European Industry is currently facing growing number of challenges and the status quo is not acceptable as a sustainable solution. The two main trends of Green and Digital transformation is a strategic call for action for all individual companies, but more and more also between them. The trends of shortening of value chains not only from technological and data-use point of view, but also from de-internationalization perspective became very urgent as recovery measures after the Covid-19 pandemics and will be considered as a potential driver for future resilience building strategies. At the same time, the existing workforce will need to accept and adopt the proposed changes as a new opportunity for sustainable growth, which shall be supported by development of new skills and largely available re-skilling. These and other aspects will be promoted and opinions from industrial leaders collected at the next pan-European conference on Industrial Technologies in Grenoble 2022, France. The feedback will be provided for further policy work on European, national and regional level.

Decarbonizing industry is the key challenge for 2050 to achieve the Net Zero ambitions. For this, one of the current pathways is the massive electrification of industrial processes in demand for low-temperature heat (up to 150°C) thanks to heat pumps. However, the electricity distribution companies have recently issued an alert: the grid development is insufficient to match the enormous future needs for electricity. In the coming decades, this will thus trigger tensions on the power grid and lead to priorization in the supply of electricity. Therefore, to alleviate electricity demand while meeting industrial needs, it is crucial to provide alternative technology, such as Absorption Heat Transformer (AHT), which has the potential to meet the demand for low temperature heat, from non critical and abundant energy sources such as industrial waste heat, with hardly any electrical use. With this objective of offering another option than vapor compression heat pumps, the ZIMBA project aims to develop and validate at TRL4, an innovative AHT system at 15 kWth and 110°C. It will be based on a novel water ammonia AHT technology improved by a two-phase ejector specifically designed in order to stabilize its performance and widen the range of its operating conditions, in particular under hot conditions. From the physical evaluation results obtained, scaling studies up to 500 kWth will be carried out by the consortium, composed of researchers and industrial manufacturers, in order to push for a design with practically no critical raw materials, easily maintainable and recyclable, and with only commercially available components for fast time to market. The integration of the system in the potential future energy market and the associated business models will be studied in order to ensure a circularity of the energy and to fully exploit the exergy of the heat losses. Thus, ZIMBA will help the EU in reducing its dependence on fossil and electricity, while meeting climate goals.

Europe’s leading position in photonics and electronics can only be secured by adapting to the next generation of optoelectronic devices requirements: high performance, multi-functionality and cost efficiency in miniaturized footprint. These can only be achieved if novel schemes for on-chip integration emerge. Among the established platforms for optoelectronic integrated circuits (OEICs), silicon nitride as a wide-band and low-loss material stands outs. However, Si3N4 itself has no active effect and the heterogeneous integration of active III-V and II-VI semiconductor chips is currently very complicated and costly. MatEl offers a unique solution to this challenge and promises to enable a novel on-chip integration scheme: Laser Digital Processing - Laser Transfer and Laser Soldering - will be employed for the accurate and fast alignment and bonding of any type of chip package (OEIC) on Si3N4. The hybrid platform will be enhanced bythe monolithic integration of advanced materials (graphene and high-quality PZT), which will enable multiple functionalities in miniaturized footprint. MatEl will thus demonstrate two next-gen optoelectronic devices at TRL5: 1) 2D light source for AR displays with integrated RGB lasers and OEIC-based demultiplexers. 2) Bio-photonic sensor for antibodies detection with on-chip VCSEL at 850 nm featuring graphene photodetectors. Overall, MatEl ’s hybrid platform will combine the wide bandwidth and high confinement provided by Si3N4 with the active functionality of III-V and II-VI lasers, supporting a broad spectrum of next-gen applications, extending far beyond these demo applications. Hence, MatEl will reinforce the existing collaborations within the consortium and introduce new eco-systems, estimated to strengthen the EU photonics and microelectronics industrial capability by generating multi M€ turnovers to the involved SMEs and more than 200 new employment positions by the end of its timeframe.

Nuclear magnetic resonance (NMR) spectroscopy is the workhorse of modern molecular structural analysis with countless scientific applications, from materials science to drug discovery. Nevertheless, even the most modern NMR spectrometers still employ the same principles as 80 years ago, induction coils and high magnetic fields, making them bulky, expensive, and inaccessible to many potential users. However, a novel type of NMR sensor emerged recently from solid-state spin quantum systems: the nitrogen-vacancy (NV) center in diamond, which has demonstrated unparalleled sensitivities in detecting NMR signals. In this proposal, we aim to significantly enhance the sensitivity of modern benchtop NMR spectrometers by several orders of magnitude. We will achieve this improvement by combining the NMR technology field with cutting-edge quantum sensing, employing improved NV-diamond materials, advanced microwave antennas, novel pulse sequences, and quantum control protocols. The goal is to achieve complete control and protection from the environmental noise of the NV-spin state, incorporating quantum memories and logical operations to reach radiofrequency sensitivities well beyond those of classical NMR sensors. The quantum-enhanced benchtop NMR spectrometer will be applied and validated in an analytical chemistry lab environment to demonstrate record sensitivities in molecular analysis enabled by quantum technology, with potential applications in quality control, environmental monitoring, medical diagnostics, online monitoring of chemical reactors, and materials discovery.

The rise of technologies such as the Internet of Things (IoT), autonomous vehicles, smart cameras, etc. is generating lots of big data. The volume of data in 2022 was 97ZB and is doubling every 2-3 years. This is leading to unprecedented growth in energy consumption and costs needed for data processing. Sending raw data for remote processing on centralized nodes is limited in terms of speed and bandwidth, and even next-gen tech like 5G or 6G will be insufficient to cope with this growth. Processing data at the Edge, where it's generated, requires increasing power efficiency by several orders of magnitude. However, the use of general-purpose digital processors based on von Neumann architecture is limited, with optimization possibilities nearing natural limits. A new class of chips, neuromorphic hardware, is needed to execute AI algorithms like Deep Learning at high speed, low energy consumption, endurance, and scalability. MultiSpin.AI’s vision is to improve neuromorphic computing by increasing the energy efficiency and processing speed by at least three orders of magnitude over digital computing and >10x compared to the most advanced neuromorphic devices to reach an unparalleled 2,000 Tera operations per second per watt (TOPS/W). To achieve this, MultiSpin.AI will develop an AI co-processor based on a crossbar of multi-level magnetic tunnel junctions (M2TJ) cells/ n-ary state cells. The use of multi-level M2TJs reduces the number of cells, simplifies circuity, and reduces the number of digital-to-analog conversions (DAC) at the input of the crossbar, and analog-to-digital conversions at the crossbar output. The combined effect is realising much higher energy efficiency and faster AI inference at the Edge. This breakthrough will help provide a significant impact by enabling transformative applications like autonomous vehicles, robots, and medical devices and help strengthen strategic autonomy for the EU chips industry and reduce CO2 emissions from AI inference.