From Research to Application: Projects at Fraunhofer EMFT

Diabetes types that require regular insulin treatment are highly dependent on the patient's ability to constantly track their blood glucose levels and self-administer insulin accordingly. This not only causes a major inconvenience in everyday life, but also involves the risk of hyperglycaemia or hypoglycaemia. In addition, a blood sugar deficiency can only be predicted inadequately in certain situations. Together with five European partners, researchers at the Fraunhofer EMFT are developing an implantable measuring and dosing system. It is capable of monitoring all relevant key metabolic parameters on a situation-specific basis and adjusting the insulin dosage accordingly.

In patient surveys on insulin dosing, patch pumps score significantly better than conventional insulin injections in terms of comfort and user-friendliness. They are easy to use, comfortable to wear, and do not need to be removed for sports, showering, etc. For safe and comfortable handling, it is essential that the patch pump doses the insulin precisely and evenly. The micropumps developed at Fraunhofer EMFT achieve dosing deviations of less than 4% even with the smallest volumes. Researchers at the institute were able to verify this in a study.

Even in today's innovative, miniaturized electronic products, traditional wiring is still prevalent. The problem: manual wiring can account for up to 80% of product costs and also has an impact on reliability, production yield, weight and resource consumption. Last but not least, it limits the possibilities for further miniaturization. In the HyPerStripes project, researchers at Fraunhofer EMFT are working with 16 partners from three European countries on a promising alternative: long, flexible electronic systems (“hyperstripes”) to replace conventional cables and printed circuit boards.

The APECS pilot line aims to be Europe’s leading platform for advanced packaging and heterogeneous integration, driving next-generation integrated systems.

Move2THz is an EU-funded research project to develop an innovative InP-on-silicon platform for energy-efficient sub-THz semiconductor technology. The aim is to strengthen Europe's technological leadership in 6G, radar, imaging, and photonics.

Whether in automobiles – especially in the context of autonomous driving – or future industrial manufacturing: plugs and electrical connection technologies have a key role to play in digital networking. They are the main interface between machines, control units and data processing systems and so they provide the basis for the functionality, simple handling and reliability of automation technology.

Air filtration systems are considered an effective measure to reduce the risk of infection by SARS-Cov-2 in enclosed spaces. However, the HEPA filters commonly used to date are maintenance-intensive and costly. Researchers at Fraunhofer EMFT, together with Helmholtz Zentrum München, ams OSRAM International GmbH and MANN+HUMMEL GmbH are developing self-sterilizing filter elements using integrated LED chips to clean the air.

In the EU project SusFe, Fraunhofer EMFT is working with European partners to develop an efficient and resource-saving design and production platform for medical devices. The aim is to meet both the growing demand for medical devices as well as the aspect of sustainability.

A birth is a miracle of nature - but also a highly vulnerable stage in the lives of mother and baby. In the EU project Newlife, researchers from 6 European countries are jointly working on innovative sensor-based monitoring solutions to ensure the well-being of mother and child during pregnancy as well as during and after birth. The aim is to digitally revolutionize paediatric and childbirth care.

Gear oils make a significant contribution to minimising friction and thus ensure the safe operation of production facilities. In the Smart Gear project, researchers at Fraunhofer EMFT are developing solutions to detect a drop in the performance of gear oils with the help of sensor technology and machine learning methods and to predict when the oil needs to be changed.