Functional Materials and Film Systems for Efficient Energy Conversion (FuSion)

Within only a couple of years the ubiquity of miniaturized computing devices and integrated sensors has become an established part of our daily life. Within only 5 years, from 2007 to 2012, the number of sensors in mobile devices like cell phones or cameras experienced a tremendous increase from 10 million to 3.5 billion.* Internet of Things including Industry 4.0, Smart Wearables, Smart Home, Smart Environment and more not only significantly further increased these numbers up to now but is expected to lead to an explosion of required computing power and embedded sensors in the future. This puts high requirements on energy supply and efficient consumption. Remote smart sensors need novel types of batteries, energy harvesting systems and have to be highly energy efficient in operation. Energy efficient sensing and computing requires novel technologies and approaches for virtually every component of smart sensors. This comprises system and processor architecture, sensing and computing algorithms, batteries and energy harvesting as well as energy conversion.

The Graduate Research School Cluster FuSion aggregates and focuses research activities at BTU Cottbus – Senftenberg and selected non-university research organizations with the objective to gain a deeper, interdisciplinary-based understanding of materials, processes and film systems supporting novel approaches and solutions for efficient energy devices. The PhD students will be integrated in an unique and highly interdisciplinary environment. The participating scientists combine experimental and theoretical large-scale and internationally highly acknowledged expertise in physics, chemistry, material science, electrical engineering, process engineering and application engineering. Combination of basic research oriented university chairs and application oriented non-university research institutes provide a broad and high quality research environment comprising e. g. availability of up-to-date process equipment in academic and professional environments and a huge manifold of high-quality characterization methods.

*TSensors Roadmap, Stanford University 2013,

This website uses cookies. There are two types of cookies: The first type supports the basic functionality of our website. The second allows us to improve our content for you by saving and analyzing pseudonymised user data. Since this second type is technically not required to run the website, you can withdraw your consent to those cookies at any time. For more information please visit our pages on data protection.


These cookies are needed for a smooth operation of our website.


For statistical reasons, we use the platform Matomo to analyse the user flow with the help of website users‘ pseudonymised data. This allows us to optimize website content.

Name Purpose Lifetime Type Provider
_pk_id Used to store a few details about the user such as the unique visitor ID. 13 months HTML Matomo
_pk_ref Used to store the attribution information, the referrer initially used to visit the website. 6 months HTML Matomo
_pk_ses Short lived cookie used to temporarily store data for the visit. 30 minutes HTML Matomo