In the field of thermal plasmas, our current focus is on plasma spraying processes and investigations into small-scale arc plasmas. On the one hand, the optimization of applications is in the foreground, while on the other hand, fundamental investigations are also being carried out, such as the interaction of plasmas with the electrodes. The models we have developed enable a consistent non-equilibrium description of plasma and the boundary layer in thermal plasmas.

Our research into non-thermal plasmas primarily covers barrier discharges, plasma ion sources and plasma jets. The focus is on studies to analyze plasma-chemical processes, the interaction of plasmas with surfaces and the optimization of plasma sources for different applications. Our models enable a time-dependent and spatially multidimensional description and detailed analysis of plasma-physical and reaction-kinetic effects.

At INP, we are dedicated to data and knowledge management in order to advance research and development in plasma research using modern methods and to support the use of data-driven methods. We develop effective tools and methods for the structured collection, documentation, and reuse of research data. Our goal is to integrate the FAIR principles – that is, research results should be findable, accessible, interoperable, and reusable – into daily practice. By using modern technologies and data management solutions, we enable the efficient analysis and use of data and technical information, which is crucial for the further development of data-driven methods and innovative plasma applications. We operate the interdisciplinary plasma technology data platform INPDAT for the publication and reuse of our plasma sources, patents and associated research data.

We use artificial intelligence (AI) and machine learning to advance research and development in the field of plasma and technological processes. By developing intelligent models and algorithms, we improve the efficiency of simulations, create new simulation approaches that integrate data and models, and support data analysis. This is crucial to being able to simulate and better understand complex physical phenomena.

Non-thermal plasmas are utilised across various applications due to their effective generation of reactive species through transient discharges, with dielectric barrier discharge (DBD) being a common method for plasma generation. The interaction between plasma and surface is crucial, making it essential to thoroughly understand how dielectrics influence plasma properties for the enhancement of applications in fields such as medicine, agriculture, and air purification. This project proposes a novel approach to data-driven diagnostics by integrating experimental and simulation data into a unified database, enabling systematic exploration of complex surface effects to address critical research questions. The database ensures consistent data formats for all measured and calculated discharge parameters, facilitating direct linking between experimental and simulation data. Through systematic data analysis, the project investigates how individual surface effects influence various discharge phases of the DBD, exploring their potential for optimising plasma properties alongside established volume effects. The data and metadata formats developed will contribute to enhancing the accessibility and reusability of research data, addressing a prominent challenge within the scientific community.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 535827833, https://gepris.dfg.de/gepris/projekt/535827833.

Analysis of mechanisms of ambient-pressure PECVD (plasma-enhanced chemical vapour deposition) using DBDs with short gas-residence times, particularly single-filament DBDs as well as plasma-sheet DBDs, in mixtures of argon with addition of small amounts of different molecular gases including HMDS and TMS as well as hydrocarbons (HCs), such as CH4, C2H2, C2H4 and C2H6. The main topics of the project include: role of ions in DBD-based PECVD from Ar-monomer mixtures; study of thin films grown by DBD-PECVD, i.e. composition, structure, properties; development of complex plasma-chemical models for DBDs in Ar with admixture of HCs or Si-containing precursors; development and execution of 1d-t or 2d-t simulations.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 504701852, https://gepris.dfg.de/gepris/projekt/504701852.

Partners: Institute for Surface Technology, Technische Universität Braunschweig

In spite of many efforts, open access for scientific publications has still not become fully established. One major reason for this is presumably that researchers in cutting-edge research often do not directly experience the immediate benefits of open access publication: access to paid literature is not a major obstacle for members of many research groups. However, since the open access transformation can only succeed with researchers, the Mehr-OA project relies on their intrinsic motivation by presenting the benefits of open access for their own research based on a specific use case: Researchers want to get answers to research questions, receive overviews of the state of research in a particular field, and obtain evaluations and analyses of the considerable number of publications. The Mehr-OA project is making a significant contribution to this by developing methods for efficient machine-based search and post-processing of open access content and testing them using the case of plasma research. To achieve this, modern machine learning methods are used to automatically extract content from centrally stored open access publications in the subject repository for natural sciences and technology (RENATE) and make it publicly available in structured form in the Open Research Knowledge Graph (ORKG).

Funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), project numbers 16KOA013A, 16KOA013B, https://www.plasma-mds.org/project-mehr-oa.html.

Partners: TIB – Leibniz Information Centre for Science and Technoloy

In recent years, low-temperature plasmas have found wide technological applications, such as chemical processing and surface modification, biomedical applications, and flow control. Understanding the underlying physical and chemical processes relevant to plasma production is therefore of great interest. A common way to gain a deeper understanding of these processes is through numerical modelling and simulations. An accurate description of the plasma and the processes that occur during its production requires the consideration of many particle species and the reactions that occur between them, spanning over long temporal and spatial scales. This often results in a large system of partial differential equations (PDEs), whose solving can take days, weeks, or in some cases even months. Optimising the solution procedure for these equations is therefore of great importance. Recent developments in the field of machine learning (ML) and artificial neural networks provide a great basis for the combination of conventional numerical techniques and modern ML methods to achieve this. The aim of the project is to introduce junior researchers from two partner institutions to the field of plasma modelling, numerical solution of the PDEs and machine learning (ML), establishing a basis for further collaboration and joint advancement of the current state of research.

Funded by the Deutscher Akademischer Austauschdienst e.V. (DAAD), project number 57703239.

Partners: Faculty of Sciences and Mathematics, University of Niš

NFDI4BIOIMAGE is a consortium of the National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) in Germany. A consortium within the NFDI comprises legally independent partner institutions working together with the goal to advance the capacity and the capability of researchers in Germany and beyond to professionally handle, store, annotate, share, and reuse research data. Our focus is on all steps of the research data life cycle for microscopy and bioimage analysis. INP participates in NFDI4BIOIMAGE with research data management workflows for bioimaging in plasma medicine and beyond.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 501864659, https://gepris.dfg.de/gepris/projekt/501864659.

Partners: NFDI4BIOIMAGE consortium, https://nfdi4bioimage.de

The Confine project is aimed to investigate the influence of short-lived species generated by discharges in small volumes on the coupling of plasma and catalytic processes. Due to the complex chemistry in gas mixtures intended a plug flow model was established which includes the volume and surface reactions, the electron energy balance and the heat equation. In a first step a mixture of helium and water was investigated and validated by LIF measurements of OH number density by project partners at the University of Minnesota.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 509169873, https://gepris.dfg.de/gepris/projekt/509169873.   

Partners: Department of Mechanical Engineering, University of Minnesota; Institut für Chemische Reaktionstechnik, Technische Universität Hamburg

Patents encapsulate a significant portion of humanity's technical knowledge, presenting invaluable insights into innovative solutions, key substances, methods, and processes that can address pressing scientific and technological queries. Despite their potential, research indicates that this treasure trove of information goes largely untapped in scientific contexts, primarily due to scientists' challenges in navigating complex patent structures and a lack of proper analytical tools. The existing solutions provide limited access and fail to incorporate semantic descriptions, hindering connections between patent data and scientific literature. To tackle these challenges, we are developing the Patents4Science-Information Infrastructure (P4SI), aimed at creating an intuitive and sustainable pathway to patent knowledge. By employing automatic data analysis and semantic linking with extensive knowledge bases, our project semantically enriches, indexes, and interconnects patent content with scientific literature, culminating in the creation of a patent-centric knowledge graph. Aligned with the Linked Open Data (LOD) and FAIR data principles, our innovative framework does not only broaden the scope of knowledge in the sciences but will also expand to include other research domains and collaborate with key initiatives like NFDI, GAIA-X, and EOSC in the future.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 496963457, https://gepris.dfg.de/gepris/projekt/496963457.

Partners: FIZ Karlsruhe – Leibniz Institute for Information Infrastructure; Leibniz-Institut für Werkstofforientierte Technologien – IWT; INM – Leibniz-Institute for New Materials gGmbH

The project aims to describe and understand low-current arcs (0.5 to 20 A) in low-voltage DC switchgears, which are increasingly important for applications like electro mobility and local grids with photovoltaic systems and batteries. Arc switching devices offer low-cost solutions combined with semiconducting devices in hybrid switchgears due to their ability to provide galvanic separation and high insulation levels. Despite decades of use, the fundamental understanding of physical processes in low-current switching arcs remains incomplete, particularly regarding unexpected behaviours of arc voltage and radiation in short gap lengths. The transfer of electric current from non-refractory cathodes, like copper alloys, to low-current arcs at atmospheric pressure poses theoretical challenges due to low thermionic electron emission. This project will tackle these issues through experimental and theoretical studies, focusing on arcs between flat cylindrical electrodes in ambient air. The investigation will evaluate the voltage-current characteristics of the breaking process, spatial arc structure, and the expected role of metal vapor and non-equilibrium conditions. A model switch will be established, employing high-speed imaging, tomography, and spectroscopy, alongside numerical simulations to create a comprehensive understanding of current transfer and arc attachment at non-refractory cathodes. The goal is to provide a self-consistent description that can explain the current-voltage characteristics and inform on switching performance, electrode erosion, and the longevity of low-voltage switching devices.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 524731006, https://gepris.dfg.de/gepris/projekt/524731006.

Partners: Institute of Electrical Power Engineering, University of Rostock