Fraunhofer-Publica

The Fraunhofer-Publica has been successfully documenting the research results of the Fraunhofer-Gesellschaft for over 30 years. The platform enables the collaborative linking of research-relevant objects and disseminates within the international scientific community.

The Fraunhofer-Publica thus fulfils its responsibility to promote the transfer of knowledge and know-how to industry and society.

Categories

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Research outputs

As an application-oriented research organisation, Fraunhofer aims to conduct highly innovative and solution-oriented research - for the benefit of society and to strengthen the German and European economy.

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Projects

Fraunhofer is tackling the current challenges facing industry head on. By pooling their expertise and involving industrial partners at an early stage, the Fraunhofer Institutes involved in the projects aim to turn original scientific ideas into marketable products as quickly as possible.

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Researchers

Scientific achievement and practical relevance are not opposites - at Fraunhofer they are mutually dependent. Thanks to the close organisational links between Fraunhofer Institutes and universities, science at Fraunhofer is conducted at an internationally first-class level.

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Institutes

The Fraunhofer-Gesellschaft is the leading organisation for applied research in Europe. Institutes and research facilities work under its umbrella at various locations throughout Germany.

Recent Additions

  • Publication
    Catalyst Interaction in Unitized Regenerative Fuel Cells
    ( 2024-05)
    Maletzko, Annabelle orcid-logo
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    Gomez Villa, Eduardo Daniel
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    Kintzel, Birgit
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    Fietzek, Harald
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    Schmidt, Michael G.
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    Christen, J. Andrés
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    Veit, P.
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    Kühne, P.
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    Bornet, Aline
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    Arenz, Matthias
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    Melke, Julia
    Unitized regenerative fuel cells have emerged as promising energy conversion and storage systems for various applications. However, in order to optimize their efficiency, it is crucial to enhance the performance of the bifunctional catalyst. This study aims to provide deeper insights into the electrochemical behavior and performance of the bifunctional catalyst. Several electrocatalysts were prepared and evaluated using rotating disc electrode measurements. The primary focus was placed on investigating the interaction between Pt, Ir, and the support material, antimony doped tin oxide (ATO), and their impact on the oxygen evolution reaction and oxygen reduction reaction. Among the analyzed catalysts, Pt black mixed with synthesized IrO2 supported on developed ATO exhibited the highest performance, considering the results from both the fuel cell and electrolyzer systems.
  • Publication
    Mechanistic effect modelling
    ( 2024-05-23)
    Hommen, Udo
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    Charles, Sandrine
    ;
    Preuss, Thomas G.
  • Publication
    Concept-Guided LLM Agents for Human-AI Safety Codesign
    ( 2024)
    Geissler, Florian
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    Roscher, Karsten
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    Trapp, Mario
    Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. This also leads to high quality requirements for generative AI. Therefore, the simplistic use of Large Language Models (LLMs) alone will not meet these quality demands. It is crucial to develop more advanced and sophisticated approaches that can effectively address the complexities and safety concerns of software systems. Ultimately, humans must understand and take responsibility for the suggestions provided by generative AI to ensure system safety. To this end, we present an efficient, hybrid strategy to leverage LLMs for safety analysis and Human-AI codesign. In particular, we develop a customized LLM agent that uses elements of prompt engineering, heuristic reasoning, and retrieval-augmented generation to solve tasks associated with predefined safety concepts, in interaction with a system model graph. The reasoning is guided by a cascade of micro-decisions that help preserve structured information. We further suggest a graph verbalization which acts as an intermediate representation of the system model to facilitate LLM-graph interactions. Selected pairs of prompts and responses relevant for safety analytics illustrate our method for the use case of a simplified automated driving system.

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