Project Details
Projekt
Tailoring of BZCY by a Combined Synthesis and Processing Approach using Field Assisted Sintering (FAST/SPS) and Ultra-Fast High Temperature Sintering (UHS)
Applicant
Professor Dr.-Ing. Martin Bram
Subject Area
Materials in Sintering Processes and Generative Manufacturing Processes
Term
since 2026
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 556363981
Proton-conducting ceramics based on Ba(Zr,Ce,Y)O3 (BZCY) are among the most promising electrolyte materials for protonic ceramic electrolysis and fuel cells (PCEC/PCFC). High Zr contents and targeted acceptor doping are required to achieve the best compromise in terms of good electrochemical performance and high chemical stability. So far, the various possibilities of the chemical composition of BZCY ceramics have mainly been investigated by trial-and-error experiments using established sintering techniques. The present project aims to demonstrate the potential of ultrafast high-temperature sintering (UHS) as a novel method to systematically investigate the influence of acceptor dopants on the microstructure and functional properties of BZCY ceramics. Y3+, Yb3+, Al3+, Sc3+, and In3+ were selected as acceptor elements for the "decoration" of the grain boundaries. As the use of rapid sintering techniques such as UHS is associated with a high risk (e.g., crack formation), field-assisted sintering technology/spark plasma sintering (FAST/SPS) is used as an alternative sintering method. FAST/SPS is used to produce reference samples whose sintering parameters can be better controlled. The additionally applied pressure also makes it possible to minimize the residual porosity, which eliminates a critical influencing factor on the functional properties. As part of the research group, the project contributes significantly to the generation of a combinatorial process-defect-microstructure-property database (P-D-M-P), which forms the basis for comprehensive modelling in the other subprojects. The main aim of the modelling is to predict the relationship between the sintering parameters and the functional properties by using data-driven machine learning concepts. In turn, the application of these learning concepts is expected to reduce the experimental effort for the further development of complex alloyed ceramics in the long term.
DFG Programme
Research Units
