Engineered Disorder in Nanostructured Materials: Coupling Experiment and Simulation

• Applicant: Professor Dr. Michael Engel, since 11/2022
• Subject Area: Thermodynamics and Kinetics as well as Properties of Phases and Microstructure of Materials ; Synthesis and Properties of Functional Materials ; Mechanical Properties of Metallic Materials and their Microstructural Origins
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 452477982

The efficiency of nanocrystals as heterogeneous catalysts depends crucially on the structure of the material exposed to the environment. Surface structure is a direct function of chemical composition but can also be modified by varying the bulk microstructure of the nanocrystals. Exploiting this microstructure dependency requires understanding the relationship between atom-pair interactions, local deformation of the crystalline structure, and the resulting long-range lattice distortion.This project aims to tackle the lack of accurate knowledge of disorder in nanostructured materials. Such disorder is a key factor for enhancing both the performance and the durability of nanocatalysts. Disorder in nanomaterials will be characterized by coupling experiment and simulation. On the experimental side, powder scattering methods are powerful techniques to resolve lattice distortion. Atomistic simulation accurately correlates lattice distortion with chemical-physical properties. While these methods are well-established for single-component nanocrystals, their application to multi-component nanomaterials requires the advancement of powder diffraction line profile analysis as well as more reliable statistical sampling. Indeed, analysis of powder scattering data is currently limited by the fact that neither Bragg profiles nor the pair distribution function capture the interplay of short-range and long-range disorder. Likewise, simulations cannot yet resolve the variability of particle populations in large powder samples.We introduce a new analysis method for the characterization of structural disorder in nanomaterials that directly couples to atomistic simulation. We achieve this coupling via artificial intelligence methods, such as particle swarm optimization and pattern recognition algorithms. Our approach overcomes the tedious development of ad hoc disorder models for a number of important microstructure architectures. We focus on multi-component metallic nanocrystals across a broad design space of the process parameters elemental composition, size, and microstructure. Interfaces between components are usually obtained by epitaxial growth of a precursor nanocrystal. We aim to discover how the disorder evolution at these interfaces affects growth kinetics, order-disorder phase transitions, chemical stability, and durability as a heterogeneous catalyst. Our results will open new pathways to optimize chemical activity and selectivity of nanocatalysts.

• DFG Programme: Research Grants
• Ehemaliger Antragsteller: Alberto Leonardi, Ph.D., until 11/2022