Engineered Disorder in Nanostructured Materials: Coupling Experiment and Simulation
Third party funded individual grant
Start date :
01.10.2021
End date :
30.09.2024
Extension date:
30.09.2025
Project details
Scientific Abstract
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.
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