Ab initio structure determination and quantitative disorder analysis on nanoparticles by electron diffraction tomography.
Yaşar KrysiakBastian BartonBernd MarlerReinhard B NederUte KolbPublished in: Acta crystallographica. Section A, Foundations and advances (2018)
Nanoscaled porous materials such as zeolites have attracted substantial attention in industry due to their catalytic activity, and their performance in sorption and separation processes. In order to understand the properties of such materials, current research focuses increasingly on the determination of structural features beyond the averaged crystal structure. Small particle sizes, various types of disorder and intergrown structures render the description of structures at atomic level by standard crystallographic methods difficult. This paper reports the characterization of a strongly disordered zeolite structure, using a combination of electron exit-wave reconstruction, automated diffraction tomography (ADT), crystal disorder modelling and electron diffraction simulations. Zeolite beta was chosen for a proof-of-principle study of the techniques, because it consists of two different intergrown polymorphs that are built from identical layer types but with different stacking sequences. Imaging of the projected inner Coulomb potential of zeolite beta crystals shows the intergrowth of the polymorphs BEA and BEB. The structures of BEA as well as BEB could be extracted from one single ADT data set using direct methods. A ratio for BEA/BEB = 48:52 was determined by comparison of the reconstructed reciprocal space based on ADT data with simulated electron diffraction data for virtual nanocrystals, built with different ratios of BEA/BEB. In this way, it is demonstrated that this smart interplay of the above-mentioned techniques allows the elaboration of the real structures of functional materials in detail - even if they possess a severely disordered structure.
Keyphrases
- electron microscopy
- crystal structure
- high resolution
- electronic health record
- big data
- machine learning
- solar cells
- data analysis
- climate change
- molecularly imprinted
- solid phase extraction
- molecular dynamics
- mass spectrometry
- deep learning
- high throughput
- artificial intelligence
- liquid chromatography
- human health
- highly efficient
- electron transfer
- quantum dots