Anisotropic atom displacement in Pd nanocubes resolved by molecular dynamics simulations supported by x-ray diffraction imaging

Nearly identical Pd nanocubes yield an x-ray powder diffraction pattern with interference fringes affording access to unprecedented structural details of nanocrystal size, shape, and complex atomic displacement for a billion-sized population. The excellent agreement between diffraction data and molecular dynamics (MD) provides strong experimental validation of MD simulations and the proposed data-interpretation paradigm. These results show that individual atomic displacements within the nanocubes are not only a function of disrupted bonds and the crystallographic plane of the adjacent surface, but are complex strain gradients extending across all surfaces of the particle strongly influenced by atomic displacements. This observation of nonuniform surface strain and the manner in which it is affected by different sizes, shapes, and locations within each facet could be the key to understanding many surface related properties of shaped nanocrystals including those associated with important catalysis applications

Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network

Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.Comment: 23 pages, 6 figures, 3 table

Gas-induced segregation in Pt-Rh alloy nanoparticles observed by in-situ Bragg coherent diffraction imaging

Bimetallic catalysts can undergo segregation or redistribution of the metals driven by oxidizing and reducing environments. Bragg coherent diffraction imaging (BCDI) was used to relate displacement fields to compositional distributions in crystalline Pt-Rh alloy nanoparticles. 3D images of internal composition showed that the radial distribution of compositions reverses partially between the surface shell and the core when gas flow changes between O2 and H2. Our observation suggests that the elemental segregation of nanoparticle catalysts should be highly active during heterogeneous catalysis and can be a controlling factor in synthesis of electrocatalysts. In addition, our study exemplifies applications of BCDI for in situ 3D imaging of internal equilibrium compositions in other bimetallic alloy nanoparticles

Single Alloy Nanoparticle X-Ray Imaging during a Catalytic Reaction

The imaging of active nanoparticles represents a milestone in decoding heterogeneous catalysts dynamics. We report the facet resolved, surface strain state of a single PtRh alloy nanoparticle on SrTiO3 determined by coherent x-ray diffraction imaging under catalytic reaction conditions. Density functional theory calculations allow us to correlate the facet surface strain state to its reaction environment dependent chemical composition. We find that the initially Pt terminated nanoparticle surface gets Rh enriched under CO oxidation reaction conditions. The local composition is facet orientation dependent and the Rh enrichment is non-reversible under subsequent CO reduction. Tracking facet resolved strain and composition under operando conditions is crucial for a rational design of more efficient heterogeneous catalysts with tailored activity, selectivity and lifetime.Comment: 15 pages, 4 figures, 32 reference

Photon shot-noise limited transient absorption soft X-ray spectroscopy at the European XFEL

Femtosecond transient soft X-ray Absorption Spectroscopy (XAS) is a very promising technique that can be employed at X-ray Free Electron Lasers (FELs) to investigate out-of-equilibrium dynamics for material and energy research. Here we present a dedicated setup for soft X-rays available at the Spectroscopy & Coherent Scattering (SCS) instrument at the European X-ray Free Electron Laser (EuXFEL). It consists of a beam-splitting off-axis zone plate (BOZ) used in transmission to create three copies of the incoming beam, which are used to measure the transmitted intensity through the excited and unexcited sample, as well as to monitor the incoming intensity. Since these three intensity signals are detected shot-by-shot and simultaneously, this setup allows normalized shot-by-shot analysis of the transmission. For photon detection, the DSSC imaging detector, which is capable of recording up to 800 images at 4.5 MHz frame rate during the FEL burst, is employed and allows approaching the photon shot-noise limit. We review the setup and its capabilities, as well as the online and offline analysis tools provided to users.Comment: 11 figure

Massive X-ray screening reveals two allosteric drug binding sites of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous health problems and economical challenges for mankind. To date, no effective drug is available to directly treat the disease and prevent virus spreading. In a search for a drug against COVID-19, we have performed a massive X-ray crystallographic screen of repurposing drug libraries containing 5953 individual compounds against the SARS-CoV-2 main protease (Mpro), which is a potent drug target as it is essential for the virus replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds binding to Mpro. In subsequent cell-based viral reduction assays, one peptidomimetic and five non-peptidic compounds showed antiviral activity at non-toxic concentrations. Interestingly, two compounds bind outside the active site to the native dimer interface in close proximity to the S1 binding pocket. Another compound binds in a cleft between the catalytic and dimerization domain of Mpro. Neither binding site is related to the enzymatic active site and both represent attractive targets for drug development against SARS-CoV-2. This X-ray screening approach thus has the potential to help deliver an approved drug on an accelerated time-scale for this and future pandemics