Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.89 and a root mean square valence error of 0.21 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials

Structure retrieval at atomic resolution in the presence of multiple scattering of the electron probe

The projected electrostatic potential of a thick crystal is reconstructed at atomic-resolution from experimental scanning transmission electron microscopy data recorded using a new generation fast- readout electron camera. This practical and deterministic inversion of the equations encapsulating multiple scattering that were written down by Bethe in 1928 removes the restriction of established methods to ultrathin ($\lesssim 50$ {\AA}) samples. Instruments already coming on-line can overcome the remaining resolution-limiting effects in this method due to finite probe-forming aperture size, spatial incoherence and residual lens aberrations.Comment: 6 pages, 3 figure

The effect of core-shell engineering on the energy product of magnetic nanometals.

Solution-based growth of magnetic FePt-FeCo (core-shell) nanoparticles with a controllable shell thickness has been demonstrated. The transition from spin canting to exchange coupling of FePt-FeCo core-shell nanostructures leads to a 28% increase in the coercivity (12.8 KOe) and a two-fold enhancement in the energy product (9.11 MGOe)

High-Resolution Spectroscopy of Bonding in a Novel BeP2N4 Compound

The recently discovered compound BeP2N4 that crystallizes in the phenakite-type structure has potential application as a high strength optoelectronic material. Therefore, it is important to analyze experimentally the electronic structure, which was done in the present work by monochromated electron energy-loss spectroscopy. The detection of Be is challenging due to its low atomic number and easy removal under electron bombardment. We were able to determine the bonding behavior and coordination of the individual atomic species including Be. This is evident from a good agreement between experimental electron energy-loss near-edge structures of the Be-K-, P-L2,3-, and N-K-edges and density functional theory calculations

Nanoscale mosaicity revealed in peptide microcrystals by scanning electron nanodiffraction.

Changes in lattice structure across sub-regions of protein crystals are challenging to assess when relying on whole crystal measurements. Because of this difficulty, macromolecular structure determination from protein micro and nanocrystals requires assumptions of bulk crystallinity and domain block substructure. Here we map lattice structure across micron size areas of cryogenically preserved three-dimensional peptide crystals using a nano-focused electron beam. This approach produces diffraction from as few as 1500 molecules in a crystal, is sensitive to crystal thickness and three-dimensional lattice orientation. Real-space maps reconstructed from unsupervised classification of diffraction patterns across a crystal reveal regions of crystal order/disorder and three-dimensional lattice tilts on the sub-100nm scale. The nanoscale lattice reorientation observed in the micron-sized peptide crystal lattices studied here provides a direct view of their plasticity. Knowledge of these features facilitates an improved understanding of peptide assemblies that could aid in the determination of structures from nano- and microcrystals by single or serial crystal electron diffraction

Reconstructing the Scattering Matrix from Scanning Electron Diffraction Measurements Alone

Three-dimensional phase contrast imaging of multiply-scattering samples in X-ray and electron microscopy is extremely challenging, due to small numerical apertures, the unavailability of wavefront shaping optics, and the highly nonlinear inversion required from intensity-only measurements. In this work, we present a new algorithm using the scattering matrix formalism to solve the scattering from a non-crystalline medium from scanning diffraction measurements, and recover the illumination aberrations. Our method will enable 3D imaging and materials characterization at high resolution for a wide range of materials

Deep Learning Coherent Diffractive Imaging

We report the development of deep learning coherent electron diffractive imaging at sub-angstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to directly recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and a Au nanoparticle with comparable quality to those reconstructed by a conventional ptychographic method. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a spatial resolution in the range of 0.70 and 0.55 angstrom (depending on different resolution criteria). The ability to replace iterative algorithms with CNNs and perform real-time imaging from coherent diffraction patterns is expected to find broad applications in the physical and biological sciences.Comment: 19 pages, 7 figure

Orientation and morphology of Pt nanoparticles in γ-alumina processed via ion implantation and thermal annealing

Structure and chemistry of metal/metal-oxide interfaces are critical for many catalytic processes and sensing. Pristine interfaces of Pt and γ -Al2O3 were fabricated using high-energy ion implantation and thermal processing. Amorphous regions of alumina develop in single crystal α-alumina during Pt+ implantation and an 800 °C thermal treatment crystalizes amorphized alumina to γ -Al2O3 and allows Pt ions to precipitate within the developing γ -alumina, yielding Pt nanoparticle tetrahedra terminated by {111} surfaces. The phase of alumina that developed and the distribution, morphology, and orientation of Pt nanoparticles was determined using x-ray diffraction, Rutherford backscattering spectrometry, transmission electron microscopy and scanning transmission electron microscopy