3篇
您的检索式：作者名="Colin Ophus"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Disentangling multiple scattering with deep learning:application to strain mapping from electron diffraction patterns显示文摘A fast,robust pipeline for strain mapping of crystalline materials is important for many technological applications.Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions,but this technique is limited when the electron beam undergoes multiple scattering.Deep-learning methods have the potential to invert these complex signals,but require a large number of training examples.We implement a Fourier space,complex-valued deep-neural network,FCU-Net,to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images.FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures,orientations,thicknesses,and microscope parameters,which are augmented with experimental artifacts.We evaluated FCU-Net against simulated and experimental datasets,where it substantially outperforms conventional analysis methods.Our code,models,and training library are open-source and may be adapted to different diffraction measurement problems. | Joydeep Munshi Alexander Rakowski Benjamin H.Savitzky Steven E.Zeltmann Jim Ciston Matthew Henderson Shreyas Cholia Andrew M.Minor Maria K.Y.Chan Colin Ophus | 2022 | npj Computational Materials2022，，1： | 2 |
| 2 | Imaging atomic-scale chemistry from fused multi-modal electron microscopy显示文摘Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions.Here,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware.In many cases,the dose requirements can be reduced by over one order of magnitude.This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. | Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden | 2022 | npj Computational Materials2022，，1： | 1 |
| 3 | Real time imaging of two-dimensional iron oxide spherulite nanostructure formation显示文摘The formation of complex hierarchical nanostructures has attracted a lot of attention from both the fundamental science and potential applications point of view.Spherulite structures with radial fibrillar branches have been found in various solids;however,their growth mechanisms remain poorly understood.Here,we report real time imaging of the formation of two-dimensional(2D)iron oxide spherulite nanostructures in a liquid cell using transmission electron microscopy(TEM).By tracking the growth trajectories,we show the characteristics of the reaction front and growth kinetics.Our observations reveal that the tip of a growing branch splits as the width exceeds certain sizes(5.5–8.5 nm).The radius of a spherulite nanostructure increases linearly with time at the early stage,transitioning to nonlinear growth at the later stage.Furthermore,a thin layer of solid is accumulated at the tip and nanoparticles from secondary nucleation also appear at the growing front which later develop into fibrillar branches.The spherulite nanostructure is polycrystalline with the co-existence of ferrihydrite and Fe3O4 through-out the growth.A growth model is further established,which provides rational explanations on the linear growth at the early stage and the nonlinearity at the later stage of growth. | Wenjing Zheng Matthew R.Hauwiller Wen-I Liang Colin Ophus Peter Ercius Emory M.Chan Ying-Hao Chu Mark Asta Xiwen Du A.Paul Alivisatos Haimei Zheng | 2019 | Nano Research2019，12，11： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员