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 r...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.展开更多
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 hav...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.展开更多
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-noi...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.展开更多
基金This work was primarily funded by the US Department of Energy in the program“4D Camera Distillery:From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML.”M.K.Y.C.and C.O.each acknowledge support of a US Department of Energy Early Career Research Award+4 种基金J.C.acknowledges support from the Presidential Early Career Award for Scientists and Engineers(PECASE)through the U.S.Department of Energy.B.H.S.and py4DSTEM development are supported by the Toyota Research InstituteS.E.Z.was supported by the National Science Foundation under STROBE Grant no.DMR 1548924Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the US Department of Energy under Contract No.DE-AC02-05CH11231Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357This research used resources of the National Energy Research Scientific Computing Center,a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231。
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.
基金This project was supported by the U.S.Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences(BES),Materials Sciences and Engineering Division under Contract No.DE-AC02-05-CH11231 within the in-situ TEM(KC22ZH)program.Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.We acknowledge Gatan Inc.for the advanced K2 IS camera and Dr.Ming Pan and Dr.Cory Czarnik for their help with part of experimental set up in this work.W.J.Z.acknowledges the support from Tianjin University Graduate School International Academic Exchange Fund.M.R.H.was funded by KAUST project under H.M.Z.at UC Berkeley.
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.
基金R.H.and J.S.acknowledge support from the Army Research Office,Computing Sciences(W911NF-17-S-0002)and Dow Chemical CompanyWork at the Molecular Foundry was supported by the Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract no.DE-AC02-05CH11231.
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.
