Characterisation of structure across the nanometre scale is key to bridging the gap between the local atomic environment and macro-scale and can be achieved by means of scanning electron nanobeam diffraction(SEND).As ...Characterisation of structure across the nanometre scale is key to bridging the gap between the local atomic environment and macro-scale and can be achieved by means of scanning electron nanobeam diffraction(SEND).As a technique,SEND allows for a broad range of samples,due to being relatively tolerant of specimen thickness with low electron dosage.This,coupled with the capacity for automation of data collection over wide areas,allows for statistically representative probing of the microstructure.This paper outlines a versatile,data-driven approach for producing domain maps,and a statistical approach for assessing their applicability.The workflow utilises a Variational AutoEncoder to identify the sources of variance in the diffraction signal,and this,in combination with clustering techniques,is used to produce domain maps.This approach is agnostic to domain crystallinity,requires no prior knowledge of crystal structure,and does not require simulation of a library of expected diffraction patterns.展开更多
In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak...In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.展开更多
基金We thank Diamond Light Source for access and support in the use of the electron Physical Science Imaging Centre(Instrument E02 and proposal numbers EM19064 and MG28749)that contributed to the results presented hereA.B.gratefully acknowledges support from the joint Oxford-Diamond-STFC studentship scheme.T.J.W.and W.I.F.Dalso acknowledge the Faraday Institution(Grant No.FIRG018)for their funding contributions.
文摘Characterisation of structure across the nanometre scale is key to bridging the gap between the local atomic environment and macro-scale and can be achieved by means of scanning electron nanobeam diffraction(SEND).As a technique,SEND allows for a broad range of samples,due to being relatively tolerant of specimen thickness with low electron dosage.This,coupled with the capacity for automation of data collection over wide areas,allows for statistically representative probing of the microstructure.This paper outlines a versatile,data-driven approach for producing domain maps,and a statistical approach for assessing their applicability.The workflow utilises a Variational AutoEncoder to identify the sources of variance in the diffraction signal,and this,in combination with clustering techniques,is used to produce domain maps.This approach is agnostic to domain crystallinity,requires no prior knowledge of crystal structure,and does not require simulation of a library of expected diffraction patterns.
基金funding through the Innovate UK Analysis for Innovators(A4i)programme(Project No.106003)Parts of this research were carried out at PETRA III.A.V.acknowledges financial support from the Royal Society as a Royal Society Industry Fellow(IF\R2\222059).
文摘In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.
