FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-p...FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.展开更多
The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules.However,an automatic identification of effective organic additives within the vast chemical space of potential compounds ne...The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules.However,an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools.Herein,we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds.One is based on the classical statistical tool of analysis of variance,the other one based on random forests.We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41.In particular,we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors.Finally,we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.展开更多
基金funded by the Deutsche Forschungsgemein-schaft(DFG,German Research Foundation)-SFB 986-Project number 192346071.
文摘FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.
基金Funding by the Helmholtz Association is gratefully acknowledged.T.W.and C.F.gratefully acknowledge funding by the Deutscher Akademischer Austauschdienst(DAAD,German Academic Exchange Service)via Projektnummer 57511455R.M.gratefully acknowledges funding by the Deutsche Forschungsgemeinschaft(D.F.G.,German Research Foundation)via Projektnummer 192346071-SFB 986 and Projektnummer 390794421-GRK 2462.
文摘The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules.However,an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools.Herein,we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds.One is based on the classical statistical tool of analysis of variance,the other one based on random forests.We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41.In particular,we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors.Finally,we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.
