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Localised solid-state nanopore fabrication via controlled breakdown using on-chip electrodes

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Nano Research 2022年第11期15卷 9881-9889页

作者： Jasper P.Fried Jacob L.Swett Binoy Paulose Nadappuram Aleksandra Fedosyuk Alex Gee ondrej e.dyck James R.Yates Aleksandar P.Ivanov Joshua B.edel Jan A.Mol Department of Materials University of OxfordOxfordOX13PHUK School of Chemistry University of New South WalesSydneyNew South Wales 2052Australia Department of Chemistry Imperial College LondonLondonW120BZUK Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37830USA Instituto de Tecnologia Química e Biológica António Xavier Universidade Nova de LisboaAv.da RepúblicaOeiras 2780-157Portugal School of Physics and Astronomy Queen Mary University of LondonLondonE14NSUK

Controlled breakdown has recently emerged as a highly accessible technique to fabricate solid-state ***,in its most common form,controlled breakdown creates a single nanopore at an arbitrary location in the ***,we introduce a new strategy whereby breakdown is performed by applying the electric field between an on-chip electrode and an electrolyte solution in contact with the opposite side of the *** demonstrate two advantages of this ***,we can independently fabricate multiple nanopores at given positions in the membrane by localising the applied field to the ***,we can create nanopores that are self-aligned with complementary nanoelectrodes by applying voltages to the on-chip electrodes to locally heat the membrane during controlled *** new controlled breakdown method provides a path towards the affordable,rapid,and automatable fabrication of arrays of nanopores self-aligned with complementary on-chip nanostructures.

关键词： solid-state nanopores dielectric breakdown nanofabrication single-molecule sensing nanopore arrays

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

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Computer Modeling in engineering & Sciences 2024年第9期140卷 2869-2889页

作者： Adéla Hamplová Alexey Lyavdansky TomášNovák ondrej Svojše David Franc Arnošt Veselý Czech University of Life Sciences in Prague(CULS) Faculty of Economics and ManagementDepartment of Information EngineeringPrague16500Czech Republic National Research University Higher School of economics(HSe) Faculty of HumanitiesInstitute for Oriental and Classical StudiesMoscow101000The Russian Federation

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow *** goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological *** research culminates in several key findings and scientific *** comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this *** also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the *** dataset serves for training and evaluating the segmentation *** employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation *** study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future *** availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

关键词： Optical character recognition instance segmentation Palmyrene ancient languages computer vision

Author Correction:Manifold learning of four-dimensional scanning transmission electron microscopy

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npj Computational Materials 2020年第1期6卷 1073-1073页

作者： Xin Li ondrej e.dyck Mark P.Oxley Andrew R.Lupini Leland McInnes John Healy Stephen Jesse Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge USA Institute for Functional Imaging of Materials Oak Ridge National Laboratory Oak Ridge USA Tutte Institute for Mathematics and Computing Ottawa Canada

The original version of the published Article had a mistake in the Acknowledgements *** Acknowledgments have been updated to the following:This research was supported by the US Department of energy,Basic energy Sciences,Materials Sciences and engineering Division(M.P.O.,A.R.L.,S.V.K.)and conducted at the Center for Nanophase Materials Sciences,which is a US DOe Office of Science User Facility(X.L.,O.e.D.,S.J.).*** *** support from Tutte Institute for Mathematics and Computing,Canada.

关键词： section ledgment Manifold

e-beam manipulation of Si atoms on graphene edges with an aberration-corrected scanning transmission electron microscope

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Nano Research 2018年第12期11卷 6217-6226页

作者： ondrej dyck Songkil Kim Sergei V.Kalinin Stephen Jesse Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37830USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37830USA School of Mechanical engineering Pusan National UniversityBusan 46241Republic of Korea

The burgeoning field of atomic-level material control holds great promise for future breakthroughs in quantum and memristive device manufacture and fundamental studies of atomic-scale chemistry. Realization of atom-by-atom control of matter represents a complex and ongoing challenge. Here, we explore the feasibility of controllable motion of dopant Si atoms at the edges of graphene via the sub-atomically focused electron beam in a scanning transmission electron microscope. We demonstrate that the graphene edges can be cleaned of Si atoms and then subsequently replenished from nearby source material. It is also shown how Si edge atoms may be “pushed” from the edge of a small hole into the bulk of the graphene lattice and from the bulk of the lattice back to the edge. This is accomplished through sputtering of the edge of the graphene lattice to bury or uncover Si dopant atoms. Finally, we demonstrate e-beam mediated hole healing and incorporation of dopant atoms. These experiments form an initial step toward general atomic-scale material control.

关键词： atomic manipulation graphene scanning transmission electron microscopy edge passivation silicon dopant electron beam dynamics

ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

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npj Computational Materials 2021年第1期7卷 909-916页

作者： Ayana Ghosh Bobby G.Sumpter ondrej dyck Sergei V.Kalinin Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of ***,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different *** limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated *** we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature *** approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the *** methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

关键词： learning experiment enable

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

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npj Computational Materials 2022年第1期8卷 705-715页

作者： Ayana Ghosh Maxim Ziatdinov ondrej dyck Bobby G.Sumpter Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN37831USA Computational Sciences and engineering Division Oak Ridge National LaboratoryOak RidgeTN37831USA

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials,naturally offering the challenge of using these as starting inputs for atomistic *** this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic ***,this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy,as well as latencies of microscopy and simulations per *** this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment,to enable the selection of regions of interest and exploring them using physical *** we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical *** pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the *** approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing ***,it is universal and can be used for other material systems.

关键词： dynamics bridges offering

Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

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npj Computational Materials 2021年第1期7卷 595-600页

作者： Mark P.Oxley Maxim Ziatdinov ondrej dyck Andrew R.Lupini Rama Vasudevan Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

The 4D scanning transmission electron microscopy(STeM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fields,structural and electronic order parameters,and other symmetry breaking distortions.A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STeM data to physically relevant *** propose an approach for the systematic exploration of 4D-STeM data using rotationally invariant variational autoencoders(rrVAe),which disentangle the general rotation of the object from other latent *** implementation of purely rotational rrVAe is discussed as are applications to simulated data for graphene and zincblende *** rrVAe analysis of experimental 4D-STeM data of defects in graphene is illustrated and compared to the classical center-of-mass *** approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.

关键词： symmetry breaking rotational

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)

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npj Computational Materials 2019年第1期5卷 1039-1046页

作者： Artem Maksov ondrej dyck Kai Wang Kai Xiao David B.Geohegan Bobby G.Sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37831USA Bredesen Center for Interdisciplinary Research University of TennesseeKnoxvilleTN 37996USA Computational Sciences&engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA

Recent advances in scanning transmission electron microscopy(STeM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic ***,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is *** is due to the inherent limitations of manual ex situ analysis of the collected volumes of *** circumvent this problem,we developed a deep-learning framework for dynamic STeM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STeM data in a matter of seconds,which are then classified into different categories using unsupervised clustering *** further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and *** approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

关键词： defect beam sulfur

Mapping mesoscopic phase evolution during e-beam induced transformations via deep learning of atomically resolved images

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npj Computational Materials 2018年第1期4卷 396-404页

作者： Rama K.Vasudevan Nouamane Laanait erik M.Ferragut Kai Wang David B.Geohegan Kai Xiao Maxim Ziatdinov Stephen Jesse ondrej dyck Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Quantum Computing Institute Oak Ridge National LaboratoryOak RidgeTN 37831USA UnitedHealth Group PO Box 1459Minneapolis 55440 MNUSA

Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real *** date,this has mostly been a manual endeavor comprising difficult frame-by-frame analysis that is simultaneously tedious and prone to ***,we turn toward the use of deep convolutional neural networks(DCNN)to automatically determine the Bravais lattice symmetry present in atomically resolved images.A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input ***-Carlo dropout is used for determining the prediction probability,and results are shown for both simulated and real atomically resolved images from scanning tunneling microscopy and scanning transmission electron microscopy.A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical *** then apply the trained network to electron beam-induced transformations in WS2,which allows tracking and determination of growth rate of *** highlight two key aspects of these results:(1)it shows that DCNNs can be trained to recognize diffraction patterns,which is markedly different from the typical“real image”cases and(2)it provides a method with inbuilt uncertainty quantification,allowing the real-time analysis of phases present in atomically resolved images.

关键词： beam evolution atomic