Electron-beam introduction of heteroatomic Pt–Si structures in graphene

Dyck, Ondrej; Zhang, Cheng; Rack, Philip D.; Fowlkes, Jason D.; Sumpter, Bobby G.; Lupini, Andrew R.; Kalinin, Sergei V.; Jesse, Stephen

doi:10.1016/j.carbon.2020.01.042

Cited by 38 publications

(32 citation statements)

References 44 publications

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“…[38,39] Electron beam irradiation has been used to encourage dopants to inject themselves into a 2D lattice in a covalent bonded arrangement, forming stable single atom dopants. [40][41][42][43] It has been used to manipulate a single heavy atom dopant in graphene, inducing migration and bond switching. The sub-Angstrom size and positional accuracy of a focused electron beam in a scanning TEM opens the pathway for atomically precise creation of materials.…”

Section: Introductionmentioning

confidence: 99%

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Park

Wen

et al. 2021

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Precise controlled filling of point vacancies in hBN with carbon atoms is demonstrated using a focused electron beam method, which guides mobile C atoms into the desired defect site. Optimization of the technique enables the insertion of a single C atom into a selected monovacancy, and preferential defect filling with sub-2 nm accuracy. Increasing the C insertion process leads to thicker 3D C nanodots seeded at the hBN point vacancy site. Other light elements are also observed to bind to hBN vacancies, including O, opening up a wide range of complex defect structures that include B, C, N, and O atoms. The ability to selectively fill point vacancies in hBN with C atoms provides a pathway for creating non-hydrogenated covalently bonded C molecules embedded in the insulating hBN.

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Section: Introductionmentioning

confidence: 99%

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Park

Wen

et al. 2021

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“…[34][35][36][37][38] Recently, the merging of such Si clusters with a single Pt atom towards heteroatomic Pt-Si structures on graphene was also reported. 39 A shortcoming of the STEM manipulation approach is, however, the difficulty in extending it to large scale fabrication. A materials system that intrinsically and readily allows single-atom and few-atom cluster formation and anchoring on graphene membranes in a more scalable and self-assembled fashion has remained elusive to date.…”

Section: Introductionmentioning

confidence: 99%

Single indium atoms and few-atom indium clusters anchored onto graphene via silicon heteroatoms

et al. 2021

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Single atoms and few-atom nanoclusters are of high interest in catalysis and plasmonics, but pathways for their fabrication and stable placement remain scarce. We report here the selfassembly of room-temperature-stable single indium (In) atoms and few-atom In clusters (2-6 atoms) that are anchored to substitutional silicon (Si) impurity atoms in suspended monolayer graphene membranes. Using atomically resolved scanning transmission electron microscopy (STEM), we find that the exact atomic arrangements of the In atoms depend strongly on the original coordination of the Si "anchors" in the graphene lattice: Single In atoms and In clusters with 3-fold symmetry readily form on 3-fold coordinated Si atoms, whereas 4-fold symmetric clusters are found attached to 4-fold coordinated Si atoms. All structures are produced by our fabrication route without the requirement for electron-beam induced materials modification. In turn, when activated by electron beam irradiation in the STEM, we observe in situ the formation, restructuring and translation dynamics of the Si-anchored In structures: Hexagon-centered 4-fold symmetric In clusters can (reversibly) transform into In chains or In dimers, whereas C-centered 3-fold symmetric In clusters can move along the zigzag direction of the graphene lattice due to the migration of Si atoms during electron-beam irradiation, or transform to Si-anchored single In atoms. Our results provide a novel framework for the controlled self-assembly and heteroatomic anchoring of single atoms and few-atom clusters on graphene.

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“…Structural discovery is not a universal concept, as it can be driven by different considerations and often reward functions are defined a priori. For example, in atomic manipulation experiments [19][20][21][39][40][41][42][43][44][45][46], we seek to find regions of clean graphene, away from contaminations and free of defects. Reversely, in experiments focusing on exploring the chemistry of graphene, we want to find regions with high defect density.…”

Section: Introductionmentioning

confidence: 99%

Towards automating structural discovery in scanning transmission electron microscopy ^*

Creange

Dyck

Vasudevan

et al. 2022

Mach. Learn.: Sci. Technol.

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Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO3 matrix, ferroelectric domains in BiFeO3, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.

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Electron-beam introduction of heteroatomic Pt–Si structures in graphene

Cited by 38 publications

References 44 publications

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Single indium atoms and few-atom indium clusters anchored onto graphene via silicon heteroatoms

Towards automating structural discovery in scanning transmission electron microscopy ^*

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Electron-beam introduction of heteroatomic Pt–Si structures in graphene

Cited by 38 publications

References 44 publications

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Atomically Precise Control of Carbon Insertion into hBN Monolayer Point Vacancies using a Focused Electron Beam Guide

Single indium atoms and few-atom indium clusters anchored onto graphene via silicon heteroatoms

Towards automating structural discovery in scanning transmission electron microscopy *

Contact Info

Product

Resources

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Towards automating structural discovery in scanning transmission electron microscopy ^*