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Reconstruction of simulated electrostatic potentials by automatic differentiation-based phase retrieval in electron microscopy imaging

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Abstract

Measuring interfacial electrostatic potentials is vital to understanding many fundamental materials properties. A variety of TEM methods exist for measuring electric potentials from the phase shift produced on an electron wave as it passes through a sample. However, most are either experimentally challenging or poorly suited to resolving nanoscale features. Here, we demonstrate the viability of a simple, automatic differentiation-based exit wave reconstruction from a focal series of images to accurately measure the nanoscale electric potentials. The analysis suggests that under optimal measurement conditions, electric potentials can be resolved to less than 0.06 V in magnitude and less than 1 nm in spatial extent.

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Data availability

The codes used for image simulation, exit wave reconstruction, and data analysis are available in a public GitHub repository at https://github.com/hailegroup/AD_GB_simulations. Simulated images are available upon request.

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Acknowledgments

The authors gratefully acknowledge the support from the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division (C.P.), the Office of Science Graduate Student Research Program (C.G.C.), and the US National Science Foundation, Division of Materials Research via Awards DMR-1720139 and DMR-2130831. This material is additionally based upon work supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number DE-SC0023438 (S.M.H.). Work performed (T.Z, M.C), in part, at the Center for Nanoscale Materials and the Advanced Photon Source, both US Department of Energy Office of Science User Facilities, was supported by the US Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357.

Funding

This study was funded by Division of Materials Research, DMR-1720139, Connor Carr, DMR-2130831, Connor Carr, DMR-1720139, Sossina Haile, DMR-2130831, Sossina Haile, Office of Science, Graduate Student Research Program, Connor Carr, DE-AC02-06CH11357, Tao Zhou, DE-AC02-06CH11357, Mathew Cherukara, Energy Frontier Research Centers, DE-SC0023438, Connor Carr, DE-SC0023438, Sossina Haile, Basic Energy Sciences.

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Authors and Affiliations

  1. Materials Science and Engineering, Northwestern University, Evanston, USA

    Connor G. Carr & Sossina M. Haile

  2. Center for Nanoscale Materials, Argonne National Laboratory, Lemont, USA

    Tao Zhou

  3. Advanced Photon Source, Argonne National Laboratory, Lemont, USA

    Mathew Cherukara

  4. Materials Science Division, Argonne National Laboratory, Lemont, USA

    Charudatta Phatak

  5. Applied Physics, Northwestern University, Evanston, USA

    Sossina M. Haile

  6. Chemistry, Northwestern University, Evanston, USA

    Sossina M. Haile

Authors
  1. Connor G. Carr
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  2. Tao Zhou
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  3. Mathew Cherukara
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  4. Charudatta Phatak
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  5. Sossina M. Haile
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Correspondence to Sossina M. Haile.

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Carr, C.G., Zhou, T., Cherukara, M. et al. Reconstruction of simulated electrostatic potentials by automatic differentiation-based phase retrieval in electron microscopy imaging. MRS Communications 13, 871–876 (2023). https://doi.org/10.1557/s43579-023-00420-8

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  • DOI: https://doi.org/10.1557/s43579-023-00420-8

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