Quantitative Electron Beam‐Single Atom Interactions Enabled by Sub‐20‐pm Precision Targeting
Source abstract
Abstract
Abstract The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful capabilities for materials analysis and modification, but sample damage, drift, and scan distortions hinder single atom analysis and deterministic manipulation. Materials analysis and modification via electron–solid interactions can be transformed by precise delivery of electrons to a specified atomic location, maintaining the beam position despite drift, and minimizing collateral dose. Here a fast, low‐dose, sub‐20‐pm precision electron beam positioning technique is developed, “atomic lock‐on,” (ALO), which offers the ability to position the beam on a specific atomic column without previously irradiating that column. This technique is used to lock onto a single selected atomic location to repeatedly measure its weak electron energy loss signal despite sample drift. Moreover, electron beam‐matter interactions in single atomic events are measured with time resolution. This enables observation of single‐atom dynamics, such as atomic bistability, revealing partially bonded atomic configurations and recapture phenomena. This opens prospects for using electron microscopy for high‐precision measurements and deterministic control of matter for quantum technologies.
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Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Electron and X-Ray Spectroscopy Techniques · Electronic and Structural Properties of Oxides
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37830 USA
Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
Funding
National Science Foundation · U.S. Department of Energy
References (43)
Show source-provided references
1. 10.1038/nature0097210.1038/nature00972 ↗
2. 10.1103/PhysRevLett.102.09610110.1103/PhysRevLett.102.096101 ↗
3. 10.1021/nl400747910.1021/nl4007479 ↗
4. 10.1038/nature1387010.1038/nature13870 ↗
5. 10.1038/nature0887910.1038/nature08879 ↗
6. 10.1038/nature0966410.1038/nature09664 ↗
7. Zhou W. · Phys. Rev. Lett. · 2012 · 109 · 20
8. 10.1017/S143192760808019710.1017/S1431927608080197 ↗
9. 10.1103/PhysRevLett.113.11550110.1103/PhysRevLett.113.115501 ↗
10. 10.1016/j.ultramic.2017.03.00510.1016/j.ultramic.2017.03.005 ↗
11. 10.1021/acs.nanolett.8b0240610.1021/acs.nanolett.8b02406 ↗
12. 10.1038/s41578-019-0118-z10.1038/s41578-019-0118-z ↗
13. 10.1038/s41565-023-01354-x10.1038/s41565-023-01354-x ↗
14. 10.1016/j.micron.2004.02.00310.1016/j.micron.2004.02.003 ↗
15. 10.1016/0304-3991(94)00172-J10.1016/0304-3991(94)00172-J ↗
16. 10.1017/S143192761300140210.1017/S1431927613001402 ↗
17. 10.1103/RevModPhys.82.20910.1103/RevModPhys.82.209 ↗
18. 10.1038/s42254-019-0058-y10.1038/s42254-019-0058-y ↗
19. 10.1021/nl304187e10.1021/nl304187e ↗
20. 10.1039/C7NR01127J10.1039/C7NR01127J ↗
21. 10.1126/science.124224810.1126/science.1242248 ↗
22. 10.1038/nnano.2014.6410.1038/nnano.2014.64 ↗
23. Klein J. · Nat. Commun. · 2022 · 13 · 1
24. 10.1073/pnas.74.5.180210.1073/pnas.74.5.1802 ↗
25. 10.1126/science.116699910.1126/science.1166999 ↗
26. 10.1038/ncomms388410.1038/ncomms3884 ↗
27. 10.1038/ncomms499110.1038/ncomms4991 ↗
28. 10.1021/acsnano.7b0079610.1021/acsnano.7b00796 ↗
29. 10.1103/PhysRevLett.109.03550310.1103/PhysRevLett.109.035503 ↗
30. Meyer J. C. · Phys. Rev. Lett. · 2012 · 108 · 19
31. 10.1021/acs.nanolett.0c0067010.1021/acs.nanolett.0c00670 ↗
32. 10.1103/PhysRevB.107.09411210.1103/PhysRevB.107.094112 ↗
33. 10.1021/acsnano.2c0745110.1021/acsnano.2c07451 ↗
34. 10.1126/sciadv.adn589910.1126/sciadv.adn5899 ↗
35. 10.1016/j.ultramic.2013.12.00410.1016/j.ultramic.2013.12.004 ↗
36. 10.1016/j.ultramic.2015.12.00210.1016/j.ultramic.2015.12.002 ↗
37. 10.1186/s40679-016-0020-310.1186/s40679-016-0020-3 ↗
38. 10.1038/s41598-016-0028-x10.1038/s41598-016-0028-x ↗
39. 10.1021/acsnano.6b0567410.1021/acsnano.6b05674 ↗
40. 10.1039/D2NR01018F10.1039/D2NR01018F ↗
41. 10.1186/s40679-016-0020-310.1186/s40679-016-0020-3 ↗
42. 10.1038/s41524-020-00473-610.1038/s41524-020-00473-6 ↗
43. O.Ronneberger P.Fischer T.Brox U‐Net: Convolutional Networks for Biomedical Image Segmentation Springer International Publishing ISBN 9783319245744 2015 pp.234–241.10.1007/978-3-319-24574-4_28 ↗