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A Semi-quantitative Predictive Model for SnO2 Adatom Diffusion & Its Application to Exit Wave Reconstruction

Published online by Cambridge University Press:  05 August 2019

Arthur N. Moya
Affiliation:
Department of Materials, University of Oxford, Parks Roads, Oxford OX1 3PH, United Kingdom.
Ofentse A. Makgae
Affiliation:
Department of Materials, University of Oxford, Parks Roads, Oxford OX1 3PH, United Kingdom.
Emanuela Liberti
Affiliation:
Department of Materials, University of Oxford, Parks Roads, Oxford OX1 3PH, United Kingdom. electron Physical Science Imaging Centre (ePSIC), Diamond Light Source Ltd., Didcot, OX11 0DE, United Kingdom.
Angus I. Kirkland
Affiliation:
Department of Materials, University of Oxford, Parks Roads, Oxford OX1 3PH, United Kingdom. electron Physical Science Imaging Centre (ePSIC), Diamond Light Source Ltd., Didcot, OX11 0DE, United Kingdom.
Corresponding
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Abstract

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Type
Advances in Phase Retrieval Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Hsieh, W-K et al. , Ultramicroscopy 98 (2–4) (2004), p. 99114.CrossRefGoogle Scholar
[2]Wang, A, Ultramicroscopy 116 (2012), p. 7785.CrossRefGoogle Scholar
[3]Haigh, SJ et al. , Journal of Physics: Conference Series 241 (2010), p. 012013.Google Scholar
[4]Egerton, RF, Microscopy & Microanalysis, 19 (02) (2013), p. 479486.CrossRefGoogle Scholar
[5]Egerton, RF, Micron 119 (2019), p. 7287.CrossRefGoogle Scholar
[6]Huang, C et al. , Journal of Physics: Conference Series 522 (2014), p. 012052.Google Scholar
[7]Arthur Moya is a Commonwealth Scholar, funded by the UK government.Google Scholar

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A Semi-quantitative Predictive Model for SnO2 Adatom Diffusion & Its Application to Exit Wave Reconstruction
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