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Author |
De Backer, A.; De Wael, A.; Gonnissen, J.; Martinez, G.T.; Béché, A.; MacArthur, K.E.; Jones, L.; Nellist, P.D.; Van Aert, S. |
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Title |
Quantitative annular dark field scanning transmission electron microscopy for nanoparticle atom-counting: What are the limits? |
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P1 Proceeding |
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Year |
2015 |
Publication |
Journal of physics : conference series |
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Volume |
644 |
Issue |
644 |
Pages |
012034 |
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Keywords |
P1 Proceeding; Electron microscopy for materials research (EMAT) |
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Abstract |
Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atom-counting diagnosed by combining a thorough statistical method and detailed image simulations. |
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Wos |
000366826200034 |
Publication Date |
2015-10-13 |
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Abbreviated Series Title |
Electron Microscopy and Analysis Group Conference (EMAG), JUN 02-JUL 02, 2015, Manchester, ENGLAND |
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ISSN |
1742-6588 |
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Additional Links |
UA library record; WoS full record |
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Notes |
The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project funding (G.0368.15N, G.0369.15N, and G.0374.15N) and a PhD research grant to A De Backer. The research leading to these results has received funding from the European Union Seventh Framework Programme under Grant Agreement 312483 – ESTEEM2 (Integrated Infrastructure Initiative-I3), ERC Starting Grant 278510 Vortex, and the UK Engineering and Physical Sciences Research Council (EP/K032518/1). The authors acknowledge Johnson-Matthey for providing the sample and PhD funding to K E MacArthur. A Rosenauer is acknowledged for providing the STEMsim program.; esteem2jra2; ECASJO; |
Approved |
Most recent IF: NA |
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Call Number |
c:irua:130314 c:irua:130314 |
Serial |
4050 |
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Author |
De Backer, A.; van den Bos, K.H.W.; Van den Broek, W.; Sijbers, J.; Van Aert, S. |
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Title |
StatSTEM: An efficient program for accurate and precise model-based quantification of atomic resolution electron microscopy images |
Type |
P1 Proceeding |
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Year |
2017 |
Publication |
Journal of physics : conference series
T2 – Electron Microscopy and Analysis Group Conference 2017 (EMAG2017), 3-6 July 2017, Manchester, UK |
Abbreviated Journal |
J. Phys.: Conf. Ser. |
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Volume |
902 |
Issue |
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Pages |
012013 |
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Keywords |
P1 Proceeding; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
An efficient model-based estimation algorithm is introduced in order to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for the overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, is investigated. The highest attainable precision is reached even for low dose images. Furthermore, advantages of the model- based approach taking into account overlap between neighbouring columns are highlighted. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. |
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000416370700013 |
Publication Date |
2017-10-16 |
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ISSN |
1742-6588 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Times cited |
1 |
Open Access |
OpenAccess |
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Notes |
The authors acknowledge nancial support from the Research Foundation Flanders (FWO, Belgium) through project funding (G.0374.13N, G.0368.15N, G.0369.15N, WO.010.16N) and a PhD research grant to K H W van den Bos, and a postdoctoral research grant to A De Backer. The research leading to these results has received funding from the European Union Seventh Framework Programme under Grant Agreement 312483 – ESTEEM2 (Integrated Infrastructure Initiative-I3). A Rosenauer is acknowledged for providing the STEMsim program. |
Approved |
Most recent IF: NA |
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Call Number |
EMAT @ emat @c:irua:147188 |
Serial |
4764 |
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Author |
Goris, B.; De Beenhouwer, J.; de Backer, A.; Zanaga, D.; Batenburg, J.; Sanchez-Iglesias, A.; Liz-Marzan, L.; Van Aert, S.; Sijbers, J.; Van Tendeloo, G.; Bals, S. |
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Title |
Investigating lattice strain in Au nanodecahedrons |
Type |
P1 Proceeding |
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Year |
2016 |
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Volume |
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Pages |
11-12 |
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Keywords |
P1 Proceeding; Electron microscopy for materials research (EMAT); Vision lab |
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Wos |
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Publication Date |
2016-12-21 |
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ISSN |
978-3-527-80846-5 |
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Additional Links |
UA library record |
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Open Access |
Not_Open_Access |
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Notes |
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Approved |
Most recent IF: NA |
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Call Number |
UA @ lucian @ c:irua:145813 |
Serial |
5144 |
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Author |
Friedrich, T.; Yu, C.-P.; Verbeek, J.; Pennycook, T.; Van Aert, S. |
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Title |
Phase retrieval from 4-dimensional electron diffraction datasets |
Type |
P1 Proceeding |
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Year |
2021 |
Publication |
Proceedings
T2 – IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network |
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Pages |
3453-3457 |
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Keywords |
P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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Abstract |
We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition. |
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Wos |
000819455103114 |
Publication Date |
2021-08-23 |
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978-1-6654-4115-5 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Times cited |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:189462 |
Serial |
7089 |
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