| |
Records |
Links |
|
Author |
Skorikov, A.; Heyvaert, W.; Albecht, W.; Pelt, D.M.; Bals, S. |
|
| |
Title |
Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles |
Type |
A1 Journal article |
| |
Year  |
2021 |
Publication |
Nanoscale |
Abbreviated Journal |
Nanoscale |
|
| |
Volume |
13 |
Issue |
|
Pages |
12242-12249 |
|
| |
Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
|
| |
Abstract |
The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex, multi-component systems, such as semiconductor devices, as well as catalytic and plasmonic nanoparticles. Unfortunately, the applicability of EDX tomography is severely limited because of extremely long acquisition times and high electron irradiation doses required to obtain 3D EDX reconstructions with an adequate signal-to-noise ratio. One possibility to address this limitation is intelligent denoising of experimental data using prior expectations about the objects of interest. Herein, this approach is followed using the deep learning methodology, which currently demonstrates state-of-the-art performance for an increasing number of data processing problems. Design choices for the denoising approach and training data are discussed with a focus on nanoparticle-like objects and extremely noisy signals typical for EDX experiments. Quantitative analysis of the proposed method demonstrates its significantly enhanced performance in comparison to classical denoising approaches. This allows for improving the tradeoff between the reconstruction quality, acquisition time and radiation dose for EDX tomography. The proposed method is therefore especially beneficial for the 3D EDX investigation of electron beam-sensitive materials and studies of nanoparticle transformations. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
000671395800001 |
Publication Date |
2021-07-08 |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2040-3364 |
ISBN |
|
Additional Links |
UA library record; WoS full record; WoS citing articles |
|
| |
Impact Factor |
7.367 |
Times cited |
11 |
Open Access |
OpenAccess |
|
| |
Notes |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek, 016.Veni.192.235 ; H2020 European Research Council, 815128 ; H2020 Marie Skłodowska-Curie Actions, 797153 ; H2020 Research Infrastructures, 731019; realnano; sygmaSB |
Approved |
Most recent IF: 7.367 |
|
| |
Call Number |
EMAT @ emat @c:irua:179756 |
Serial |
6799 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Skorikov, A.; Heyvaert, W.; Albrecht, W.; Pelt, D.M.; Bals, S. |
|
| |
Title |
EMAT Simulated 3D Nanoparticle Structures Dataset |
Type |
Dataset |
| |
Year |
2021 |
Publication |
|
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Dataset; Electron microscopy for materials research (EMAT) |
|
| |
Abstract |
This dataset contains 1000 simulated nanoparticle-like 3D structures and noisy EDX-like elemental maps based on them. These data are intended to be used for quantitative analysis of data processing methods in (EDX) tomography of nanoparticles and training the data-driven approaches for these tasks. The dataset is structured as follows: voxel_data/clean 3D voxel grid representation of the simulated nanoparticles. Voxel intensities are adjusted so that the total intensity equals 103. All 3D structures have unique identifiers in 0..999 range. The data derived from a 3D structure preserves this unique identifier. sinograms/clean Tilt series of projection images obtained from the corresponding 3D structures over an angular range of -75..75 degrees with a tilt step of 10 degrees to simulate a typical tilt series used in EDX tomography. Total intensity in each projection image equals 103. sinograms/noisy Tilt series of projection images corrupted with Poisson noise and an additional spatially uniform background noise. projections/clean Projection images extracted from the clean tilt series at 0 degrees tilt angle. projections/noisy Projection images extracted from the noisy tilt series at 0 degrees tilt angle. images/clean Visualizations of the clean projections as PNG images with the intensity range adjusted to 0..255 images/noisy Visualizations of the noisy projections as PNG images with the intensity range adjusted to 0..255 |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
|
Publication Date |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Additional Links |
UA library record |
|
| |
Impact Factor |
|
Times cited |
|
Open Access |
Not_Open_Access |
|
| |
Notes |
|
Approved |
Most recent IF: NA |
|
| |
Call Number |
UA @ admin @ c:irua:180615 |
Serial |
6838 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Vanrompay, H.; Buurlage, J.‐W.; Pelt, D.M.; Kumar, V.; Zhuo, X.; Liz‐Marzán, L.M.; Bals, S.; Batenburg, K.J. |
|
| |
Title |
Real‐Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles |
Type |
A1 Journal article |
| |
Year |
2020 |
Publication |
Particle & Particle Systems Characterization |
Abbreviated Journal |
Part Part Syst Char |
|
| |
Volume |
37 |
Issue |
37 |
Pages |
2000073 |
|
| |
Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
|
| |
Abstract |
A detailed 3D investigation of nanoparticles at a local scale is of great importance to connect their structure and composition to their properties. Electron tomography has therefore become an important tool for the 3D characterization of nanomaterials. 3D investigations typically comprise multiple steps, including acquisition, reconstruction, and analysis/quantification. Usually, the latter two steps are performed offline, at a dedicated workstation. This sequential workflow prevents on-the-fly control of experimental parameters to improve the quality of the 3D reconstruction, to select a relevant nanoparticle for further characterization or to steer an in-situ tomography experiment. Here, we present an efficient approach to overcome these limitations, based on the real-time reconstruction of arbitrary 2D reconstructed slices through a 3D object. Implementation of this method may lead to generalized implementation of electron tomography for routine nanoparticle characterization in 3D. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
000536357100001 |
Publication Date |
2020-05-29 |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0934-0866 |
ISBN |
|
Additional Links |
UA library record; WoS full record; WoS citing articles |
|
| |
Impact Factor |
2.7 |
Times cited |
10 |
Open Access |
OpenAccess |
|
| |
Notes |
Fonds Wetenschappelijk Onderzoek, 1S32617N ; Fonds Wetenschappelijk Onderzoek, G026718N ; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, 639.073.506 016.Veni.192.235 ; H.V. acknowledges financial support by the Research Foundation Flanders (FWO grant 1S32617N). S.B acknowledges financial support by the Research Foundation Flanders (FWO grant G026718N). Financial support was provided by The Netherlands Organization for Scientific Research (NWO), project numbers 639.073.506 and 016.Veni.192.235. This project received funding as well from the European Union’s Horizon 2020 research and innovation program under grant agreement No 731019 (EUSMI) and No 815128 (REALNANO). H.V. and J.-W.B contributed equally to this work.; sygma |
Approved |
Most recent IF: 2.7; 2020 IF: 4.474 |
|
| |
Call Number |
EMAT @ emat @c:irua:169704 |
Serial |
6371 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Bladt, E.; Pelt, D.M.; Bals, S.; Batenburg, K.J. |
|
| |
Title |
Electron tomography based on highly limited data using a neural network reconstruction technique |
Type |
A1 Journal article |
| |
Year |
2015 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
|
| |
Volume |
158 |
Issue |
158 |
Pages |
81-88 |
|
| |
Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
|
| |
Abstract |
Gold nanoparticles are studied extensively due to their unique optical and catalytical properties. Their exact shape determines the properties and thereby the possible applications. Electron tomography is therefore often used to examine the three-dimensional (3D) shape of nanoparticles. However, since the acquisition of the experimental tilt series and the 3D reconstructions are very time consuming, it is difficult to obtain statistical results concerning the 3D shape of nanoparticles. Here, we propose a new approach for electron tomography that is based on artificial neural networks. The use of a new reconstruction approach enables us to reduce the number of projection images with a factor of 5 or more. The decrease in acquisition time of the tilt series and use of an efficient reconstruction algorithm allows us to examine a large amount of nanoparticles in order to retrieve statistical results concerning the 3D shape. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
Amsterdam |
Editor |
|
|
| |
Language |
|
Wos |
000361574800011 |
Publication Date |
2015-07-10 |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0304-3991; |
ISBN |
|
Additional Links |
UA library record; WoS full record; WoS citing articles |
|
| |
Impact Factor |
2.843 |
Times cited |
25 |
Open Access |
OpenAccess |
|
| |
Notes |
335078 COLOURATOM; FWO; COST Action MP1207; 312483 ESTEEM2; esteem2jra4; ECASSara; (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); |
Approved |
Most recent IF: 2.843; 2015 IF: 2.436 |
|
| |
Call Number |
c:irua:126675 c:irua:126675 |
Serial |
988 |
|
| Permanent link to this record |
