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Skorikov A, Heyvaert W, Albrecht W, Pelt DM, Bals S (2021) EMAT Simulated 3D Nanoparticle Structures Dataset
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
Keywords: Dataset; Electron microscopy for materials research (EMAT)
DOI: 10.5281/zenodo.4580545
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“Real‐Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles”. Vanrompay H, Buurlage J‐W, Pelt DM, Kumar V, Zhuo X, Liz‐Marzán LM, Bals S, Batenburg KJ, Particle &, Particle Systems Characterization 37, 2000073 (2020). http://doi.org/10.1002/ppsc.202000073
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.
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
Impact Factor: 2.7
Times cited: 10
DOI: 10.1002/ppsc.202000073
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“Electron tomography based on highly limited data using a neural network reconstruction technique”. Bladt E, Pelt DM, Bals S, Batenburg KJ, Ultramicroscopy 158, 81 (2015). http://doi.org/10.1016/j.ultramic.2015.07.001
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.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 2.843
Times cited: 25
DOI: 10.1016/j.ultramic.2015.07.001
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“Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles”. Skorikov A, Heyvaert W, Albecht W, Pelt DM, Bals S, Nanoscale 13, 12242 (2021). http://doi.org/10.1039/D1NR03232A
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.
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
Impact Factor: 7.367
Times cited: 11
DOI: 10.1039/D1NR03232A
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