| Records |
| Author |
Friedrich, T.; Yu, C.-P.; Verbeek, J.; Pennycook, T.; Van Aert, S. |
| Title |
Phase retrieval from 4-dimensional electron diffraction datasets |
Type |
P1 Proceeding |
| Year |
2021 |
Publication |
Proceedings
T2 – IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network |
Abbreviated Journal  |
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| Volume |
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Issue |
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Pages |
3453-3457 |
| Keywords |
P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| 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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Series Issue |
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Edition |
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| ISSN |
978-1-6654-4115-5 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
| Notes |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:189462 |
Serial |
7089 |
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| Author |
Ren, P.; Zhang, T.; Jain, N.; Ching, H.Y.V.; Jaworski, A.; Barcaro, G.; Monti, S.; Silvestre-Albero, J.; Celorrio, V.; Chouhan, L.; Rokicinska, A.; Debroye, E.; Kustrowski, P.; Van Doorslaer, S.; Van Aert, S.; Bals, S.; Das, S. |
| Title |
An atomically dispersed Mn-photocatalyst for generating hydrogen peroxide from seawater via the Water Oxidation Reaction (WOR) |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Journal of the American Chemical Society |
Abbreviated Journal |
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| Volume |
145 |
Issue |
30 |
Pages |
16584-16596 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Organic synthesis (ORSY); Theory and Spectroscopy of Molecules and Materials (TSM²) |
| Abstract |
In this work, we have fabricatedan aryl amino-substitutedgraphiticcarbon nitride (g-C3N4) catalyst with atomicallydispersed Mn capable of generating hydrogen peroxide (H2O2) directly from seawater. This new catalyst exhibitedexcellent reactivity, obtaining up to 2230 & mu;M H2O2 in 7 h from alkaline water and up to 1800 & mu;Mfrom seawater under identical conditions. More importantly, the catalystwas quickly recovered for subsequent reuse without appreciable lossin performance. Interestingly, unlike the usual two-electron oxygenreduction reaction pathway, the generation of H2O2 was through a less common two-electron water oxidation reaction(WOR) process in which both the direct and indirect WOR processesoccurred; namely, photoinduced h(+) directly oxidized H2O to H2O2 via a one-step 2e(-) WOR, and photoinduced h(+) first oxidized a hydroxide (OH-) ion to generate a hydroxy radical ((OH)-O-& BULL;), and H2O2 was formed indirectly by thecombination of two (OH)-O-& BULL;. We have characterized thematerial, at the catalytic sites, at the atomic level using electronparamagnetic resonance, X-ray absorption near edge structure, extendedX-ray absorption fine structure, high-resolution transmission electronmicroscopy, X-ray photoelectron spectroscopy, magic-angle spinningsolid-state NMR spectroscopy, and multiscale molecular modeling, combiningclassical reactive molecular dynamics simulations and quantum chemistrycalculations. |
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Wos |
001034983300001 |
Publication Date |
2023-07-24 |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0002-7863 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
15 |
Times cited |
21 |
Open Access |
Not_Open_Access |
| Notes |
S.D. thanks the IOF grant and Francqui start up grant from the University of Antwerp, Belgium, for the financial support. P.R. thanks CSC and T.Z. thanks FWO for their financial assistance to finish this work. E.D. would like to thank the KU Leuven Research Fund for financial support through STG/21/010. J.S.A. acknowledges financial support from MCIN/AEI/10.13039/501100011033 and EU NextGeneration/PRTR (Project PCI2020-111968/3D-Photocat) and Diamond Synchrotron (rapid access proposal SP32609). This work was supported by the European Research Council (grant 770887-PICOMETRICS to S.V.A. and Grant 815128-REALNANO to S.B.). S.B. and S.V.A. acknowledge financial support from the Research Foundation Flanders (FWO, Belgium, project G.0346.21 N). We also thank Mr. Jian Zhu and Mr. Shahid Ullah Khan from the University of Antwerp, Belgium, for helpful discussions. |
Approved |
Most recent IF: 15; 2023 IF: 13.858 |
| Call Number |
UA @ admin @ c:irua:198426 |
Serial |
8831 |
| Permanent link to this record |
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| Author |
Friedrich, T.; Yu, C.-P.; Verbeeck, J.; Van Aert, S. |
| Title |
Phase object reconstruction for 4D-STEM using deep learning |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
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| Volume |
29 |
Issue |
1 |
Pages |
395-407 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
In this study, we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently, a corresponding patch of the phase object is recovered using the phase object approximation. Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full-phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation, and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on the thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness, and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM. |
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Wos |
001033590800038 |
Publication Date |
2023-01-12 |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1431-9276 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
2.8 |
Times cited |
1 |
Open Access |
OpenAccess |
| Notes |
We acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 770887 PICOMETRICS) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 823717 ESTEEM3. J.V. and S.V.A acknowledge funding from the University of Antwerp through a TOP BOF project. The direct electron detector (Merlin, Medipix3, Quantum Detectors) was funded by the Hercules fund from the Flemish Government. This work was supported by the FWO and FNRS within the 2Dto3D project of the EOS program (grant number 30489208). |
Approved |
Most recent IF: 2.8; 2023 IF: 1.891 |
| Call Number |
UA @ admin @ c:irua:198221 |
Serial |
8912 |
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| Author |
Monai, M.; Jenkinson, K.; Melcherts, A.E.M.; Louwen, J.N.; Irmak, E.A.; Van Aert, S.; Altantzis, T.; Vogt, C.; van der Stam, W.; Duchon, T.; Smid, B.; Groeneveld, E.; Berben, P.; Bals, S.; Weckhuysen, B.M. |
| Title |
Restructuring of titanium oxide overlayers over nickel nanoparticles during catalysis |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Science |
Abbreviated Journal |
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| Volume |
380 |
Issue |
6645 |
Pages |
644-651 |
| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Applied Electrochemistry & Catalysis (ELCAT) |
| Abstract |
Reducible supports can affect the performance of metal catalysts by the formation of suboxide overlayers upon reduction, a process referred to as the strong metal-support interaction (SMSI). A combination of operando electron microscopy and vibrational spectroscopy revealed that thin TiOx overlayers formed on nickel/titanium dioxide catalysts during 400 degrees C reduction were completely removed under carbon dioxide hydrogenation conditions. Conversely, after 600 degrees C reduction, exposure to carbon dioxide hydrogenation reaction conditions led to only partial reexposure of nickel, forming interfacial sites in contact with TiOx and favoring carbon-carbon coupling by providing a carbon species reservoir. Our findings challenge the conventional understanding of SMSIs and call for more-detailed operando investigations of nanocatalysts at the single-particle level to revisit static models of structure-activity relationships. |
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Thesis |
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Place of Publication |
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Wos |
000999020900010 |
Publication Date |
2023-05-11 |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0036-8075; 1095-9203 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
56.9 |
Times cited |
29 |
Open Access |
OpenAccess |
| Notes |
This work was supported by BASF and NWO CHIPP (research grant to B.M.W.); the MCEC NWO Gravitation Program (B.M.W.); the ARC-CBBC NWO Program (B.M.W.); the European Research Council (grant 770887 PICOMETRICS to S.V.A.); and the European Research Council (grant 815128 REALNANO to S.B.). |
Approved |
Most recent IF: 56.9; 2023 IF: 37.205 |
| Call Number |
UA @ admin @ c:irua:197432 |
Serial |
8923 |
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| Author |
Zhang, Z.; Lobato, I.; Brown, H.; Jannis, D.; Verbeeck, J.; Van Aert, S.; Nellist, P. |
| Title |
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions |
Type |
Dataset |
| Year |
2023 |
Publication |
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Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Dataset; Electron microscopy for materials research (EMAT) |
| Abstract |
Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1]. The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions. We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration. Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3] Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference. Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script. Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689. |
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UA library record |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:203392 |
Serial |
9042 |
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| Author |
Hugenschmidt, M.; Jannis, D.; Kadu, A.A.; Grünewald, L.; De Marchi, S.; Perez-Juste, J.; Verbeeck, J.; Van Aert, S.; Bals, S. |
| Title |
Low-dose 4D-STEM tomography for beam-sensitive nanocomposites |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
ACS materials letters |
Abbreviated Journal |
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| Volume |
6 |
Issue |
1 |
Pages |
165-173 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
Electron tomography is essential for investigating the three-dimensional (3D) structure of nanomaterials. However, many of these materials, such as metal-organic frameworks (MOFs), are extremely sensitive to electron radiation, making it difficult to acquire a series of projection images for electron tomography without inducing electron-beam damage. Another significant challenge is the high contrast in high-angle annular dark field scanning transmission electron microscopy that can be expected for nanocomposites composed of a metal nanoparticle and an MOF. This strong contrast leads to so-called metal artifacts in the 3D reconstruction. To overcome these limitations, we here present low-dose electron tomography based on four-dimensional scanning transmission electron microscopy (4D-STEM) data sets, collected using an ultrafast and highly sensitive direct electron detector. As a proof of concept, we demonstrate the applicability of the method for an Au nanostar embedded in a ZIF-8 MOF, which is of great interest for applications in various fields, including drug delivery. |
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Wos |
001141178500001 |
Publication Date |
2023-12-11 |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
2639-4979 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
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Times cited |
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Open Access |
Not_Open_Access |
| Notes |
This work was supported by the European Research Council (Grant 815128 REALNANO to S.B., Grant 770887 PICOMETRICS to S.V.A.). J.P.-J. and S.M. acknowledge financial support from the MCIN/AEI/10.13039/501100011033 (Grants No. PID2019-108954RB-I00) and EU Horizon 2020 research and innovation program under grant agreement no. 883390 (SERSing). J.V., S.B., S.V.A., and L.G. acknowledge funding from the Flemish government (iBOF-21-085 PERsist). |
Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:202771 |
Serial |
9053 |
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| Author |
Grünewald, L.; Chezganov, D.; De Meyer, R.; Orekhov, A.; Van Aert, S.; Bogaerts, A.; Bals, S.; Verbeeck, J. |
| Title |
Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” |
Type |
Dataset |
| Year |
2023 |
Publication |
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Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
| Abstract |
Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:203389 |
Serial |
9100 |
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| Author |
Cioni, M.; Delle Piane, M.; Polino, D.; Rapetti, D.; Crippa, M.; Arslan Irmak, E.; Pavan, G.M.; Van Aert, S.; Bals, S. |
| Title |
Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning |
Type |
Dataset |
| Year |
2024 |
Publication |
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Abbreviated Journal |
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Issue |
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Pages |
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Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. |
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UA library record |
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Open Access |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:205843 |
Serial |
9143 |
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