| Records |
| Author |
Cioni, M.; Delle Piane, M.; Polino, D.; Rapetti, D.; Crippa, M.; Arslan Irmak, E.; Van Aert, S.; Bals, S.; Pavan, G.M. |
| Title |
Sampling real-time atomic dynamics in metal nanoparticles by combining experiments, simulations, and machine learning |
Type |
A1 Journal article |
Year  |
2024 |
Publication |
Advanced Science |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-13 |
| Keywords |
A1 Journal article; 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 allow 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. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
001206888000001 |
Publication Date |
2024-04-24 |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2198-3844 |
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 |
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| Notes |
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Approved |
no |
| Call Number |
UA @ admin @ c:irua:205442 |
Serial |
9171 |
| Permanent link to this record |
| |
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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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| Volume |
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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) |
| 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Wos |
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Publication Date |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Additional Links |
UA library record |
| Impact Factor |
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Times cited |
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Open Access |
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| Notes |
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Approved |
no |
| Call Number |
UA @ admin @ c:irua:205843 |
Serial |
9143 |
| Permanent link to this record |
