| Records |
| Author |
Ying, J.; Xiao, Y.; Chen, J.; Hu, Z.-Y.; Tian, G.; Van Tendeloo, G.; Zhang, Y.; Symes, M.D.D.; Janiak, C.; Yang, X.-Y. |
| Title |
Fractal design of hierarchical PtPd with enhanced exposed surface atoms for highly catalytic activity and stability |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
Nano letters |
Abbreviated Journal |
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| Volume |
23 |
Issue |
16 |
Pages |
7371-7378 |
| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
Hierarchicalassembly of arc-like fractal nanostructures not onlyhas its unique self-similarity feature for stability enhancement butalso possesses the structural advantages of highly exposed surface-activesites for activity enhancement, remaining a great challenge for high-performancemetallic nanocatalyst design. Herein, we report a facile strategyto synthesize a novel arc-like hierarchical fractal structure of PtPdbimetallic nanoparticles (h-PtPd) by using pyridinium-type ionic liquidsas the structure-directing agent. Growth mechanisms of the arc-likenanostructured PtPd nanoparticles have been fully studied, and precisecontrol of the particle sizes and pore sizes has been achieved. Dueto the structural features, such as size control by self-similaritygrowth of subunits, structural stability by nanofusion of subunits,and increased numbers of exposed active atoms by the curved homoepitaxialgrowth, h-PtPd displays outstanding electrocatalytic activity towardoxygen reduction reaction and excellent stability during hydrothermaltreatment and catalytic process. |
| Address |
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Thesis |
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Place of Publication |
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Wos |
001042181100001 |
Publication Date |
2023-08-03 |
| 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 |
1530-6984 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
10.8 |
Times cited |
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Open Access |
Not_Open_Access |
| Notes |
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Approved |
Most recent IF: 10.8; 2023 IF: 12.712 |
| Call Number |
UA @ admin @ c:irua:198408 |
Serial |
8870 |
| Permanent link to this record |
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| Author |
Chai, Z.-N.; Wang, X.-C.; Yusupov, M.; Zhang, Y.-T. |
| Title |
Unveiling the interaction mechanisms of cold atmospheric plasma and amino acids by machine learning |
Type |
A1 Journal article |
| Year |
2024 |
Publication |
Plasma processes and polymers |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-26 |
| Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
| Abstract |
Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image |
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Wos |
001202061200001 |
Publication Date |
2024-04-15 |
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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 |
1612-8850 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
3.5 |
Times cited |
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Open Access |
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| Notes |
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Approved |
Most recent IF: 3.5; 2024 IF: 2.846 |
| Call Number |
UA @ admin @ c:irua:205512 |
Serial |
9181 |
| Permanent link to this record |
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| Author |
Ghosh, S.; Pradhan, B.; Bandyopadhyay, A.; Skvortsova, I.; Zhang, Y.; Sternemann, C.; Paulus, M.; Bals, S.; Hofkens, J.; Karki, K.J.; Materny, A. |
| Title |
Rashba-type band splitting effect in 2D (PEA)₂PbI₄ perovskites and its impact on exciton-phonon coupling |
Type |
A1 Journal article |
| Year |
2024 |
Publication |
The journal of physical chemistry letters |
Abbreviated Journal |
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| Volume |
15 |
Issue |
31 |
Pages |
7970-7978 |
| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
Despite a few recent reports on Rashba effects in two-dimensional (2D) Ruddlesden-Popper (RP) hybrid perovskites, the precise role of organic spacer cations in influencing Rashba band splitting remains unclear. Here, using a combination of temperature-dependent two-photon photoluminescence (2PPL) and time-resolved photoluminescence spectroscopy, alongside density functional theory (DFT) calculations, we contribute to significant insights into the Rashba band splitting found for 2D RP hybrid perovskites. The results demonstrate that the polarity of the organic spacer cation is crucial in inducing structural distortions that lead to Rashba-type band splitting. Our investigations show that the intricate details of the Rashba band splitting occur for organic cations with low polarity but not for more polar ones. Furthermore, we have observed stronger exciton-phonon interactions due to the Rashba-type band splitting effect. These findings clarify the importance of selecting appropriate organic spacer cations to manipulate the electronic properties of 2D perovskites. |
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Place of Publication |
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Wos |
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=brocade2&SrcAuth=WosAPI&KeyUT=WOS:001280 |
Publication Date |
2024-07-30 |
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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 |
1948-7185 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
5.7 |
Times cited |
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Open Access |
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| Notes |
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Approved |
Most recent IF: 5.7; 2024 IF: 9.353 |
| Call Number |
UA @ admin @ c:irua:207672 |
Serial |
9313 |
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| Author |
Zhang, Y.; Grünewald, L.; Cao, X.; Abdelbarey, D.; Zheng, X.; Rugeramigabo, E.P.; Zopf, M.; Verbeeck, J.; Ding, F. |
| Title |
Supplementary Information and Data for “Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots” |
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Dataset |
| Year |
2024 |
Publication |
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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 |
Raw and processed TEM and AFM data for the article Unveiling the 3D Morphology of Epitaxial GaAs/AlGaAs Quantum Dots. |
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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 |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:208086 |
Serial |
9319 |
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| Author |
Zhang, Y.; Grunewald, L.; Cao, X.; Abdelbarey, D.; Zheng, X.; Rugeramigabo, E.P.; Verbeeck, J.; Zopf, M.; Ding, F. |
| Title |
Unveiling the 3D morphology of epitaxial GaAs/AlGaAs quantum dots |
Type |
A1 Journal article |
| Year |
2024 |
Publication |
Nano letters |
Abbreviated Journal |
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| Volume |
24 |
Issue |
33 |
Pages |
10106-10113 |
| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
Strain-free GaAs/AlGaAs semiconductor quantum dots (QDs) grown by droplet etching and nanohole infilling (DENI) are highly promising candidates for the on-demand generation of indistinguishable and entangled photon sources. The spectroscopic fingerprint and quantum optical properties of QDs are significantly influenced by their morphology. The effects of nanohole geometry and infilled material on the exciton binding energies and fine structure splitting are well-understood. However, a comprehensive understanding of GaAs/AlGaAs QD morphology remains elusive. To address this, we employ high-resolution scanning transmission electron microscopy (STEM) and reverse engineering through selective chemical etching and atomic force microscopy (AFM). Cross-sectional STEM of uncapped QDs reveals an inverted conical nanohole with Al-rich sidewalls and defect-free interfaces. Subsequent selective chemical etching and AFM measurements further reveal asymmetries in element distribution. This study enhances the understanding of DENI QD morphology and provides a fundamental three-dimensional structural model for simulating and optimizing their optoelectronic properties. |
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Wos |
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=brocade2&SrcAuth=WosAPI&KeyUT=WOS:001280 |
Publication Date |
2024-07-25 |
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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 |
1530-6984 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
10.8 |
Times cited |
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Open Access |
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| Notes |
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Approved |
Most recent IF: 10.8; 2024 IF: 12.712 |
| Call Number |
UA @ admin @ c:irua:207525 |
Serial |
9326 |
| Permanent link to this record |
