| 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 |
|
| 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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| 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 |
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 |
| 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 |
001202061200001 |
Publication Date |
2024-04-15 |
| 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 |
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 |
|
| Notes |
|
Approved |
Most recent IF: 3.5; 2024 IF: 2.846 |
| Call Number |
UA @ admin @ c:irua:205512 |
Serial |
9181 |
| Permanent link to this record |
