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Bhatia, H.; Keshavarz, M.; Martin, C.; Van Gaal, L.; Zhang, Y.; de Coen, B.; Schrenker, N.J.; Valli, D.; Ottesen, M.; Bremholm, M.; Van de Vondel, J.; Bals, S.; Hofkens, J.; Debroye, E. |
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Title |
Achieving High Moisture Tolerance in Pseudohalide Perovskite Nanocrystals for Light-Emitting Diode Application |
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A1 Journal Article |
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Year |
2023 |
Publication |
ACS Applied Optical Materials |
Abbreviated Journal |
ACS Appl. Opt. Mater. |
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Volume |
1 |
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6 |
Pages |
1184-1191 |
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Keywords |
A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ; |
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Abstract |
The addition of potassium thiocyanate (KSCN) to the FAPbBr3 structure and subsequent post-treatment of nanocrystals (NCs) lead to high quantum confinement, resulting in a photoluminescent quantum yield (PLQY) approaching unity and microsecond decay times. This synergistic approach demonstrated exceptional stability under humid conditions, retaining 70% of the PLQY for over a month, while the untreated NCs degrade within 24 h. Additionally, the devices incorporating the post-treated NCs displayed 1.5% external quantum efficiency (EQE), a 5-fold improvement over untreated devices. These results provide promising opportunities for the use of perovskites in moisture-stable optoelectronics. |
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Wos |
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Publication Date |
2023-06-23 |
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ISSN |
2771-9855 |
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UA library record |
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Open Access |
OpenAccess |
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Notes |
Hercules Foundation, HER/11/14 ; European Commission; Ministerio de Ciencia e Innovaci?n, PID2021-128761OA-C22 ; European Regional Development Fund; Vlaamse regering, CASAS2 Meth/15/04 ; Fonds Wetenschappelijk Onderzoek, 1238622N 1514220N 1S45223N G.0B39.15 G.0B49.15 G098319N S002019N ZW15_09-GOH6316 ; Onderzoeksraad, KU Leuven, C14/19/079 db/21/006/bm iBOF-21-085 STG/21/010 ; Junta de Comunidades de Castilla-La Mancha, SBPLY/21/180501/000127 ; H2020 European Research Council, 642196 815128 ; |
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Most recent IF: NA |
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Call Number |
EMAT @ emat @c:irua:201011 |
Serial |
8975 |
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Author |
Chai, Z.-N.; Wang, X.-C.; Yusupov, M.; Zhang, Y.-T. |
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Title |
Unveiling the interaction mechanisms of cold atmospheric plasma and amino acids by machine learning |
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A1 Journal article |
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Year |
2024 |
Publication |
Plasma processes and polymers |
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1-26 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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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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001202061200001 |
Publication Date |
2024-04-15 |
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1612-8850 |
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UA library record; WoS full record |
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Impact Factor |
3.5 |
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Most recent IF: 3.5; 2024 IF: 2.846 |
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Call Number |
UA @ admin @ c:irua:205512 |
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9181 |
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