| Records |
| Author |
Wang, K.; Ceulemans, S.; Zhang, H.; Tsonev, I.; Zhang, Y.; Long, Y.; Fang, M.; Li, X.; Yan, J.; Bogaerts, A. |
| Title |
Inhibiting recombination to improve the performance of plasma-based CO2 conversion |
Type |
A1 Journal Article |
| Year |
2024 |
Publication |
Chemical Engineering Journal |
Abbreviated Journal |
Chemical Engineering Journal |
| Volume |
481 |
Issue |
|
Pages |
148684 |
| Keywords |
A1 Journal Article; Plasma-based CO2 splitting Recombination reactions In-situ gas sampling Fluid dynamics modeling Kinetics modeling Afterglow quenching; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ; |
| Abstract |
Warm plasma offers a promising route for CO2 splitting into valuable CO, yet recombination reactions of CO with oxygen, forming again CO2, have recently emerged as critical limitation. This study combines experiments and fluid dynamics + chemical kinetics modelling to comprehensively analyse the recombination reactions upon CO2 splitting in an atmospheric plasmatron. We introduce an innovative in-situ gas sampling technique, enabling 2D spatial mapping of gas product compositions and temperatures, experimentally confirming for the first time the substantial limiting effect of CO recombination reactions in the afterglow region. Our results show that the CO mole fraction at a 5 L/min flow rate drops significantly from 11.9 % at a vertical distance of z = 20 mm in the afterglow region to 8.6 % at z = 40 mm. We constructed a comprehensive 2D model that allows for spatial reaction rates analysis incorporating crucial reactions, and we validated it to kinetically elucidate this phenomenon. CO2 +M⇌O+CO+M and CO2 +O⇌CO+O2 are the dominant reactions, with the forward reactions prevailing in the plasma region and the backward reactions becoming prominent in the afterglow region. These results allow us to propose an afterglow quenching strategy for performance enhancement, which is further demonstrated through a meticulously developed plasmatron reactor with two-stage cooling. Our approach substantially increases the CO2 conversion (e.g., from 6.6 % to 19.5 % at 3 L/min flow rate) and energy efficiency (from 13.5 % to 28.5 %, again at 3 L/min) and significantly shortens the startup time (from ~ 150 s to 25 s). Our study underscores the critical role of inhibiting recombination reactions in plasma-based CO2 conversion and offers new avenues for performance enhancement. |
| 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  |
001168999200001 |
Publication Date |
2024-01-10 |
| 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 |
1385-8947 |
ISBN |
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Additional Links |
UA library record; WoS full record |
| Impact Factor |
15.1 |
Times cited |
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Open Access |
Not_Open_Access |
| Notes |
Key Research and Development Program of Zhejiang Province, 2023C03129 ; Vlaamse regering; European Research Council; National Natural Science Foundation of China, 51976191 52276214 ; Horizon 2020 Framework Programme; Fonds De La Recherche Scientifique – FNRS; Fonds Wetenschappelijk Onderzoek, 1101524N ; Vlaams Supercomputer Centrum; Horizon 2020, 101081162 810182 ; European Research Council; |
Approved |
Most recent IF: 15.1; 2024 IF: 6.216 |
| Call Number |
PLASMANT @ plasmant @c:irua:204352 |
Serial |
8993 |
| 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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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 |
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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 |
