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Projects / Programmes source: ARIS

Multiscale modeling of photocatalytic CO2 reduction with computer intensive simulations (multiPHOCOS)

Research activity

Code Science Field Subfield
1.07.00  Natural sciences and mathematics  Computer intensive methods and applications   

Code Science Field
1.01  Natural Sciences  Mathematics 
Keywords
multi-scale modelling, photocatalysis, simulations, lateral interactions, model, optimization, machine learning, CO2, reduction reaction, methanol
Evaluation (rules)
source: COBISS
Points
14,695.79
A''
7,837.97
A'
10,753.74
A1/2
12,727.12
CI10
15,728
CImax
2,772
h10
59
A1
37.98
A3
15.75
Data for the last 5 years (citations for the last 10 years) on April 23, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  549  15,672  14,285  26.02 
Scopus  576  17,406  15,966  27.72 
Researchers (12)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  34342  PhD Matej Huš  Chemical engineering  Researcher  2021 - 2024  687 
2.  51704  PhD Khaja Mohaideen Kamal Musthafa  Chemical engineering  Researcher  2021 - 2024  23 
3.  32002  PhD Drejc Kopač  Physics  Head  2021 - 2024  118 
4.  51193  PhD Andrii Kostyniuk  Chemical engineering  Researcher  2021 - 2024  61 
5.  53962  Žan Kovačič  Chemical engineering  Researcher  2021 - 2024  26 
6.  25446  PhD Blaž Likozar  Chemical engineering  Researcher  2021 - 2024  1,214 
7.  13311  PhD Marjeta Maček Kržmanc  Materials science and technology  Researcher  2021 - 2024  182 
8.  34528  PhD Andraž Pavlišič  Materials science and technology  Researcher  2022 - 2024  106 
9.  29399  PhD Andrej Pohar  Chemical engineering  Researcher  2021  157 
10.  39350  PhD Anže Prašnikar  Chemical engineering  Technical associate  2021 - 2024  64 
11.  24273  PhD Matjaž Spreitzer  Materials science and technology  Researcher  2021 - 2024  363 
12.  54620  Taja Žibert  Chemical engineering  Technical associate  2021 - 2024  19 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0104  National Institute of Chemistry  Ljubljana  5051592000  20,982 
2.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,706 
Abstract
The overall objective of the proposed project is to study the photocatalytic CO2 hydrogenation to methanol via multi-scale modelling approach. We will systematically employ ab-initio density functional theory (DFT) simulations to model chemical reaction pathway on perovskite-type photocatalytic materials. Next, we will incorporate accurate adsorbate-adsorbate lateral interactions at meso-scale via kinetic Monte Carlo simulations. The results of the theoretical research will be evaluated within a case study, where the modelled photocatalyst will be synthesized and photocatalytic CO2 activation will be experimentally performed. The case study will serve as a confirmation of the accuracy of our multi-scale computer modelling. While CO2 emission is steadily rising and its level in the atmosphere is alarming, catalytic CO2 hydrogenation is and will remain a sustainable way to prevent further increase of the CO2 concentration. Hydrogenation of CO2 into hydrocarbon fuels, in particular methanol, is an attractive solution for energy storage. In particular, photocatalytic CO2 reduction is an energetically advantageous approach, which has recently gained a lot of attention. Using solar energy directly in the photocatalytic process, the conversion is sustainable, with zero carbon footprint. The key ingredient for a successful photocatalytic CO2 hydrogenation to methanol is the use of an efficient and optimized catalyst, with superior activity and durability. Since a plethora of various candidate materials and geometries are available, finding a perfect photocatalyst requires a large number of screening experiments to be conducted. With in silico approach, we will study a photocatalytically active material candidates, such as SrTiO3 perovskite surface. We will build on our previous works to reproduce the CO2 reduction reaction pathway mechanism within the photocatalytic process, in which the wavelength of incident light helps in promoting the material activity, by lowering the activation energy barriers of rate-determining steps. The obtained energetics and the proposed reaction mechanism will be used in the meso-scale kinetic Monte Carlo (kMC) simulations. The emphasis will be given to the accurate adsorbate lateral interactions incorporation via cluster expansion Hamiltonian. The methodology has been tested on various simple systems, however it is computational demanding when used on complex reaction pathways with many intermediates, which will be the case with CO2 hydrogenation to methanol. We will employ the framework to our system, and study the lateral interactions effect on different levels, moving from short-range to long-range interactions. We will study to what extent the accurate description of lateral interaction affects the catalytic performance, and to what accuracy should the lateral interaction be incorporated to obtain a realistic description of the reaction in experimentally relevant conditions. Lastly, the case study in collaboration with Jozef Stefan Institute will serve as a validation of the theoretical modelling, while providing experimental correlations between the catalyst structure and performance. The promising catalysts will be synthesized using precise synthetic control over the exposed crystal facets and terminations. The characterization will confirm the similarity of the synthesized catalyst and the modelled one, while photocatalytic experiments will provide experimental catalytic activity and selectivity. Comparing different kMC simulations results will give us a direct evidence of the accuracy of lateral interaction that is experimentally relevant. Furthermore, comparison with the experimental results, in particular energetics, will serve as an indication of how precise the DFT methodology is for our modelled reaction.
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