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Agnieszka Patrycja Seremak
Publikasjoner
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Njoroge, Peter Ng'ang'a; Solemsli, Bjørn Gading; Wijerathne, Asanka; Berdiell, Izar Capel; Seremak, Agnieszka Patrycja & Chiesa, Mario
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(2025).
Influence of Aluminum Distribution in Cu-MOR Systems on Methane-to-Methanol Conversion: A Combined Experimental and Theoretical Study.
Journal of Physical Chemistry C.
ISSN 1932-7447.
129(44),
s. 20044–20056.
doi:
10.1021/acs.jpcc.5c06045.
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A series of copper-mordenite (MOR) samples of different provenances were investigated in the methane-to-methanol (MTM) reaction after preparing their copper-exchanged analogues. Noticeable activity improvements were observed when biasing the Al framework distribution of the confined side-pocket toward 12-ring openings (T2 and T4 enrichment) over 8-ring openings (T1 and T3), achieved by using K+ or Na+ in the synthesis gel, respectively. This was rationalized by performing a geometry optimization algorithm using density functional theory (DFT) simulations, which revealed distortions in the structure of the pores among different idealized zeolite models. From this, effects on the copper species were observed, as evidenced from both electron paramagnetic resonance (EPR) spectroscopy and operando X-ray absorption spectroscopy (XAS), which suggested varying monomeric [Cu]2+/[CuOH]+ concentrations with intrinsic copper reducibility differences. Monte Carlo simulations on selected MOR structures of the experimental series exposed dimeric structures with more acute Cu–O–Cu angles, thereby suggesting a more reactive system for Cu-MOR based on K+ in the synthesis gel, in line with the experimental finding. The combined insights from simulations, calculations, and experiments have enabled us to establish a synthesis-structure–activity relationship for mordenite in methane conversion, highlighting the reactive interplay between pore geometry and copper speciation.
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Seremak, Agnieszka; Goeminne, Ruben; Berdiell, Izar Capel; Lundegaard, Lars F.; Speybroeck, Veronique Van & Svelle, Stian
(2025).
Exploring the guest–host relationship for zeolite Y: a synergistic structural and theoretical investigation.
Journal of Materials Chemistry A.
ISSN 2050-7488.
13,
s. 35755–35755.
doi:
10.1039/d5ta04948b.
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Understanding the mobility and distribution of cations in the presence of additional guest species within the zeolite framework is inherently very complex because several thermal and chemical processes occur simultaneously. In this study, the dehydration of zeolite Na-Y was studied using in situ powder X-ray diffraction (XRD). The crystal structure parameters, both the evolution of lattice parameters and the occupancies of sodium at specific positions in the model, were monitored upon heating. A complementary computational study was performed to understand interactions between the framework, cations, and water at the molecular level. Grand Canonical Monte Carlo (GCMC) simulations provided initial insights into water adsorption. Machine Learning Potentials (MLPs) were then trained to the ab initio Potential Energy Surface (PES) using a deep neural network, modeling the dynamics of the framework and cations at various water loadings. Strong agreement between computational results and experimental data reveal that upon dehydration, zeolite Na-Y initially contracts due to water removal, but subsequently expands as sodium cations migrate to the double 6-membered rings (site I). This study demonstrates significant benefits of integrating parametric Rietveld refinement and Machine Learning assisted Molecular Dynamics simulations in understanding dynamic behavior of guest molecules in nanoporous materials at operating conditions and interpreting complex and convoluted experimental data.
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Seremak, Agnieszka Patrycja; Berdiell, Izar Capel; Arstad, Bjørnar; Fjermestad, Torstein & Svelle, Stian
(2025).
Investigating the Flexibility of H-ZSM-5 Zeolite Upon Adsorption of Coke Precursors: A Theoretical and Experimental Approach.
Journal of Physical Chemistry C.
ISSN 1932-7447.
129(2),
s. 1183–1197.
doi:
10.1021/acs.jpcc.4c07349.
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The flexibility of the H-ZSM-5 zeolite upon adsorption of selected coke precursors was investigated using both theoretical and experimental approaches. Four structural models with varying active site locations were analyzed through density functional theory (DFT) simulations to determine their responses to different types and quantities of aromatic molecules. Complementary experimental analysis was performed, allowing for a direct comparison with the theoretical findings, using thermogravimetric analysis (TGA), nitrogen adsorption (N2 adsorption), solid-state NMR, and X-ray diffraction (XRD). By employing proposed flexibility descriptors, significant structural changes in the MFI-type zeolite framework were identified, particularly in the unit cell parameters and the morphology of the straight channels. These changes were driven by electrostatic repulsion between adsorbates and by electrostatic attraction between adsorbates and the zeolite framework. The observed structural changes depended on both the active site location and the size and number of coke precursors. Consistent trends in structural flexibility were observed in both experimental and theoretical studies, primarily driven by variations in organic species loading. Our findings show the critical importance of active site location in influencing the magnitude of framework flexibility, which, in turn, affects the stabilization and accommodation of different coke precursors within the zeolite structure.
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Seremak, Agnieszka Patrycja
(2025).
Na-Y Zeolite Water Interaction: A Multimodal Analysis Using GCMC, Machine Learning Potentials and Operando X-Ray Diffraction.
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Seremak, Agnieszka
(2024).
Flexibility of Na-Y faujasite zeolite upon water adsorption/desorption: application of GCMC & Machine Learning Potentials.
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Seremak, Agnieszka
(2024).
Understanding Cation Mobility and Framework Flexibility in Na-Y Zeolite During Water Adsorption/Desorption via GCMC and Machine Learning Potentials.
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Seremak, Agnieszka Patrycja
(2024).
Flexibility of Na-Y faujasite zeolite upon water adsorption/desorption: application of GCMC & Machine Learning Potentials.
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Seremak, Agnieszka Patrycja; Goeminne, Ruben; Berdiell, Izar Capel; Speybroeck, Veronique Van & Svelle, Stian
(2024).
Understanding Cation Mobility and Framework Flexibility in Na-Y Zeolite During Water Adsorption/Desorption via GCMC and Machine Learning Potentials.
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Seremak, Agnieszka Patrycja
(2023).
Theoretical study of local and global deformation of H-ZSM-5.
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Seremak, Agnieszka Patrycja
(2026).
Molecular Insights into Guest-Host Interactions in Zeolite and Zeotype Catalysts.
Universitetet i Oslo.
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Zeolites and zeotypes are crystalline materials with pores so small that only individual molecules can fit inside them. They are essential in industrial processes such as catalysis and gas separation, and understanding how they behave at the atomic level is key to designing better and more durable versions of them.
But their internal structure is far more dynamic than it appears. When molecules enter the pores, the framework shifts and reorganises in ways that are difficult to predict and nearly impossible to observe directly through experiments alone. These changes affect how well the material performs and how quickly it breaks down, yet the molecular level mechanisms behind them remain poorly understood.
Therefore, this thesis uses computer simulations to investigate three distinct examples of these structural responses. Aromatic molecules confined in an industrially relevant zeolite were found to deform the framework in ways that help explain why some zeolites deactivate faster than others. Removing water from a sodium containing zeolite was shown to trigger sodium migration, causing the framework to first contract and then expand through a coupled mechanism experiments cannot easily resolve. A third study tackled how zeotype break down in water, identifying where current methods fall short and pointing toward machine learning based approaches as the way forward.
Together, these findings build a molecular level foundation for understanding and ultimately predicting how zeolites and zeotypes respond to their chemical environment.
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Publisert
11. nov. 2022 16:13
- Sist endret
17. jan. 2025 14:19
CompSci is a Doctoral Programme (2021-2026) launched and managed by the Faculty of Mathematics and Natural Sciences at the University of Oslo (UiO).
CompSci combines a disciplinary doctoral programme with intensive training in computational and data science. It is partly funded by the EU Horizon 2020 under the Marie Skłodowska-Curie Action (MSCA) - Co-funding of Regional, National and International Programmes (COFUND).
MSCA grant agreement Nº 945371