Researcher:
Demir, Hakan

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Master Student

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Hakan

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Demir

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Demir, Hakan

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2021 - 20236

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    Recent advances in computational modeling of MOFs: from molecular simulations to machine learning
    (Elsevier B.V., 2023) N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Demir, Hakan; Harman, Hilal Dağlar; Gülbalkan, Hasan Can; Aksu, Gökhan Önder; Keskin, Seda; Other; PhD Student; PhD Student; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; N/A; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; N/A; 40548
    The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an almost boundless number of materials some of which can be a substitute for the traditionally used porous materials in various fields including gas storage and separation, catalysis, drug storage and delivery. The number of MOFs and their potential applications are growing so quickly that, when novel MOFs are synthesized, testing them for all possible applications is not practical. High-throughput computational screening approaches based on molecular simulations of materials have been widely used to investigate MOFs and identify the optimal MOFs for a specific application. Despite the growing computational resources, given the enormous MOF material space, computational identification of promising MOFs requires more efficient approaches in terms of time and effort. Leveraging data-driven science techniques can offer key benefits such as accelerated MOF design and discovery pathways via the establishment of machine learning (ML) models and interpretation of complex structure-performance relationships that can reach beyond expert intuition. In this review, we present key scientific breakthroughs that propelled computational modeling of MOFs and discuss the state-of-the-art approaches extending from molecular simulations to ML algorithms. Finally, we provide our perspective on the potential opportunities and challenges for the future of big data-driven MOF design and discovery. © 2023 The Authors
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    PublicationOpen Access
    Hypothetical yet effective: computational identification of high-performing MOFs for CO2 capture
    (Elsevier, 2022) Department of Chemical and Biological Engineering; Demir, Hakan; Keskin, Seda; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; N/A; 40548
    With the advances in computational resources and algorithms, computer simulations are being increasingly used to tackle the most challenging problems of the world. Among them, CO2 capture is a topic that needs imminent attention as the presence of high levels of CO2 in the air can lead to drastic shifts in global climate. Here, a recently developed hypothetical metal-organic framework (MOF) database comprised of anion-pillared (AP) MOFs is computationally screened for the separation of CO2/CO, CO2/H-2, and CO2/N-2 gas mixtures at room temperature. The best performing MOFs are identified using three performance metrics, adsorption selectivity, working capacity, and regenerability, in conjunction. In these top materials, the preferential adsorption sites are illustrated, which will be useful in guiding the experimental design of new MOFs with extraordinarily high CO2 selectivities. The favorable separation performances of AP MOFs suggest that efficient gas separations can be conducted using MOFs without open metal sites.
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    PublicationOpen Access
    Computational insights into efficient CO2 and H2S capture through zirconium MOFs
    (Elsevier, 2022) Department of Chemical and Biological Engineering; Keskin, Seda; Demir, Hakan; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 40548; N/A
    Separation of CO2 involving mixtures is relevant to the various industrial settings and preserving environment for which different classes of materials including metal-organic frameworks (MOFs) have been researched. Herein, CO2/CO, CO2/H-2, CO2/N-2, and H2S/CO2 separation properties of the zirconium MOFs are computationally investigated mimicking vacuum swing adsorption (VSA) process. Structure-performance relationships are established and the best performing adsorbent materials are determined considering three performance metrics: adsorption selectivity, working capacity, and regenerability. For CO2/CO separation in dry conditions, PCN-59, BUT-10, and PCN-58 are identified to be the top three materials with CO2/CO selectivities of 219.8, 47.2, and 28.6, CO2 working capacities of 6.9, 5.3, and 4.0 mol/kg, CO2 regenerabilities of 63.3, 82.1, and 87.2 %, successively. In humid conditions, UiO-66-OH and MOF-805 appear promising for CO2/CO separation. Regarding CO2/H-2 separation in dry conditions, PCN-59, BUT-10, and LIFM-94 are ranked as the top three MOFs exhibiting CO2/H-2 selectivities of 1445.6, 378.1, and 411.3, CO2 working capacities of 3.6, 2.4, and 2.2 mol/kg, and CO2 regenerabilities of 56.6, 84.9, and 83.9 %, successively. These three materials are also found to be the top three materials for CO2/N-2 separation in dry conditions with CO2/N-2 selectivities of 346.0, 53.3, and 54.9, CO2 working capacities of 3.6, 2.3, and 2.2 mol/kg, and CO2 regenerabilities of 56.3, 84.1, and 83.9 %, successively. For CO2/H-2 and CO2/N-2 separation in humid conditions, UiO-66-NH2 is potentially useful. Considering H2S/CO2 separation in dry conditions, NU-1101, PCN-58, and LMOF-1 are identified to be the best three MOFs attaining H2S/CO2 selectivities of 109.7, 30.9, and 90.7, H2S working capacities of 1.6, 2.3, and 1.2 mol/kg, and H2S regenerabilities of 43.0, 56.4, and 43.7 %, respectively. All top materials for H2S/CO2 separation show relatively large water affinities (PCN-57 having the smallest affinity) which might render them inefficient for H2S/CO2 separation in humid conditions. Adsorbate density profiles are generated for the top 3 materials to elucidate the adsorption mechanisms for each gas separation. A comparison of predictions based on PACMOF and EQeq charges demonstrates drastic differences in material rankings, and separation performance metrics.
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    PublicationOpen Access
    Multi-level computational screening of in silico designed MOFs for efficient SO(2) capture
    (American Chemical Society (ACS), 2022) Department of Chemical and Biological Engineering; Keskin, Seda; Demir, Hakan; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 40548; N/A
    SO2 presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal-organic frameworks (MOFs), cost-effective separation processes for SO2 emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO2/CH4, SO2/CO2, and SO2/N2 separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO2/CH4 selectivities of 62.4-16899.7, SO2 working capacities of 0.3-20.1 mol/kg, and SO2 regenerabilities of 5.8-98.5%. Regarding SO2/CO2 separation, they possess SO2/CO2 selectivities of 13.3- 367.2, SO2 working capacities of 0.1-17.7 mol/kg, and SO2 regenerabilities of 1.9-98.2%. For the SO2/N2 separation, their SO2/N2 selectivities, SO2 working capacities, and SO2 regenerabilities span the ranges of 137.9-67,338.9, 0.4-20.6 mol/kg, and 7.0-98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.
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    PublicationOpen Access
    Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H-2, CH4/N-2, and N-2/H-2 mixtures
    (Royal Society of Chemistry (RSC), 2023) Department of Chemical and Biological Engineering; Keskin, Seda; Demir, Hakan; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 40548; N/A
    The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF4/CH4, CH4/H-2, CH4/N-2, and N-2/H-2 separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation. 15, 13, and 16 out of the top 20 MOFs identified for the CH4/H-2, CH4/N-2, and N-2/H-2 adsorption-based separation, respectively, were found to have -OCH3 groups as one of the functional groups. The biggest improvements in CF4/CH4, CH4/H-2, CH4/N-2, and N-2/H-2 selectivities were found to be induced by the presence of -OCH3-OCH3 groups in MOFs. For CH4/H-2 separation, MOFs with two and three functionalized linkers were the best adsorbent candidates while for N-2/H-2 separation, all the top 20 materials involve two functional groups. Membrane performances of the MOFs were also studied for CH4/H-2 and CH4/N-2 separation and the results showed that MOFs having -F-NH2 and -F-OCH3 functional groups present the highest separation performances considering both the membrane selectivity and permeability.
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    PublicationOpen Access
    Zr-MOFs for CF4/CH4, CH4/H-2, and CH4/N-2 separation: towards the goal of discovering stable and effective adsorbents
    (Royal Society of Chemistry (RSC), 2021) Department of Chemical and Biological Engineering; Demir, Hakan; Keskin, Seda; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; N/A; 40548
    Zirconium metal-organic frameworks (MOFs) can be promising adsorbents for various applications as they are highly stable in different chemical environments. In this work, a collection of Zr-MOFs comprised of more than 100 materials is screened for CF4/CH4, CH4/H-2, and CH4/N-2 separations using atomistic-level simulations. The top three MOFs for the CF4/CH4 separation are identified as PCN-700-BPDC-TPDC, LIFM-90, and BUT-67 exhibiting CF4/CH4 adsorption selectivities of 4.8, 4.6, and 4.7, CF4 working capacities of 2.0, 2.0, and 2.1 mol kg(-1), and regenerabilities of 85.1, 84.2, and 75.7%, respectively. For the CH4/H-2 separation, MOF-812, BUT-67, and BUT-66 are determined to be the top performing MOFs demonstrating CH4/H-2 selectivities of 61.6, 36.7, and 46.2, CH4 working capacities of 3.0, 4.1, and 3.4 mol kg(-1), and CH4 regenerabilities of 70.7, 82.7, and 74.7%, respectively. Regarding the CH4/N-2 separation, BUT-67, Zr-AbBA, and PCN-702 achieving CH4/N-2 selectivities of 4.5, 3.4, and 3.8, CH4 working capacities of 3.6, 3.9, and 3.5 mol kg(-1), and CH4 regenerabilities of 81.1, 84.0, and 84.5%, in successive order, show the best overall separation performances. To further elucidate the adsorption in top performing adsorbents, the adsorption sites in these materials are analyzed using radial distribution functions and adsorbate density profiles. Finally, the water affinities of Zr-MOFs are explored to comment on their practical use in real gas separation applications. Our findings may inspire future studies probing the adsorption/separation mechanisms and performances of Zr-MOFs for different gases.