Researcher:
Gülbalkan, Hasan Can

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PhD Student

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Hasan Can

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Gülbalkan

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Gülbalkan, Hasan Can

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2021 - 20237

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    PublicationMetadata only
    Integrating molecular simulations with machine learning guides in the design and synthesis of [bmim][bf(4)]/mof composites for co(2)/n(2) separation
    (American Chemical Society, 2023) N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Gülbalkan, Hasan Can; Habib, Nitasha; Durak, Özce; Uzun, Alper; Keskin, Seda; PhD Student; PhD Student; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences; College of Engineering and Engineering; College of Engineering; N/A; N/A; N/A; N/A; 59917; 40548
    Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-n-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]) with a large variety of MOFs for CO2 and N2 adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF4]/MOF composites. The most important features that affect the CO2/N2 selectivity of composites were extracted from ML and utilized to computationally generate an IL/MOF composite, [BMIM][BF4]/UiO-66, which was not present in the original material data set. This composite was finally synthesized, characterized, and tested for CO2/N2 separation. Experimentally measured CO2/N2 selectivity of the [BMIM][BF4]/UiO-66 composite matched well with the selectivity predicted by the ML model, and it was found to be comparable, if not higher than that of all previously synthesized [BMIM][BF4]/MOF composites reported in the literature. Our proposed approach of combining molecular simulations with ML models will be highly useful to accurately predict the CO2/N2 separation performances of any [BMIM][BF4]/MOF composite within seconds compared to the extensive time and effort requirements of purely experimental methods.
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    PublicationMetadata only
    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
    Effect of MOF database selection on the assessment of gas storage and separation potentials of MOFs
    (Wiley, 2021) Eruçar, İlknur; Department of Chemical and Biological Engineering; N/A; Harman, Hilal Dağlar; Gülbalkan, Hasan Can; Aksu, Gökhan Önder; Altundal, Ömer Faruk; Altıntaş, Çiğdem; Avcı, Gökay; Keskin, Seda; Researcher; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; N/A; 40548
    Development of computation‐ready metal–organic framework databases (MOF DBs) has accelerated high‐throughput computational screening (HTCS) of materials to identify the best candidates for gas storage and separation. These DBs were constructed using structural curations to make MOFs directly usable for molecular simulations, which caused the same MOF to be reported with different structural features in different DBs. We examined thousands of common materials of the two recently updated, very widely used MOF DBs to reveal how structural discrepancies affect simulated CH4, H2, CO2 uptakes and CH4/H2 separation performances of MOFs. Results showed that DB selection has a significant effect on the calculated gas uptakes and ideal selectivities of materials at low pressure. A detailed analysis on the curated structures was provided to isolate the critical elements of MOFs determining the gas uptakes. Identification of the top‐performing materials for gas separation was shown to strongly depend on the DB used in simulations.
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    PublicationOpen Access
    Composites of porous materials with ionic liquids: synthesis, characterization, applications, and beyond
    (Elsevier, 2022) Department of Chemical and Biological Engineering; Durak, Özce; Zeeshan, Muhammad; Habib, Nitasha; Gülbalkan, Hasan Can; Alsuhile, Ala Abdulalem Abdo Moqbel; Çağlayan, Hatice Pelin; Öztulum, Samira Fatma Kurtoğlu; Zhao, Yuxin; Haşlak, Zeynep Pınar; Uzun, Alper; Keskin, Seda; PhD Student; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; 59917; 40548
    Modification of the physicochemical properties of porous materials by using ionic liquids (ILs) has been widely studied for various applications. The combined advantages of ILs and porous materials provide great potential in gas adsorption and separation, catalysis, liquid-phase adsorption and separation, and ionic conductivity owing to the superior performances of the hybrid composites. In this review, we aimed to provide a perspective on the evolution of IL/porous material composites as a research field by discussing several different types of porous materials, including metal organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, and carbonaceous-materials. The main challenges and opportunities in synthesis methods, characterization techniques, applications, and future opportunities of IL/porous materials are discussed in detail to create a road map for the area. Future advances of the field addressed in this review will provide in-depth insights into the design and development of these novel hybrid materials and their replacement with conventional materials.
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    PublicationOpen Access
    An integrated computational-experimental hierarchical approach for the rational design of an IL/UiO-66 composite offering infinite CO2 selectivity
    (Wiley, 2022) Department of Chemical and Biological Engineering; Department of Chemistry; Zeeshan, Muhammad; Gülbalkan, Hasan Can; Durak, Özce; Haşlak, Zeynep Pınar; Ünal, Uğur; Keskin, Seda; Uzun, Alper; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Chemistry; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of Engineering; College of Sciences; Graduate School of Sciences and Engineering; N/A; N/A; N/A; N/A; 42079; 40548; 59917
    Owing to the possibility of generating theoretically unlimited numbers of ionic liquid (IL)-metal-organic framework (MOF) combinations, experimental studies on IL/MOF composites for gas separation applications are mostly conducted on a trial-and-error basis. To address this problem, an integrated computational-experimental hierarchical approach is presented for selecting the best IL-MOF combination for a target gas separation application. For this purpose, UiO-66 and pyrrolidinium-based ILs are chosen as the parent MOF and IL family, respectively, and three powerful computational tools, Conductor-like Screening Model for Realistic Solvents calculations, density functional theory calculations, and grand canonical Monte Carlo simulations, are integrated to identify the most promising IL-UiO-66 combination as 1-n-butyl-1-methylpyrrolidinium dicyanamide/UiO-66, [BMPyrr][DCA]/UiO-66. Then, this composite is synthesized, characterized in deep detail, and tested for CO2/N-2, CO2/CH4, and CH4/N-2 separations. Results demonstrate that [BMPyrr][DCA]/UiO-66 offers an extraordinary gas separation performance, with practically infinite CO2 and CH4 selectivities over N-2 at 15 degrees C and at low pressures. The integrated hierarchical approach proposed in this work paves the way for the rational design and development of novel IL/MOF composites offering exceptional performance for any desired gas separation application.
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    PublicationOpen Access
    Doubling CO2/N2 separation performance of CuBTC by incorporation of 1-n-ethyl-3-methylimidazolium diethyl phosphate
    (Elsevier, 2021) Department of Chemical and Biological Engineering; Zeeshan, Muhammad; Gülbalkan, Hasan Can; Haşlak, Zeynep Pınar; Keskin, Seda; Uzun, Alper; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 40548; 59917
    1-ethyl-3-methylimidazolium diethyl phosphate ([EMIM][DEP]) was incorporated into copper benzene-1,3,5-tricarboxylate, CuBTC. Consequences of molecular interactions on the CO2 separation performance of CuBTC were investigated. Scanning electron microscopy and X-ray diffraction results showed that the surface morphology and crystal structure of CuBTC remained intact upon the incorporation of the ionic liquid (IL); and the results of thermogravimetric analysis and infrared spectroscopy indicated the presence of interactions between the anion of the IL and the open metal sites of CuBTC. Gas adsorption measurements for the pristine CuBTC and IL-incorporated CuBTC were performed at 25 °C in a pressure range of 0.1–1 bar. Data showed that ideal CO2/CH4 and CO2/N2 selectivities of IL-incorporated CuBTC were 1.6- and 2.4-times higher compared to those of the pristine CuBTC at 0.01 bar, respectively. Moreover, for the CO2/CH4:50/50 and CO2/N2:15/85 mixtures, the corresponding selectivities were improved by more than 1.5- and 1.9-times compared to that of pristine CuBTC at 0.01 bar, respectively.
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    PublicationOpen Access
    Multi-scale computational screening to accelerate discovery of IL/COF composites for CO2/N-2 separation
    (Elsevier, 2022) Department of Chemical and Biological Engineering; Uzun, Alper; Keskin, Seda; Gülbalkan, Hasan Can; Haşlak, Zeynep Pınar; Altıntaş, Çiğdem; Faculty Member; Researcher; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of Engineering; Graduate School of Sciences and Engineering; 59917; 40548; N/A; N/A; N/A
    Covalent organic frameworks (COFs) have emerged as novel adsorbents and membranes for gas separation. Incorporation of ionic liquids (ILs) into COFs is important to exceed the current performance limits of COFs. However, synthesis and testing of a nearly unlimited number of IL/COF combinations are simply impractical. Herein, we used a multi-scale computational screening approach combining COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, Grand Canonical Monte Carlo (GCMC), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations to unlock both the adsorption-and membrane based CO2/N-2 separation performances of IL/COF composites. Several adsorbent and membrane performance assessment metrics including selectivity, working capacity, regenerability, adsorbent performance score, and permeability were computed. Our results revealed that IL incorporation into COFs significantly improves CO2/N-2 adsorption selectivities (from 12 to 26) and adsorbent performance scores (from 3.7 to 12 mol/kg). By performing DFT calculations, the nature of the interactions between CO2, N-2, COFs, and their IL-incorporated composites was evaluated. The high CO2 selectivity of IL/COF composites was attributed to the cooperative intermolecular effects induced by the COF and the IL. Finally, IL/COF membranes were studied, and results showed that they achieve significantly higher CO2 permeabilities (2.4 x 10(4)-9.4 x 10(5) Barrer) than polymeric and zeolite membranes with comparable selectivities (up to 15.7). This shows a great promise of IL/COF composites to replace the conventional membrane materials for flue gas separation. Our results will be useful in accelerating the experimental efforts to design new IL/COF composites that can achieve high-performance CO2 separation.