Researcher: Harman, Hilal Dağlar
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Harman, Hilal Dağlar
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Publication Metadata only Metal-organic framework-based materials for the abatement of air pollution and decontamination of wastewater(PERGAMON-ELSEVIER SCIENCE LTD, 2022) Erucar, Ilknur; Heidari, Golnaz; Zare, Ehsan Nazarzadeh; Moradi, Omid; Srivastava, Varsha; Iftekhar, Sidra; Sillanpaa, Mika; N/A; N/A; Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Altıntaş, Çiğdem; Keskin, Seda; PhD Student; Researcher; Faculty Member; Department of Chemical and Biological Engineering; N/A; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 40548Developing new and efficient technologies for environmental remediation is becoming significant due to the increase in global concerns such as climate change, severe epidemics, and energy crises. Air pollution, primarily due to increased levels of H2S, SOx, NH3, NOx, CO, volatile organic compounds (VOC), and particulate matter (PM) in the atmosphere, has a significant impact on public health, and exhaust gases harm the natural sulfur, nitrogen, and carbon cycles. Similarly, wastewater discharged to the environment with metal ions, herbicides, pharmaceuticals, personal care products, dyes, and aromatics/organic compounds is a risk for health since it may lead to an outbreak of waterborne pathogens and increase the exposure to endocrine-disrupting agents. Therefore, developing new and efficient air and water quality management systems is critical. Metal-organic frameworks (MOFs) are novel materials for which the main application areas include gas storage and separation, water harvesting from the atmosphere, chemical sensing, power storage, drug delivery, and food preservation. Due to their versatile structural motifs that can be modified during synthesis, MOFs also have a great promise for green applications including air and water pollution remediation. The motivation to use MOFs for environmental applications prompted the modification of their structures via the addition of metal and functional groups, as well as the creation of heterostructures by mixing MOFs with other nanomaterials, to effectively remove haz-ardous contaminants from wastewater and the atmosphere. In this review, we focus on the state-of-the-art environmental applications of MOFs, particularly for water treatment and air pollution, by highlighting the groundbreaking studies in which MOFs have been used as adsorbents, membranes, and photocatalysts for the abatement of air and water pollution. We finally address the opportunities and challenges for the environmental applications of MOFs.Publication Metadata only Prediction of O-2/N-2 selectivity in metal-organic frameworks via high-throughput computational screening and machine learning(American Chemical Society (ACS), 2022) Orhan, İbrahim B.; Le, Tu C.; Babarao, Ravichandar; N/A; Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Keskin, Seda; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 40548Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O-2 and N-2 uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O-2/N-2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r(2) correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O-2/N-2 separation based on the adsorption and diffusion selectivity.Publication Metadata 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; 40548Considering 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.Publication Metadata 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; 40548The 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 AuthorsPublication Metadata only An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH4 and H2 adsorption(Royal Soc Chemistry, 2019) Erucar, Ilknur; N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Altıntaş, Çiğdem; Avcı, Gökay; Harman, Hilal Dağlar; Azar, Ayda Nemati Vesali; Velioğlu, Sadiye; Keskin, Seda; Researcher; PhD Student; PhD Student; PhD Student; Researcher; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; 200650; 40548Computation-ready metal-organic framework (MOF) databases (DBs) have tremendous value since they provide directly useable crystal structures for molecular simulations. The currently available two DBs, the CoRE DB (computation-ready, experimental MOF database) and CSDSS DB (Cambridge Structural Database non-disordered MOF subset) have been widely used in high-throughput molecular simulations. These DBs were constructed using different methods for collecting MOFs, removing bound and unbound solvents, treating charge balancing ions, missing hydrogens and disordered atoms of MOFs. As a result of these methodological differences, some MOFs were reported under the same name but with different structural features in the two DBs. In this work, we first identified 3490 common MOFs of CoRE and CSDSS DBs and then performed molecular simulations to compute their CH4 and H-2 uptakes. We found that 387 MOFs result in different gas uptakes depending on from which DB their structures were taken and we identified them as problematic' MOFs. CH4/H-2 mixture adsorption simulations showed that adsorbent performances of problematic MOFs, such as selectivity and regenerability, also significantly change depending on the DB used and lead to large variations in the ranking of materials and identification of the top MOFs. Possible reasons of different structure modifications made by the two DBs were investigated in detail for problematic MOFs. We described five main cases to categorize the problematic MOFs and discussed what types of different modifications were performed by the two DBs in terms of removal of unbound and bound solvents, treatment of missing hydrogen atoms, charge balancing ions etc. with several examples in each case. With this categorization, we aimed to direct researchers to computation-ready MOFs that are the most consistent with their experimentally reported structures. We also provided the new computation-ready structures for 54 MOFs for which the correct structures were missing in both DBs. This extensive comparative analysis of the two DBs will clearly show how and why the DBs differently modified the same MOFs and guide the users to choose either of the computation-ready MOFs from the two DBs depending on their purpose of molecular simulations.Publication Open Access Database for CO2 separation performances of MOFs based on computational materials screening(American Chemical Society (ACS), 2018) Eruçar, İlknur; Department of Chemical and Biological Engineering; Altıntaş, Çiğdem; Avcı, Gökay; Harman, Hilal Dağlar; Azar, Ayda Nemati Vesali; Velioğlu, Sadiye; Keskin, Seda; Researcher; Post Doctorate Student; Department of Chemical and Biological Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 40548Metal-organic frameworks (MOFs) are potential adsorbents for CO2 capture. Because thousands of MOFs exist, computational studies become very useful in identifying the top performing materials for target applications in a time-effective manner. In this study, molecular simulations were performed to screen the MOF database to identify the best materials for CO2 separation from flue gas (CO2/N-2) and landfill gas (CO2/CH4) under realistic operating conditions. We validated the accuracy of our computational approach by comparing the simulation results for the CO2 uptakes, CO2/N-2 and CO2/CH4 selectivities of various types of MOFs with the available experimental data. Binary CO2/N-2 and CO2/CH4 mixture adsorption data were then calculated for the entire MOF database. These data were then used to predict selectivity, working capacity, regenerability, and separation potential of MOFs. The top performing MOF adsorbents that can separate CO2/N-2 and CO2/CH4 with high performance were identified. Molecular simulations for the adsorption of a ternary CO2/N-2/CH4 mixture were performed for these top materials to provide a more realistic performance assessment of MOF adsorbents. The structure-performance analysis showed that MOFs with Delta Q(st)(0) > 30 kJ/mol, 3.8 angstrom < pore-limiting diameter < 5 angstrom, 5 angstrom < largest cavity diameter < 7.5 angstrom, 0.5 < phi < 0.75, surface area < 1000 m(2)/g, and rho > 1 g/cm(3) are the best candidates for selective separation of CO2 from flue gas and landfill gas. This information will be very useful to design novel MOFs exhibiting high CO2 separation potentials. Finally, an online, freely accessible database https://cosmoserc.ku.edu.tr was established, for the first time in the literature, which reports all of the computed adsorbent metrics of 3816 MOFs for CO2/N-2, CO2/CH4, and CO2/N-2/CH4 separations in addition to various structural properties of MOFs.Publication Open 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; 40548Development 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.Publication Open Access Exploring the performance limits of MOF/polymer MMMs for O-2/N-2 separation using computational screening(Elsevier, 2021) Eruçar, İlknur; Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Keskin, Seda; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of EngineeringAir separation is one of the most challenging separations because of the very similar molecular dimensions of gas molecules. We used a high-throughput computational screening approach to identify the upper performance limits of metal organic framework (MOF) membranes and MOF/polymer mixed matrix membranes (MMMs) for O-2/N-2 separation. Gas permeabilities and selectivities were calculated for 5629 MOF membranes and 78,806 different types of MOF/polymer MMMs, which represent the largest number of MOF-based membranes studied to date for air separation. Our results showed that many MOF membranes exceed the upper bound established for polymer membranes due to their high permeabilities and/or selectivities. The maximum achievable O-2 permeability and O-2/N-2 selectivity of MOF/polymer MMMs were computed as 2710.8 Barrer and 19.8, respectively. Results revealed that MOF/polymer MMMs can outperform MMMs composed of traditional fillers, such as zeolites, in terms of O-2 permeability and O-2/N-2 selectivity. The impacts of purity of air mixture and the structural flexibility of MOFs on the gas separation performances of MMMs were also discussed. These results provide molecular-level insights into adsorption and diffusion behaviors of O-2 and N-2 in MOF membranes in addition to presenting structure-performance relations of MOFs that can lead to high-performance membranes and fillers for MMMs.Publication Open Access Computational selection of high-performing covalent organic frameworks for adsorption and membrane-based CO2 /H2 separation(American Chemical Society (ACS), 2020) Department of Chemical and Biological Engineering; Keskin, Seda; Altıntaş, Çiğdem; Harman, Hilal Dağlar; Aksu, Gökhan Önder; Researcher; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 40548; N/A; N/A; N/ACovalent organic frameworks (COFs) have high potential in gas separation technologies because of their porous structures, large surface areas, and good stabilities. The number of synthesized COFs already reached several hundreds, but only a handful of materials were tested as adsorbents and/or membranes. We used a high-throughput computational screening approach to uncover adsorption-based and membrane-based CO2/H2 separation potentials of 288 COFs, representing the highest number of experimentally synthesized COFs studied to date for precombustion CO2 capture. Grand canonical Monte Carlo (GCMC) simulations were performed to assess CO2/H2 mixture separation performances of COFs for five different cyclic adsorption processes: pressure swing adsorption, vacuum swing adsorption, temperature swing adsorption (TSA), pressure−temperature swing adsorption (PTSA), and vacuum−temperature swing adsorption (VTSA). The results showed that many COFs outperform traditional zeolites in terms of CO2 selectivities and working capacities and PTSA is the best process leading to the highest adsorbent performance scores. Combining GCMC and molecular dynamics (MD) simulations, CO2 and H2 permeabilities and selectivities of COF membranes were calculated. The majority of COF membranes surpass Robeson’s upper bound because of their higher H2 permeabilities compared to polymers, indicating that the usage of COFs has enormous potential to replace current materials in membrane-based H2/CO2 separation processes. Performance analysis based on the structural properties showed that COFs with narrow pores [the largest cavity diameter (LCD) < 15 Å] and low porosities (ϕ < 0.75) are the top adsorbents for selective separation of CO2 from H2, whereas materials with large pores (LCD > 20 Å) and high porosities (ϕ > 0.85) are generally the best COF membranes for selective separation of H2 from CO2. These results will help to speed up the engineering of new COFs with desired structural properties to achieve high-performance CO2/H2 separations.Publication Open Access High-throughput screening of MOFs as fillers in mixed matrix membranes for flue gas separation(Wiley, 2019) Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Keskin, Seda; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; N/A; 40548High-throughput computational screening of metal organic frameworks (MOFs) is performed to evaluate their performances as fillers in mixed matrix membranes (MMMs). Grand canonical Monte Carlo and molecular dynamics simulations are performed to calculate CO2 and N-2 permeabilities of 7822 synthesized MOFs. This data are then combined with the experimentally reported gas permeability data of 14 different polymers using a theoretical permeation model. As a result, CO2 permeabilities and CO2/N-2 selectivities of 109 508 different types of MOF-based MMMs are estimated. The maximum CO2/N-2 selectivity and CO2 permeability of MOF/polymer MMMs are computed as 64.3 and 36 103 Barrer, respectively. The top 50 MOFs that significantly improve CO2/N-2 separation performances of highly permeable polymers are identified and their potentials for separation of binary CO2/N-2 mixture are examined at practical operating conditions. Results show that several MOFs offer significant improvements both in the gas permeability and selectivity of polymers when used as fillers in MMMs for flue gas separation. The MOF structure-membrane performance relations are also investigated for MOF/polymer MMMs, and results show that MOFs with narrow pore sizes (3.75-5.12 angstrom), low surface areas (<1000 m(2) g(-1)), and moderate porosities (0.41-0.58) lead to highly selective MMMs.