Researcher: Altundal, Ömer Faruk
Loading...
Name Variants
Altundal, Ömer Faruk
Email Address
Birth Date
4 results
Search Results
Now showing 1 - 4 of 4
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 Combined GCMC, MD, and DFT approach for unlocking the performances of COFs for methane purification(American Chemical Society (ACS), 2021) Department of Chemical and Biological Engineering; Keskin, Seda; Haşlak, Zeynep Pınar; Altundal, Ömer Faruk; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 40548; N/A; N/ACovalent organic frameworks (COFs) are promising materials for gas storage and separation; however, the potential of COFs for separation of CH4 from industrially relevant gases such as H-2, N-2, and C2H6 is yet to be investigated. In this work, we followed a multiscale computational approach to unlock both the adsorption- and membrane-based CH4/H-2, CH4/N-2, and C2H6/CH4 separation potentials of 572 COFs by combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations and density functional theory (DFT) calculations. Adsorbent performance evaluation metrics of COFs, adsorption selectivity, working capacity, regenerability, and adsorbent performance score were calculated for separation of equimolar CH4/H-2, CH4/N-2, and C2H6/CH4 mixtures at vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions to identify the best-performing COFs for each mixture. Results showed that COFs could achieve selectivities of 2-85, 1-7, and 2-23 for PSA-based CH4/H-2, CH4/N-2, and C2H6/CH4 separations, respectively, outperforming conventional adsorbents such as zeolites and activated carbons for each mixture. Structure-performance relations revealed that COFs with pore sizes <10 angstrom are promising adsorbents for all mixtures. We identified the gas adsorption sites in the three top-performing COFs commonly identified for each mixture by DFT calculations and computed the binding strength of gases, which were found to be on the order of C2H6 > CH4 > N-2 > H-2, supporting the GCMC results. Nucleus-independent chemical shift (NICS) indexes of aromaticity for adsorption sites were calculated, and the results revealed that the degree of linker aromaticity could be a measure for the selection or design of highly alkane-selective COF adsorbents over N-2 and H-2. Finally, COF membranes were shown to achieve high H-2 permeabilities, 4.57 x 10(3)-1.25 x 10(6) Barrer, and decent membrane selectivities, as high as 4.3, outperforming polymeric and MOF-based membranes for separation of H-2 from CH4.Publication Open Access Can COFs replace MOFs in flue gas separation? high-throughput computational screening of COFs for CO2/N2 separation(Royal Society of Chemistry (RSC), 2020) Department of Chemical and Biological Engineering; Keskin, Seda; Altundal, Ömer Faruk; Altıntaş, Çiğdem; Faculty Member; Researcher; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 40548; N/A; N/ACovalent organic frameworks (COFs) are under study as adsorbent and membrane candidates for gas separation applications. However, experimental testing of all synthesized COF materials as adsorbents and membranes under different operating conditions is not practical. Herein, we used a high-throughput computational screening approach to investigate adsorption- and membrane-based flue gas separation performances of 295 COFs. Adsorption selectivity, working capacity, percent regenerability and adsorbent performance score of COFs were calculated for separation of CO2/N(2)mixture for three different cyclic adsorption processes, pressure swing adsorption (PSA), vacuum swing adsorption (VSA) and temperature swing adsorption (TSA). The top performing COFs were identified for each process based on the combination of several metrics. Selectivities of the top COFs were predicted to be greater than those of zeolites and activated carbons. Molecular simulations were performed considering the wet flue gas for the top COF adsorbents and results revealed that most COFs retained their high CO(2)selectivities in the presence of water. Using COFs with detailed geometry optimization and high-accuracy partial charges in molecular simulations led to lower selectivities and adsorbent performance scores compared to using experimentally reported COFs with approximate charges. Membrane-based flue gas separation performances of COFs were also studied and most COFs were found to have comparable CO(2)permeabilities with metal organic frameworks (MOFs), up to 3.96 x 10(6)barrer, however their membrane selectivities were lower than MOFs, 0.38-21, due to their large pores and the lack of metal sites in their frameworks. Structure-performance relations revealed that among the COFs we studied, the ones with pore sizes <10 angstrom, accessible surface areas <4500 m(2)g(-1)and 0.6 < porosity <0.8 are not only highly selective adsorbents but also CO(2)selective membranes.Publication Open Access Machine learning meets with metal organic frameworks for gas storage and separation(American Chemical Society (ACS), 2021) Yıldırım, Ramazan; Department of Chemical and Biological Engineering; Keskin, Seda; Altıntaş, Çiğdem; Altundal, Ömer Faruk; Researcher; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 40548; N/A; N/AThe acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.