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Reply to Comment on ‘Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening’ Cigdem Altintas, Sadiye Velioglu, and Seda Keskin ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.9b02614 • Publication Date (Web): 28 Mar 2019 Downloaded from http://pubs.acs.org on April 5, 2019
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ACS Applied Materials & Interfaces
Reply to Comment on ‘Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening’ Cigdem Altintas, Sadiye Velioglu, Seda Keskin* Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey Corresponding author: *
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We would like to thank Dr. Poor and Dr. Marek for further analyzing 6 metal organic frameworks (MOFs) among the 54,808 MOFs that we examined in our original publication for CO2/N2 and CO2/CH4 separations.1 We would like to clarify several points raised in the comment about our original work before giving our detailed analysis on these 6 MOFs. In our original work, we computationally screened a published, non-disordered MOF database2 to identify the most promising material candidates for CO2 separations. This is known as ‘high-throughput computational screening’ based on predetermined criteria to identify the top materials and exactly the same approach has been widely used by several research groups to examine MOFs for various applications in the last decade.3-6 We removed the solvent molecules of ~55,000 MOFs to mimic ‘experimentally activated MOFs’, computed their pore sizes and refined the MOF database to eliminate materials having very narrow pores that limit the gas adsorption. We compared our simulation results with a large number of experimental data for CO2 uptakes, CO2/CH4 and CO2/N2 selectivities of many MOFs and showed the good agreement between simulations and experiments in our original work.1 We also showed the good agreement between the predicted selectivities of top materials identified from our computational screening and experimentally reported values for these top materials. This was a very important check-point showing the accuracy and validity of our computational screening procedure because the top MOFs were identified among ~55,000 materials in our original work1 and their experimental gas uptake and selectivity data were then searched from the literature. Our work1 was solely based on the screening of the solvent-free, published MOF database using molecular simulations. Activation of a MOF means the discharge of any solvent molecules and other chemicals from the pores without damaging the structural integrity to obtain a porous structure.7 In molecular simulations, experimental activation of a MOF can be only mimicked by the removal of solvent molecules from the pores of the framework. In high-throughput computational studies, where several thousands of MOFs are examined, it is not practical to manually remove solvent molecules one by one from every single MOF. The current state-ofthe-art in performing high-throughput molecular simulations of several thousands of MOFs requires automated solvent removal, a procedure that has been widely used by many research groups. We screened the MOF database published by Jimenez’s group2 and used the Python script published by the same group to remove solvents from the MOFs in our original work.1 A recent work by Snurr’s group8 also followed the same approach and used the same Python script to remove solvent molecules from the same MOF database in order to simulate the fully activated MOF structures. The Python script provided with the MOF database was developed to identify 74 different kinds of solvents and to remove them from MOFs.2 However, it was not developed to check if the solvent removal would affect the structural integrity of MOFs. The comment of Poor and Marek (PM) implies that we used the Python script without care and obtained problematic structures. We disagree. The MOFs discussed in their comment were having problems (a) because of the structure deposited into the MOF database (SUFSUH01), (b) because of the possibility of structural instability after the solvent removal (AQEKUD), (c) because of a problem in the algorithm of the Python script; accidental
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ACS Applied Materials & Interfaces
removal of a part of the framework during the solvent removal process (MIDXON, VIHHIE, and QUQQUP). Our brief comments about these problems are as follows: (a) Some MOFs have been deposited into the Cambridge Structural Database (CSD)9 with missing features as we discuss in detail below, however this is not a problem related with our computational approach. We used the published MOF database.2 (b) Stability of a MOF can be lost after the solvent removal as we discuss in detail below, however this is not a problem related with our computational approach. We screened the solvent-free MOFs in our original work1 and a MOF was not eliminated from the promising materials list if we did not find a direct statement about its instability in its synthesis paper. (c) This problem is related with the algorithm of the Python script provided with the MOF database2 and we were not aware that the script might accidentally remove a necessary part of the framework. We accept that automatic solvent removal from thousands of MOFs using the Python script is the limitation of our computational study. Although automatic solvent removal is a must to mimic experimentally activated MOFs and the same script has been used by other computational research groups as discussed above, this process might generate unphysical structures due to the accidental removal of a part of the framework by the Python script. The comment of PM unfortunately does not explicitly mention about these problems and misleads the readers about our work. We performed detailed analysis and molecular simulations for the 6 problematic MOFs in this reply to clearly explain the origins of the problems and presented how to solve these problems as can be seen below: MIDXON and VIHHIE: During the solvent removal step, the Python script accidentally removed the pyridine ligand of MIDXON and tetrahydrofuran (THF) molecules coordinated to Li+ of VIHHIE since both pyridine and THF molecules were in the predefined solvent lists of the Python script. We agree that without the pyridine and THF, these structures are not chemically meaningful. After we removed the unbound water molecules and kept the pyridine and bound water molecules in MIDXON, its PLD (pore limiting diameter) and LCD (the largest cavity diameters) were calculated as 1.17 and 2.85 Å, respectively. After we manually edited VIHHIE to keep the THF molecules in the framework, its PLD and LCD were calculated as 1.21 and 3.06 Å, respectively. Our molecular simulations on these edited MOFs indicated that gases are not able to be adsorbed into the pores and we concluded that these edited MOFs cannot be considered as promising materials. We note that the same problem may also occur in other MOFs which have pyridine or THF molecules therefore, this type of MOFs must be carefully handled. It would be very useful to update the published Python script2 of the database so that only unbound pyridine and THF existing within the framework would be considered as the solvent molecules to be removed. SUFSUH01: This MOF was deposited into the CSD9 with missing hydrogen (H) atoms and ions. We clearly stated in our original work1 that we used the published MOF database2 without modification to screen experimentally reported MOFs. We never claimed to complete the missing atoms of experimentally reported MOFs. Of course, missing H atoms can be automatically added using a software such as Materials Studio10 or Mercury11 before performing molecular simulations. However, the correctness of the final structure in terms of 3 ACS Paragon Plus Environment
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the positions of H atoms would never be certain. One may suggest geometrically optimizing the MOF after adding H atoms, but then it would be a different structure than the experimentally reported one in the CSD. Experimentally reported, CSD deposited SUFSUH01 does not have ClO4- ions and PM even stated that they were not able to perform molecular simulations since the structure has missing ions. This example clearly shows that the problem of this MOF is resulting from the experimentally deposited structure, not from the solvent removal process, and PM were not able to offer a solution. This structure was not edited in this reply since the original synthesis paper of this MOF12 was not accessible. We labeled these MOFs, MIDXON, VIHHIE, and SUFSUH01 in Figure 1 to examine the effects of their structural problems on the structure-performance relations of MOFs published in our original work.1 Once their structures were corrected as explained above, these MOFs have LCDs 85% criteria in our original work.1 That means regardless of the selectivity, the optimized MOF still would not be a promising candidate for CO2/N2 and CO2/CH4 separations due to its low R% as shown in Table 1. Table 1. Predicted separation performance of ICORAV.
SCO2/N2 APSCO2/N2 (mol/kg) RCO2/N2 (%) Ranking (APSCO2/N2) SCO2/CH4 APSCO2/CH4 (mol/kg) RCO2/CH4 (%) Ranking (APSCO2/CH4)
Our original publication1 540.36 681.54 43.92
Structure with DDEC charges22 772.75 1085.28 44.84
27 81.38 87.64 31.25 69
15 121.98 130.42 29.59 43
Optimized structure 1
Optimized structure 2
Optimized structure 3
399.45 289.19 22.18
385.43 292.77 23.59
406.39 318.89 23.72
Optimized structure average 397.09 300.28 23.17
104 47.54 40.18 22.34 239
104 44.76 37.55 22.18 263
92 43.12 31.37 19.66 321
99 45.14 36.37 21.39 275
Since the R% values of the MOFs were computed to be 85% criteria and ranking materials based on their adsorbent performance scores (APS).1 Although QUQQUP was computed to have R%>90% for CO2/N2 and CO2/CH4 separations, it was not in the top performing MOF list in our original work1 because of its low APS values for both separations. During the solvent removal step, the Python script removed the pyridine ligand of QUQQUP similar to the case of MIDXON as explained above. CSD-deposited QUQQUP has pyridines and it is free of coordinated and lattice solvent molecules. Therefore, we performed new molecular simulations using the original structure in the CSD. Results of this proposed MOF were compared with our originally published results1, 25 and results of the proposed structure by PM given in Table 2 of their comment. Since R% values of the proposed structure are lower than 85% for both CO2 separations, this MOF is still a non-promising one, indicating that our original judgment about the material’s gas separation performance would be the same. PM also stated that their proposed QUQQUP exhibited higher selectivity and APS than the structure reported by us for CH4/H2 separation.25 However, R% of this proposed structure was computed as ~52% both by PM and us as shown in Table 2. We clearly stated that the top MOFs were identified using R%>85% criteria in our original works,1, 25 therefore even if the selectivity and APS of the proposed MOF were higher, it would not be considered as a promising material because of its low R% for CH4/H2 separation. We also note that PM should have included the units of computed metrics such as APS in Table 1 of their comment. Table 2. Predicted separation performance of QUQQUP.
SCO2/N2 APSCO2/N2 (mol/kg) RCO2/N2 (%) Ranking (APSCO2/N2) SCO2/CH4 APSCO2/CH4 (mol/kg) RCO2/CH4 (%) Ranking (APSCO2/CH4) SCH4/H2 APSCH4/H2 (mol/kg) RCH4/H2 (%) Ranking (APSCH4/H2)
Our previous structure1, 25 13.43
Proposed structure 34.12
Proposed structure by PM 37.82
12.44 90.14 2062
46.97 80.66 825
55.87 77.89 686ǂ
4.72 14.78 90.13
4.65 7.34 64.09
4.59 6.83 58.77
812 22.72 120.81 87.17 872
1663 120.38 208.95 52.23 151
1798ǂ 123.27 212.44 51.78 137ǂ
Since R% values of the proposed MOFs were computed to be
