1 High-throughput Computational Screening of the MOF Database for

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High-throughput Computational Screening of the MOF Database for CH4/H2 Separations Cigdem Altintas, Ilknur Erucar, and Seda Keskin ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b18037 • Publication Date (Web): 09 Jan 2018 Downloaded from http://pubs.acs.org on January 12, 2018

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ACS Applied Materials & Interfaces

High-throughput Computational Screening of the MOF Database for CH4/H2 Separations Cigdem Altintas,a Ilknur Erucarb and Seda Keskina* a

Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey b

Department of Natural and Mathematical Sciences, Faculty of Engineering, Ozyegin University, Cekmekoy, 34794, Istanbul, Turkey *Corresponding author. Email: [email protected], Phone: +90 (212) 338-1362 Submitted to ACS Applied Materials & Interfaces Abstract

Metal organic frameworks (MOFs) have been considered as one of the most exciting porous materials discovered in the last decade. Large surface areas, high pore volumes, tailorable pore sizes make MOFs highly promising in a variety of applications, mainly in gas separations. The number of MOFs has been increasing very rapidly and experimental identification of materials exhibiting high gas separation potential is simply impractical. High-throughput computational screening studies in which thousands of MOFs are evaluated to identify the best candidates for a target gas separation is crucial in directing experimental efforts to the most useful materials. In this work, we used molecular simulations to screen the most complete and recent collection of MOFs from the Cambridge Structural Database (CSD) to unlock their CH4/H2 separation performances. This is the first study in the literature which examines potential of all existing MOFs for adsorption-based CH4/H2 separation. 4,350 MOFs were ranked based on several adsorbent evaluation metrics including selectivity, working capacity, adsorbent performance score, sorbent selection parameter, and regenerability. A large number of MOFs was identified to have extraordinarily large CH4/H2 selectivities compared to traditional adsorbents such as zeolites and activated carbons. We examined the relations between structural properties of MOFs such as pore sizes, porosities, surface areas and their selectivities. Correlations between the heat of adsorption, adsorbility, metal type of MOFs and selectivities were also studied. Based on these relations, a simple mathematical model that can predict CH4/H2 selectivity of MOFs was suggested which will be very useful in guiding the design and development of new MOFs with extraordinarily high CH4/H2 separation performances. Keywords: metal organic framework; adsorption; separation; selectivity; regenerability 1 ACS Paragon Plus Environment

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1. Introduction: We have witnessed the very quick growth of metal organic frameworks (MOFs) in the last decade. MOFs are crystalline nanoporous materials composed of metal complexes connected with organic linkers.1 They are crystalline structures with exceptional physical properties such as very large surface areas (up to 6000 m2/g), high pore volumes (1-4 cm3/g), a large variety of pore sizes (1-98 Å), and reasonable chemical stabilities. The most exciting feature of MOFs compared to traditional porous materials is their chemical and topological tunability. The controllable synthesis of MOFs led to a large diversity of materials having different geometry and chemical functionality.2 The number of synthesized MOFs has been rapidly increasing and already reached to several thousands. Theoretically unlimited numbers of MOFs can be synthesized by changing the organic linkers and metals.3 MOFs have been studied for a wide range of applications including gas storage and gas separation,4 catalysis,5 sensing,6 drug storage and delivery.7 Among these, gas storage and separation can be considered as the most mature ones because high porosities, large surface areas, varieties in pore sizes and shapes make MOFs highly promising adsorbent candidates. Existence of large numbers of MOFs generates both an opportunity and a challenge for the material search. It is an excellent opportunity to have thousands of candidates that can achieve various gas separations. On the other hand, it is challenging to identify and select the best MOF for a target gas separation application. Experimental synthesis and testing of a material for a gas separation generally takes several weeks. Testing thousands of MOFs using purely experimental techniques is simply impractical. Computational methods play a critical role in examining a large number of MOFs in a time-effective manner to identify the most promising materials for desired applications.8 Computer simulations have been successful in providing molecular-level information about gas adsorption in MOFs.9 High-throughput molecular simulation studies generally focused on hypothetical MOFs. Storage of CH4,10-11 H2,12-14 CO215-16 in hypothetical MOFs was examined. A recent study17 used molecular simulations to screen hypothetical MOFs for CO2/CH4 and N2/CH4 separations. Although hypothetical MOFs are very useful in producing structure-property relations as we will discuss later, the main drawback is that there is no guarantee for experimental synthesizability of these hypothetical materials. An experimental synthesis protocol should be designed to make these materials and this is often very complicated.

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Computer simulations require the experimental crystal structures of real MOFs. When a MOF is experimentally synthesized, it is deposited into Cambridge Structural Database (CSD),18 the world’s essential database for crystal structures. Previously, a challenge was that structures in the CSD were not labeled as MOF and there was no simple way to search specifically for MOFs, which is now possible as we will explain below. Another challenge is that the crystal structure files of MOFs deposited to the CSD generally have several problems such as existence of solvent molecules in the pores that must be removed before molecular simulations to make pores available for gas adsorption. Some other MOF structures have disordered and/or missing atoms that should be ‘corrected’ before the molecular simulations. Recent studies aimed to build a MOF library. For example, Goldsmith et al.19 started with 20,000 MOFs from the CSD, excluded ~16,000 because of the problematic crystal structures such as missing hydrogen atoms and disorders. They then computed theoretical H2 storage capacity for ∼4,000 structures. Snurr’s group20 also started with 20,000 MOFs and ended up with 4,764 MOFs by excluding highly disordered and difficult-to-correct materials. In this way, they constructed a very useful database, CoRE MOF (computation-ready experimental MOFs) and examined CH4 storage in these MOFs using molecular simulations. Li et al.21 recently started with CoRE MOF database, discarded MOFs with zero accessible surface areas and examined 2,054 MOFs for CO2/H2O separation. Jiang’s group22 also used the CoRE MOF to study CO2/N2 and CO2/CH4 separations using molecular simulations. Jimenez’s group23 very recently reported the most complete collection of MOFs maintained and updated, for the first time, by the CSD. They discussed that a small number of non-MOF structures is present in the CoRE MOF whereas some MOF structures are missing. In their collection, they included all MOFs and integrated the library with CSD to allow subsequent addition of new MOFs. To the best of our knowledge, this complete MOF library has not been screened for any gas separation application to date. In this work, we performed high-throughput molecular simulations to identify the adsorption-based CH4/H2 separation performances of this complete collection of MOFs. Separation of CH4 from H2 is industrially and economically important because H2 purification from various process streams constitutes the largest commercial use of pressure swing adsorption (PSA).24 Highly efficient adsorbents are strongly needed for PSA. Various porous materials, such as single walled carbon nanotubes,25 carbons,26 titanosilicates27 and zeolites28 have been studied for this separation using molecular simulations. Predicted CH4/H2 selectivities of these traditional adsorbents are not sufficiently high for practical applications, therefore MOFs have been recently considered 3 ACS Paragon Plus Environment


as alternatives to achieve high CH4/H2 selectivities. Molecular simulations were performed for isoreticular MOFs (IRMOFs)29,30 mixed-ligand interpenetrated MOFs,31 zeolitic imidazolate frameworks (ZIFs)32,33 for adsorption-based CH4/H2 separation. Either a single or a few different types of MOFs was examined in these simulations. Wu et al.34 computed selectivities of 105 different MOFs and our research group35 studied 250 MOF structures for CH4/H2 separations using molecular simulations. These studies showed that MOFs have higher adsorption selectivities than zeolites. As this literature review shows current studies on MOF adsorbents for separation of CH4/H2 mixtures have investigated only a very small fraction of the MOFs reported in the CSD. Considering the large variety and number of available MOFs, there may be many existing MOFs with better separation performances. Furthermore, structure-performance relations which greatly guide the design and development of new materials can be only generated if a large number and diversity of MOFs are studied. With these motivations, we performed the first high-throughput molecular simulation study in the literature which unlocks the potential of all existing MOFs in the world for adsorption-based CH4/H2 separation. Adsorption data of CH4/H2 mixtures obtained from the grand canonical Monte Carlo (GCMC) simulations were used to calculate several adsorbent selection metrics such as adsorption selectivity, working capacity, adsorbent performance score, sorbent selection parameter, and regenerability of MOFs. Top performing MOF adsorbents were identified based on the combination of these metrics. The types of the metal sites available in the highly promising MOFs were also identified. Separation performances of MOFs were compared with traditional adsorbents, such as zeolites, carbon-based materials and silica gels to assess the potential of MOFs for CH4/H2 separations. We examined the relations between structural properties such as pore sizes, porosities, surface areas of MOFs and their selectivities to provide the structure-performance relationships that can serve as a map for experimental synthesis of new MOFs with better gas separation performances. Relations between the heat of adsorption, adsorbility and selectivities were also explored. Based on these correlations, a simple mathematical model was suggested that can accurately predict MOFs’ selectivities based on easily computable properties. 2. Computational details: 2.1 MOFs: We used the most complete collection of MOFs available and the only collection integrated within the CSD database.23 This collection is comprised of 69,666 MOFs with a 4 ACS Paragon Plus Environment

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wide range of chemical and structural properties. The non-disordered MOF subset contains 54,808 structures and we started with it. The bound and unbound solvents in MOFs were removed using a Python script available in the literature.23 We computed physical properties of MOFs such as accessible surface area, accessible pore volume, pore limiting diameter (PLD) and the largest cavity diameter (LCD) using Zeo++ software.36 We then refined the collection to only have the MOFs that have non-zero accessible gravimetric surface areas and PLDs greater than 3.75 Å so that both CH4 and H2 molecules can be adsorbed into the pores. After this refinement, we ended up with 4,350 different MOFs that span a wide range of chemical functionalities. Figure S1 shows the distribution of PLDs and LCDs for all MOFs together with the kinetic diameters of H2 and CH4 molecules. PLDs and LCDs of MOFs are in the range of 3.75-31 Å and 4.0-33.6 Å, respectively. 3,842 MOFs have LCDs greater than PLDs and 508 MOFs have almost identical PLDs and LCDs. We also showed the relation between porosity, surface area and LCD in Figure S2. The surface areas (porosities) of MOFs range from 27.7 to 7,091.7 m2/g (0.28 to 0.91). Only a small number of MOFs (32) has accessible surface areas 4,000 m2/g). A large number of MOFs (3,566) has porosities between 0.5 and 0.7. 461 MOFs have mediocre porosities (0.3-0.4) and 323 MOFs have very large porosities (>0.8). As the porosity increases, surface areas and LCDs also increase as shown in Figure S2. MOFs with high LCDs (18-33.6 Å) have high porosities (0.65-0.91) and high surface areas (most of them larger than 1,000 m2/g). All these computed structural properties of MOFs together with their CSD names are given in SI. 2.2 Molecular simulations: Grand canonical Monte Carlo (GCMC) simulations have been widely used to compute gas adsorption isotherms in porous materials.37 We performed GCMC simulations as implemented in the RASPA simulation code.38 Both single-component and mixture GCMC simulations were performed. All the GCMC simulations were performed at room temperature. Three different types of moves were considered for single-component GCMC simulations including translation, reinsertion and swap of a molecule. In the binary mixture GCMC simulations, another trial move, identity exchange of molecules was also performed. We considered equimolar bulk mixtures in the simulations. The Lorentz-Berthelot mixing rules were employed. The cut-off distance for truncation of the intermolecular interactions was set to 13 Å. The simulation cell lengths were increased to at least 26 Å along each dimension and 5 ACS Paragon Plus Environment


periodic boundary conditions were applied in all simulations. For each MOF, simulations were carried out for 10,000 cycles with the first 5,000 cycles for initialization and the last 5,000 cycles for taking ensemble averages. Peng-Robinson equation of state was used to convert the pressure to the corresponding fugacity. The isosteric heat of adsorption (Qst), difference in the partial molar enthalpy of adsorbate between the bulk and adsorbed phases, and the Henry’s constants of gas molecules were also calculated at the limit of zero-coverage (infinite dilution) using the Widom particle insertion method.37 More details of these simulations can be found in the literature.37, 39 Single-site spherical Lennard-Jones (LJ) 12-6 potential was used to model H240 and CH441 molecules which are given in Table S1. The potential parameters of MOF atoms were taken from the Universal Force Field (UFF).42 These potentials and force fields were selected based on the results of our previous simulation studies.43-45 In our previous studies, we showed very good agreement between our simulation results and experimentally measured CH4 and H2 adsorptions in many MOFs.45-46 For example, we validated the accuracy our CH4 simulations by comparing with 267 experimental adsorption data of CH4 in a very large number of MOFs at a variety of pressures and temperatures.45 Similarly, we showed the good agreement between simulated H2 uptake and the experimentally reported data of a variety of MOFs including many sub-families such as Bio-MOFs, COFs, CPOs, IRMOFs, PCNs, ZIFs.46 Good agreements between our simulations and experiments for full CH4 and H2 adsorption isotherms can be seen in both of these studies. As examples, we showed a comparison of the adsorption isotherms of CH4 and H2 in some prototypical MOFs, IRMOF1, CuBTC, UiO-66 and ZIF-8 in Figure S3. Comparison of our simulated adsorption isotherms of CH4 and H2 with the experiments for various different MOFs can be also seen in Table S2. We also showed the good agreement between experimentally reported CH4/H2 selectivities and simulated ones for several ZIFs47 and MOFs.35 These results validated the accuracy of our molecular simulations and the choice of the force fields. MOFs were assumed to be rigid in their reported crystallographic structures in simulations. This assumption has been used in all high-throughput molecular simulation studies of MOFs to save significant computational time. The main aim of this work is to demonstrate the potential value of a material using efficient computational screening approach prior to experiments. Furthermore, since the gas molecules we studied are relatively small compared to the MOFs’ pore sizes, flexibility is expected to have a negligible effect on the gas adsorption. 2.3 Adsorbent evaluation metrics: 6 ACS Paragon Plus Environment

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Results obtained from molecular simulations of equimolar CH4/H2 mixtures were used to predict separation performances of MOFs. Several adsorbent evaluation metrics have been defined and used to date. In this study, we considered five commonly used metrics, adsorption selectivity (Sads), working capacity (∆N), adsorbent performance score (APS), sorbent selection parameter (Ssp), and regenerability (R%). Mathematical definitions of these metrics can be seen in Table 1. Adsorption selectivity, Sads is the most widely used metric to evaluate adsorbents and it is simply defined as the ratio of compositions of the adsorbed gases (x) in the adsorbent normalized by the ratio of bulk phase compositions (y) of components. In Table 1, subscript 1 represents the strongly adsorbed gas (CH4) and subscript 2 represents the weakly adsorbed gas (H2). Working capacity (∆N) is defined as the difference between the gas uptakes (N) at the adsorption and desorption pressures in the unit of mol gas per kg adsorbent.48 It is used for the strongly adsorbed component of the gas mixture, which is CH4 in this work. Adsorbent performance score (APS) was recently defined by Chung et al.16 as the product of selectivity and working capacity to easily identify the top performing adsorbent materials. Sorbent selection parameter (Ssp) includes the ratio of working capacities.49 Regenerability (R%) is an important metric in cyclic PSA processes35 since it determines the per cent regeneration of the adsorption sites while desorption step is ongoing.48 All these metrics were computed for equimolar CH4/H2 mixtures at room temperature at an adsorption pressure of 10 bar and desorption pressures of 1 bar since most of the PSA separations in industry are performed under these conditions. Previous molecular simulation studies reported that selectivities of MOFs do not significantly change with the temperature.50 It is important to highlight two important aspects of our work in terms of using performance evaluation metrics to assess MOF adsorbents: (1) Some high-throughput molecular simulation studies17,

21

calculated adsorption

selectivity of MOFs using the ratio of Henry’s constants of single-component gases computed at zero loading and reported ideal selectivities whereas a smaller number of studies16 used mixture adsorption data to compute mixture selectivities. Ideal selectivity at infinite dilution represents the intrinsic separation capacity of a material when there is no interaction between gas species. Ideal selectivity may significantly differ from the mixture selectivity when interactions (competitive or cooperative) between two gas molecules exist.51 The deviation between ideal and mixture selectivities becomes more pronounced as the pressure increases due to the multi-component mixture effects that are dominant at high pressures.35 Therefore,



in this work, we calculated ‘mixture selectivity’ of all MOFs using the adsorbed loadings of each gas species at the adsorption pressure of interest to represent real operating conditions. (2) Selectivity has been generally considered as the most critical factor to rank MOFs for CH4/H2 separations in the previous molecular simulation studies that we summarized above.29-35 Other parameters that can be used to compare adsorbents also exist but rarely studied. For example, Rege and Yang49 proposed the pressure swing adsorption selection parameter to compare two adsorbents for a gas separation on the basis of their equilibrium adsorption capacities and Llewelyn et al.52 defined an adsorbent performance indicator to evaluate adsorption-based gas separation performances of MOFs. In our recent study,53 we showed that R% is a very important metric to screen materials in identifying the most promising adsorbents. For example, it was shown that several MOFs with high CO2 selectivities exhibit very low R%, limiting their practical usage as adsorbents.53 In order to efficiently rank the very large number of MOFs considered in this study, we first eliminated MOFs having R%1,000 at 1 bar. Similarly, most MOFs (3,939) exhibit mixture selectivities between 10 and 1,000 whereas only 3 MOFs have very high mixture selectivities, >1,000 at 10 bar. Results shown in Figure 1 suggest that ideal selectivity can make a preliminary estimate for the separation performance of MOFs if the MOF has a low selectivity, less than 10. However, for MOFs that are promising with mixture selectivities greater than 100, ideal selectivity significantly underestimates the mixture selectivity. This means it is not accurate to screen MOFs based on ideal selectivity for the identification of the most promising materials. Therefore, we used the mixture selectivity throughout this manuscript and simply refereed it as selectivity in the remaining sections. Figure 2 shows both the CH4/H2 selectivity and CH4 working capacity of MOF adsorbents computed at an adsorption pressure of 10 bar and desorption pressure of 1 bar. Selectivities of MOFs are in the range of 1.4-2,028 whereas working capacities vary from 0.001 to 7.3 mol/kg. Most MOFs have a trade-off between selectivity and working capacity. Materials with high selectivities (>500) generally suffer from low working capacities (100) but low working capacities (3 mol/kg) but low selectivities (2,000) but they suffer from very low R% (85%) and high selectivity are represented by red stars in Figure 4(a). For example, CAYSIE, OXUPUT, and CAYSOK have selectivities of 44, 47, 49 and R% of 87, 86, 86%, 10 ACS Paragon Plus Environment

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respectively. In Figure 4(b), R% of MOFs are plotted as a function of their APSs and the top 20 regenerable MOFs with the highest APSs are shown with red stars. Results so far suggested that considering selectivity is not enough to decide if a MOF is promising for gas separation application because a MOF with high selectivity might have a low ∆N and/or R% which will make the separation process economically inefficient. Therefore, we used the following ranking strategy to rank MOFs: we first focused on the MOFs with R%>85. Among these MOFs, we identified the top 20 MOFs according to their selectivities and APSs, and listed their performance evaluation metrics in Tables 2 and 3. 8 of the MOFs, which are RORVAX, PIBXOP, KEWZOD, KINNEC, JOVXUP, SIKGEA, FEHCOM and ROHKAC, were found to be common in both tables and represented in bold. These MOFs can be considered as the best adsorbent candidates for CH4/H2 separations in terms of all adsorbent evaluation metrics. It is important to note that in addition to having high scores in performance evaluation metrics, MOFs should be also stable in order to find place in real applications. We searched for the stability information of these 8 MOFs. KEWZOD55 and JOVXUP56 were reported to be stable at high temperatures. FEHCOM57 was reported to keep its structural integrity after complete removal of guests and guest-free ROHKAC58 was reported to be thermally stable up to ~250 °C. PIBXOP was reported as very stable in common organic solvents.59 RORVAX was found to be the only promising MOF which was reported to lose its porosity after solvent removal.60 We could not find any information about the stability of KINNEC61 and SIKGEA.62 Among the MOFs discussed in Figures 2 and 3, UTEXIB63 was identified as a robust structure with thermal stability up to 450°C whereas no information was available for QUQQID64 and WEHJAW.65 Stabilities of the most promising materials identified in this work are most likely to be examined under practical gas separation experiments in future studies. 3.3 Structure-performance relations: Understanding the relation between structural properties of MOFs and their performances for a target gas separation is highly useful not only to easily select the promising materials among many available ones but also to guide the further design and synthesis of new MOFs with exceptionally high gas separation properties. For example, Fernandez and Woo66 reported a large-scale, quantitative structure-property relationship analysis for hypothetical MOFs and examined the effects of pore sizes and void fraction on the simulated CH4 storage capacities. Snurr’s group67 examined structure-property relations of 11 ACS Paragon Plus Environment


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a large number of hypothetical MOFs for their CO2 separation performances from CH4 and N2. Although, these relations provide an insight about the structural properties that would lead to design of new MOFs with high separation performance, it is not certain that the relations found for hypothetical MOFs will be valid for real MOFs. The strength of this study is that every MOF we considered is real, experimentally synthesized. Selectivities of adsorbents are generally correlated with the difference of isosteric heats of adsorption values of the two gas species that are aimed to be separated. Based on the Langmuir

adsorption

theory,

Yang

et

al.68

suggested

a

correlation,

ln S = 0 .8558 + ∆ Q 0st (R × T ) to show the relation between the difference of heat of

adsorption values of gases at infinite dilution loading ( ∆ Q st0 ) and selectivity (S). Here, R is the ideal gas constant and T is temperature. This correlation was proposed to be suitable for all the physical adsorption-based separation of gas mixtures in porous materials. We tested its validity for all the MOFs for the first time in the literature. Selectivities of MOFs computed at three different pressures, at infinite dilution (using the ratio of Henry’s constants of each competing gas molecule), 1 bar, and 10 bar, are shown in Figure 5 as a function of ∆ Q st0 together with the line obtained from the Yang’s correlation. Predictions of the correlation are in a very good agreement with the infinite dilution selectivities as shown in Figure 5(a) since this correlation is based on ∆ Q st0 computed at zero pressure. The correlation was found to predict the mixture selectivities of MOFs well at low ∆ Q st0 values (≤15 kJ/mol) at 1 bar and at lower ∆ Q st0 values (≤12 kJ/mol) at 10 bar as shown in Figures 5(b) and (c). Yang’s correlation generally overestimates MOFs’ selectivities as ∆ Q st0 increases. This can be explained with the following discussion: the correlation considers zero-coverage enthalpies, in other words, it only accounts for adsorbent-gas interactions. As the pressure increases, gas-gas interactions play more important role in adsorption but the correlation does not include these effects. Therefore, deviations between selectivities and ∆ Q st0 become more observable at higher pressures. Results of Figure 5 suggest that Yang’s correlation can be used for a rough initial screening of MOFs for CH4/H2 separations. Inspiring from the solubility theory,69 inverse of the adsorbility (1/∆AD) was recently defined as (1 ∆AD = φ ∆ Q st0 ) .34 Figure 6 shows that as 1/∆AD decreases, selectivity increases. 1/∆AD correlates well with selectivity of MOFs both at 1 and 10 bar. This means inverse of the adsorbility can be used to make an accurate estimation about the selectivities of MOFs at practical operating pressures.


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Among many structural/chemical parameters, ∆ Q st0 is not the easiest one to obtain because it requires either a computational study or an experimental measurement. In order to establish an easier-to-use structure-performance relation, we examined the correlations between selectivities of MOFs and their easily measurable/computable structural properties such as pore sizes, porosities and surface areas. Correlations between APS values of MOFs and structural properties were also examined, however much weaker correlations was found compared to the ones between selectivities and structural properties. Figure 7 shows that as the LCDs of MOFs decrease, selectivities increase because small cavities are more favorable adsorption sites for gas molecules due to the strong confinement of the gas molecules. Although the relation is not perfect, Figure 7 suggests that MOFs with LCDs larger than 15 Å exhibit lower selectivities, 5,000 m2/g) generally exhibit selectivities less than 10. Results so far indicated that selectivities usually does not present very good correlations with a commonly used single property such as pore size, ϕ, Sacc, ∆ Q st0 but an interplay of these factors.34, 70 Correlation coefficients (R2 values) between selectivity and five parameters that we discussed so far ∆ Q st0 , 1/∆AD , LCD, ϕ, Sacc, are given in Figure S4 both for 1 and 10 bar. At 1 bar, selectivity shows strong correlations with 1/∆AD (R2 =0.82) and ∆ Q st0 (R2=0.78), but there is not a strong correlation between Sacc or LCDs of MOFs and selectivities (R230%. Considering the large variety in chemistry, topology and physical properties of MOFs, it is somehow natural that establishing a simple mathematical model that works for all the available MOFs is impractical. When we analyzed the MOFs for which the deviations are high, it occurred that these are the MOFs that do not follow the general structure-performance relations. For example, Figures 6 and 7 suggested that as 1/∆AD and LCD decreases, selectivities increase and this is what the model uses in making selectivity predictions. There is a MOF which have a high LCD (11 Å) but it exhibits high selectivity (2,027) in contract to the general LCD-selectivity relation we discussed. As a result, the model significantly underpredicts the selectivity of this material (80). To summarize, our suggested model can be used to make reasonable selectivity predictions for 70% of the MOFs, which follow the general structure-performance correlations, within 85% are labeled. Zn (33%), Cu (16%) and Co (11%) are the most observed metals in these easily regenerable MOFs. Other metals seen in highly selective MOFs that are located at the right of that figure are Cr, Mn, Pr, and U. It is challenging to quantify the effect of the metal type on selectivity but our results suggest that MOFs with Zn, Cu, Co and Mn appear to be more promising in terms of high CH4/H2 selectivity and high R%. 4. Conclusion: Considering the rapid increase in the number of synthesized MOFs, high-throughput computational screening studies play an important role in identification of MOFs with high 14 ACS Paragon Plus Environment

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gas separation performance. In this study, we screened the recent MOF database to show the ultimate performance limits of MOFs for CH4/H2 separations. Our results showed that MOFs can outperform traditional adsorbent materials such as zeolites, activated alumina, silica gel, carbon-based materials in CH4/H2 separations due to their higher selectivities and working capacities. The top 20 materials that combine high selectivity, high working capacity and high regenerability were identified and listed. We also examined the relations between structural properties of 4,350 MOFs and their gas separation performances to guide the future experimental synthesis efforts towards the structural properties that are likely to result in materials with better CH4/H2 separation abilities. A simple model was also suggested to predict the CH4/H2 selectivity of MOFs based on easily measurable/computable structural properties. The ultimate choice of the MOF should be, of course, also include other parameters such as stability, cost, synthesis conditions of the MOFs. We believe that our results will trigger experimental efforts to accelerate design of new MOFs with better separation capacities. Acknowledgement: S.K. acknowledges ERC-2017-Starting Grant. This study has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-2017-Starting Grant, grant agreement No 756489-COSMOS). Authors thank Zeynep Sumer and Zehra Ozgenlik for their fruitful discussions. Supporting Information. Pore limiting diameters (PLD) and the largest cavity diameters (LCD) of MOFs, porosities and accessible surface areas of MOFs, R2 values showing the relation between selectivity and several parameters calculated at 1 bar and 10 bar, interaction potential parameters used for gas molecules, coefficients of the model used to predict selectivity of MOFs. 5. References: (1) Li, H.; Eddaoudi, M.; O'Keeffe, M.; Yaghi, O. M. Design and Synthesis of an Exceptionally Stable and Highly Porous Metal-Organic Framework. Nature 1999, 402, 276−279. (2) Farha, O. K.; Hupp, J. T. Rational Design, Synthesis, Purification, and Activation of MetalOrganic Framework Materials. Acc. Chem. Res. 2010, 43, 1166−1175. (3) Getman, R. B.; Bae, Y. S.; Wilmer, C. E.; Snurr, R. Q. Review and Analysis of Molecular Simulations of Methane, Hydrogen, and Acetylene Storage in Metal-Organic Frameworks. Chem. Rev. 2012, 112, 703−723. (4) Adatoz, E.; Avci, A. K.; Keskin, S. Opportunities and Challenges of MOF-Based Membranes in Gas Separations. Sep. Purif. Technol. 2015, 152, 207−237.



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Page 20 of 35

Table 1. Adsorbent evaluation metrics used in ranking of MOFs. Parameter

Formula

Selectivity

S ads (1 / 2 ) =

Working capacity

∆N = Nads - Ndes

Adsorbent performance score

APS = Sads(1/ 2) × ∆N1

Sorbent selection parameter

Ssp =

Per cent regenerability

R% =

x1 / x 2 y1 / y 2

(Sads (1 / 2 ) ) 2 (Sdes (1 / 2 ) )

×

∆N1 ∆N 2

∆N 1 × 100 % N 1,ads


Page 21 of 35 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. 20 top performing MOFs ranked based on selectivity. MOFs written in bold are common in Tables 2 and 3. MOFs

SCH4 / H2

CAYSOK OXUPUT CAYSIE YOVTOS CAYYEG SAZQEQ QANSEE SUTWOT FEHCOM ROHKAC KINNEC JOVXUP KEWZOD RORVAX SIKGEA HOWHEI PIBXOP RINPUZ PACZUQ CATDEH

48.59 47.36 43.96 37.95 36.30 34.32 32.92 31.44 31.18 30.49 30.24 30.01 29.96 29.89 29.85 29.70 28.94 28.67 28.33 28.10

∆NCH4 (mol/kg) 1.65 0.38 1.55 1.46 1.44 1.32 1.22 2.39 3.68 3.55 4.51 4.53 4.57 6.33 4.25 3.03 4.84 3.11 1.73 1.08

R (%)

APS (mol/kg)

86.31 85.53 86.68 85.99 86.31 87.27 88.55 85.09 86.51 86.01 85.16 85.02 85.00 85.16 85.30 85.04 85.75 86.28 85.47 87.90

80.31 18.09 68.04 55.36 52.44 45.46 40.07 75.24 114.60 108.08 136.50 136.07 136.95 189.16 126.94 89.85 140.16 89.04 49.10 30.34



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Table 3. 20 top performing MOFs ranked based on APS. MOFs written in bold are common in Tables 2 and 3. MOFs

SCH4 / H2

RORVAX QUQQAV VOCXUH PIBXOP KEWZOD KINNEC JOVXUP METPAC SIKGEA JUCKEZ FIRNUR QUQQUP AFEHUO FEHCOM JUCKID KUTPEW ROHKAC DABWUA EYEYAJ CUFFOZ

29.89 27.13 27.59 28.94 29.96 30.24 30.01 27.97 29.85 27.24 24.08 22.72 27.06 31.18 25.65 25.84 30.49 26.62 24.92 24.35

∆NCH4 (mol/kg) 6.33 5.65 5.25 4.84 4.57 4.51 4.53 4.67 4.25 4.56 5.14 5.32 4.43 3.68 4.31 4.24 3.55 3.92 4.15 4.22

R (%)

APS (mol/kg)

85.16 86.11 85.50 85.75 85.00 85.16 85.02 85.64 85.30 85.20 86.29 87.17 86.56 86.51 86.09 85.68 86.01 85.24 85.58 85.32

189.16 153.35 144.83 140.16 136.95 136.50 136.07 130.69 126.94 124.24 123.71 120.81 119.98 114.60 110.61 109.47 108.08 104.36 103.46 102.87


Page 23 of 35 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 4. Frequency of metals in 4350 MOFs. 774 MOFs have more than one type of metal.

Ag Al As Au B Ba Be Bi Ca Cd Ce Co Cr Cs Cu Dy Er Eu Fe Ga Gd Ge Hf

0.063 0.006 0.000 0.005 0.025 0.004 0.001 0.003 0.004 0.106 0.007 0.105 0.007 0.003 0.167 0.011 0.007 0.016 0.037 0.003 0.012 0.001 0.001

Hg Ho In Ir K La Li Lu Mg Mn Mo Na Nd Ni Np Os Pb Pd Pr Pt Rb Re Rh

0.006 0.003 0.024 0.002 0.010 0.010 0.005 0.002 0.010 0.048 0.008 0.018 0.012 0.060 0.000 0.000 0.009 0.005 0.007 0.004 0.001 0.002 0.005


Ru Sb Sc Si Sm Sn Sr Tb Tc Te Th Ti Tl Tm U V W Y Yb Zn Zr

0.006 0.005 0.001 0.012 0.006 0.003 0.003 0.011 0.000 0.000 0.000 0.002 0.000 0.003 0.006 0.007 0.012 0.003 0.006 0.259 0.006


(a)1 bar 4

10

3

2

10

SCH

4/H2

, mixture

10

1

10

0

10

0

10

1

2

10

3

10

4

10

10

SCH /H , ideal 4

2

(b)10 bar 3

2

10

4/H2

, mixture

10

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 35

1

10

0

10

0

10

1

2

10

10

3

10

SCH /H , ideal 4

2

Figure 1. Comparison of ideal and mixture selectivities of MOFs at (a)1 bar (b)10 bar. Red line represents x=y to guide the eye.


Page 25 of 35

10

APS

4

0.03 100.00

10

3

300.00

4/H2

500.00

10

2

10

1

10

0

801.90

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0

1

2

3

4

5

6

7

8

∆ΝCH (mol/kg) 4

Figure 2. Selectivities and working capacities of MOFs color-coded according to their adsorbent performance scores.



7

10

6

10

5

10

4

SSP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 35

10

3

10

2

10

1

10

0

10

0

500

1000

1500

SCH

4/H2

Figure 3. Sorbent selection parameters and selectivities of MOFs.


2000

Page 27 of 35

100 (a) 80

R (%)

60

40

20

0 0 10

1

10

2

10

3

10

4

10

SCH

4/H2

100 (b) 80

60

R (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


40

20

0 0

200

400

600

800

APS (mol/kg)

Figure 4. R% of MOFs as a function of (a)selectivities (b)APSs. Red dashed lines show R%=85%. Red stars represent the most promising MOFs with the highest (a)selectivities (b)APSs above the red line.



10

10

5

10

(a)infinite dilution

(b) 1 bar

9

10

8

4

10

10

7

10

2

10

5

10

4

4

3

10

SCH /H

2

6

SCH /H

4

10

2

10

3

10

2

1

10

10

1

10

0

10

0

0

10

20

0

30

40

50

10

60

0

10

20

30

40

50

0

∆Qst (kJ/mol)

∆Qst (kJ/mol) 4

10

(c)10 bar

3

2

10

4

SCH /H

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 35

2

10

1

10

0

10

0

10

20

30

40

50

60

0

∆Qst (kJ/mol)

Figure 5. Relation between ∆ Q 0st and selectivities of MOFs at (a)infinite dilution (b)1 bar (c)10 bar. Red line shows the slope of Yang’s correlation defined in the text.


60

Page 29 of 35

(a) 1 bar 4

10

3

SCH

4/H2

10

2

10

1

10

2

R =0.82 0

10

0.0

0.1

0.2

0.3

0.4

0.5

1/∆AD (mol/kJ)

(b) 10 bar 3

10

4/H2

2

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10

1

10

2

R =0.74 0

10

0.0

0.1

0.2

0.3

0.4

0.5

1/∆AD (mol/kJ)

Figure 6. Relation between selectivities and the inverse of adsorbility of MOFs at (a)1 bar (b)10 bar. Red line shows the correlation.



(a) 1 bar 4

10

3

SCH

4/H2

10

2

10

1

10

0

10

5

10

15

20

25

30

35

25

30

35

LCD (Å)

(b) 10 bar 4

10

3

4/H2

10

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 35

2

10

1

10

0

10

5

10

15

20

LCD (Å) Figure 7. Relation between the largest cavity diameters and selectivities of MOFs at (a)1 bar (b) 10 bar.


Page 31 of 35

(a) 1 bar 4

10

3

SCH

4/H2

10

2

10

1

10

0

10

0.2

0.4

0.6

0.8

1.0

0.8

1.0

φ

(b) 10 bar 3

4/H2

10

2

10

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

10

0

10

0.2

0.4

0.6

φ

Figure 8. Relation between porosities and selectivities of MOFs at (a)1 bar (b)10 bar.



5

10

(a) 1 bar

4

10

3

SCH

4/H2

10

2

10

1

10

0

10

0

2000

4000

6000

8000

6000

8000

2

Sacc (m /g) 4

10

(b) 10 bar

3

4/H2

10

2

10

SCH

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 35

1

10

0

10

0

2000

4000 2

Sacc (m /g) Figure 9. Relation between accessible surface areas and selectivities of MOFs at (a)1 bar (b) 10 bar.


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


SCH /H , predicted 4 2

Page 33 of 35

10

3

10

2

10

1

10

Number of MOFs 756 1523 2243 3018 3556

0

10

0

10

1

10

2

2

Error%

1 High-throughput Computational Screening of the MOF Database for

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