Tuning Binding Tendencies of Small Molecules in Metal-Organic

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C: Surfaces, Interfaces, Porous Materials, and Catalysis

Tuning Binding Tendencies of Small Molecules in Metal-Organic Frameworks with Open Metal Sites by Metal Substitution and Linker Functionalization Wenqin You, Yang Liu, Joshua D. Howe, Dai Tang, and David S. Sholl J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b08855 • Publication Date (Web): 05 Nov 2018 Downloaded from http://pubs.acs.org on November 6, 2018

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The Journal of Physical Chemistry

Tuning Binding Tendencies of Small Molecules in MetalOrganic Frameworks with Open Metal Sites by Metal Substitution and Linker Functionalization Wenqin You, Yang Liu, Joshua D. Howe, Dai Tang and David S. Sholl* School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States Corresponding author: E-mail: [email protected] Abstract Metal-organic frameworks (MOFs) with open metal sites (OMS) are known to be selective for ethylene relative to ethane. In practical applications of this separation, the presence of other small molecules such as H2O, CO, and C2H2 may affect the suitability of sorbents. We used density functional theory (DFT) calculations to compute the binding energies of H2O, CO, C2H2, C2H4, and C2H6 in M-BTC (BTC = 1,3,5-benzenetricarboxylic acid) with 12 different metals forming OMS (M = Mg, Ti, V, Cr, Mo, Mn, Fe, Ru, Co, Ni, Cu, and Zn). To probe the generality of these results for MOFs containing other ligands, we performed similar calculations for metalsubstituted MOFs based on four more materials with dimeric Cu sites. Our results provide useful insights into the variations in binding energies that are achievable by metal substitution in this broad class of MOFs, as well as pointing towards feasible adsorption-based separation strategies for complex molecular mixtures. Zn OMS MOFs were predicted to have the highest C2H4/C2H6 selectivity, but the strong binding energy of solvents and other small molecules in these materials may create practical challenges. We used DFT calculations to examine whether functionalizing linkers in these materials with electron withdrawing (-fluorine) and donating (-methyl) groups offer a useful way to tune molecular binding energies on OMS in these materials.

1. INTRODUCTION Industrial scale separation of olefins and paraffins is currently accomplished by cryogenic distillation, a highly energy intensive process.1 This situation has created great interest in adsorption-based separations for these mixtures. One class of candidate materials for olefin/paraffin separations is metal-organic frameworks (MOFs) with open metal sites (OMS), which have favorable π-π interactions between olefin double bonds and OMS.2 One challenge associated with these separations is that other molecular species, for example, CO, C2H2, or H2O, that potentially bind to OMS may also be present in feed streams, causing competition for these sites.3 The binding affinities of small molecules on OMS in MOFs are affected by the electronegativity and binding geometries of molecules at the metal centers, which are further 1 ACS Paragon Plus Environment

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determined by the factors such as the topology and organic linker of MOFs, the number and species of atoms bonded to the metal, and the species of metal.3a, 4 Our recent work assessed the binding energies of ethylene, water, and carbon monoxide on a set of 65 MOFs with open Cu sites in the form of Cu dimers.5 One outcome of that work was that the influence of the organic linker and MOF topology is modest in determining the binding energies of small molecules on open Cu sites. It is likely, in contrast, that varying the identity of the metal forming an OMS can have a strong effect on molecular binding energies. One of the key objectives of this paper is to explore this situation for the small molecules listed above for a large collection of isostructural MOFs. Several previous studies have characterized molecular binding energies on OMS in groups of isostructural MOFs. Lee et al3a. systematically investigated the binding enthalpies of 14 small molecules in a series of isostructural M-MOF-74 materials (M = Mg, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) using Density Functional Theory (DFT). A smaller set of M-MOF-74 materials (M =Zn, Co, Ni, and Mg) were also experimentally studied to determine the adsorption properties of toxic gases.6 Geier et al.7 illustrated selective adsorption of ethylene over ethane in the M-MOF-74 family (M = Mg, Mn, Fe, Co, Ni, and Zn). Apart from M-MOF-74, a family of M3(BTC)2 materials (denoted below as M-BTC) is another OMS MOF with abundant metal analogues. Cu-BTC (also known as HKUST-1) is the best known of these materials. Wade et al.8 reported the synthesis, activation, and heats of CO2 adsorption in M-BTC with M=Cr, Fe, Ni, Cu, Mo, and Ru. Wu et al.9 computationally investigated the adsorptive separation of methanol and acetone in the M-BTC with M = Ti, Fe, Cu, Co, Ru, and Mo. In this work, we use DFT calculations to calculate the binding energy of five small molecules (H2O, CO, C2H2, C2H4 and C2H6) on a wide range of MOFs with OMS. We first consider the impact of metal substitution in one specific material (Cu-BTC) in detail. To this end, we examine twelve metal-substituted M-BTCs, including seven experimentally reported materials (M = Cr,10 Mo,11 Fe,12 Ru,13 Ni,14 Cu,15 Zn16) and five hypothetical materials (M = Mg, Ti, V, Mn, Co). To understand the generality of our results for MOFs containing ligands other than BTC, we perform similar calculations for metal-substituted MOFs based on four additional experimentally accessible materials with dimeric Cu OMS. Our results provide useful insights into the variations in binding energies that are achievable by metal substitution in this broad class of MOFs, as well as pointing towards feasible adsorption-based separation strategies for complex molecular mixtures. Although the identity of the linker in an OMS MOF has a smaller impact on molecular binding energies than the identity of the metal, it is still useful to understand how binding energies can be tuned via linker functionalization. A number of studies have used molecular simulations to study the effect of linker functionalization on the binding energies of physisorbed molecules in MOFs.17 Less attention has been paid to this situation for binding at OMS. We probe this issue for M-BTC MOFs by contrasting the influence of electron withdrawing groups (specifically, fluorine) and electron donating groups (specifically, methyl groups).

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2. METHODS Molecular binding energies in MOFs were calculated using spatially periodic density functional theory (DFT) using the Vienna Ab Initio Simulation Package (VASP),18 along with a plane-wave basis set and projected-augmented wave (PAW)19 pseudopotentials. All calculations used the Perdew, Burke, and Ernzerhof (PBE)20 GGA exchange-correlation functional with D3 dispersion corrections (PBE-D3)21 unless noted otherwise. The reason we choose PBE-D3 is that Grajciar et al.22 evaluated the accuracy of nine exchange-correlation functionals for the description of adsorption in Cu-BTC and reported that PBE-D2 and -D3 performed the best among the nine functionals with respect to DFT/CC. Additionally, our recent work compared the performance of two dispersion corrected DFT methods (PBE-D3 and vdW-DF2) and found that the relative binding energies of pairs of molecules on the OMS in Cu-BTC are not highly sensitive to the choice of functional. 5 Our previous work also compared the binding energies for water, ethylene and CO in CuBTC obtained using PBE-D3 DFT calculations with experimental heats of adsorption, indicating good (although of course not perfect) agreement of the calculations with experimental observations. To the best of our knowledge, experimental information on the heat of adsorption of the species we consider below in M-BTC materials when M is not Cu is not currently available, so we are not able to directly benchmark our calculations in these materials against experimental data.” During geometry relaxation of MOFs, we simultaneously optimized both the lattice parameters and atomic positions using a plane-wave cutoff energy of 520 eV and Γ-point sampling for Brillouin zone integration. Using a quasi-Newton method, we relaxed geometries until the force on each atom is smaller than 0.03 eV/Å. DFT calculations based on the local density approximation (LDA) and generalized-gradient approximations (GGA) often fail to describe energetics and geometries around OMS, which can be corrected by adding an additional Hubbard-like term.23 DFT+U was used to describe the strong on-site Coulomb interaction of localized electrons, and the strength of the on-site interactions are usually described by on-site Coulomb parameters U and on-site exchange parameter J. Thus, for open-shell 3d or 4d transition metals, we used Hubbard U corrections24 for the localized d elections with U values for Ti, V, Cr, Mo, Mn, Fe, Ru, Co, Ni, and Cu of 2.0, 3.1, 3.5, 4.38, 4.0, 4.0, 4.5, 3.3, 6.4, and 4.0eV, respectively.25 The J values are set as zero. Spin ordering was considered by including spin polarization. The Cu dimers in Cu-BTC are known to be an antiferromagnetic.26 We compared antiferromagnetic and ferromagnetic ordering of Fe, Co, and Ni dimers in Fe-BTC, Co-BTC and Ni-BTC, respectively, and found that antiferromagnetic state is preferred. Therefore, locally antiferromagnetic ordering was imposed for each metal dimer and a high-spin electronic state was used for each metal ion.27 The initial optimized structure for Cu-BTC was taken from our previous work.5 We constructed initial structures of other M-BTC materials by replacing each Cu atom in Cu-BTC with the metal of interest. Each isostructural analogue was then energy minimized in the absence of adsorbed molecules. Due to qualitative differences in the local geometries of the metal centers that result from metal identity, many of these optimizations required further manual adjustment 3 ACS Paragon Plus Environment


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of metal—oxygen bond lengths and interionic distances to converge the systems to the desired energy-minimized structures. The ground-state spin alignments and local spin states of the metal dimers in these systems are highly sensitive to these parameters. After this initial geometry optimization, the cell shape and the volume were fixed in all subsequent calculations. Table S1 reports the lattice parameters, the distance between metals in dimers, the metal and oxygen distance, and the metal magnetic moment for each optimized M-BTCs. As might be expected, the lattice parameters correlate well with the ionic radii of the metal ions (see Figure S1). For MOFs based on other linkers, a similar procedure was used starting from experimentally reported structures for Cu-based materials.28 For MOFs with functionalized linkers, initial structures were generated by selecting hydrogen atoms of each linker in the primitive cell randomly and replacing them by the functional groups of interest. The functionalized structures were then optimized. During calculations of molecular binding, we allowed all atomic positions in the MOF and guest molecules to relax. The binding energies of adsorbed molecules were defined by ―∆𝐸 = 𝐸𝑎𝑑𝑠 + 𝑀𝑂𝐹 ― 𝐸𝑀𝑂𝐹 ― 𝐸𝑎𝑑𝑠

(1)

where 𝐸𝑎𝑑𝑠 + 𝑀𝑂𝐹, 𝐸𝑀𝑂𝐹,and 𝐸𝑎𝑑𝑠 represent the energy of a MOF with an adsorbed molecule, the energy of the empty MOF, and the energy of the adsorbate (guest molecule) in the gas phase, respectively. Zero-point energy and thermal contributions are neglected in this definition. With this convention, ∆𝐸 is positive when adsorption is exothermic. The initial orientation of guest molecules in CuBTC are taken from ref 12 for water, ref 6 for ethylene, ref 48 for carbon monoxide, ref 5 for ethane, and ref 5 for acetylene. In addition to these initial configurations, we explored a number of other initial structures for each molecule. Results are reported below only for the most stable state found from each set of calculations. Our calculations of binding energies in other M-BTC MOFs, used the same initial structures as in CuBTC. We noted in our earlier work that calculations that do not allow the MOF to be flexible can be useful in understanding the relative binding energies of pairs of molecules.5 Nevertheless, calculations that allow the MOF degrees of freedom to relax are necessary to accurately compute binding energies of individual species. For example, Lee et al.29 reported the contribution to the binding enthalpies of CO in M-MOF-74 due to relaxation of the MOF ranges from 1.2 kJ/mol (M=Zn) to 15.8 kJ/mol (M=Ti). Therefore, both MOFs and guest molecules are fully flexible during all the calculations reported below.

3. RESULTS AND DISCUSSION 3.1 Tuning Binding Affinity by Metal Substitution in Five Dimer OMS MOFs 3.1.1 Linker Species and Names for Five MOFs with OMS The systems we examined include five types of MOFs with open Cu sites and also materials in which the Cu center of each MOF is substituted by eleven different metals. All these MOFs have metal dimers formed by the same paddlewheel unit as Cu-BTC but differ in the linkers that connect these units. In addition to Cu-BTC, we examined UMCM-153,30 SNU-21,31 4 ACS Paragon Plus Environment

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PCN-16,32 and MCF-22,33 which have structure codes, ANUGOG, EPOTAF, NUTQAV, and XOPLOE, respectively. The linkers for these materials are shown in Figure 1, and structural details of each MOF are given in Table S2.

Figure 1. Linkers for the five MOFs used in our calculations. Oxygen, carbon, and hydrogen atoms are depicted in red, brown, and white colors, respectively.

3.1.2

Binding Energies of Adsorbed Molecules in Isostructural MOFs

As described above, we optimized the structure of each MOF with 12 different metals and then calculated the binding energies of H2O, CO, C2H2, C2H4, and C2H6 with fully flexible adsorbed molecules and frameworks. This gave a total of 300 distinct adsorbed species. Figure 2 and Table S3 shows the average and the range of binding energies of each molecule for the 12 different metal centers in these MOFs. Figure 2a shows the results for H2O and CO and the results for C2 hydrocarbons are shown in Figure 2b. The binding energies for every example are listed in Tables S4-S8. Figure 2 shows that, as might be expected for molecular binding at OMS, the variation in binding energies associated with changing the metal center is considerably stronger than variations due to the identity of the MOF linker. From the perspective of reversible binding for use in chemical separations, the binding energies vary widely among the metal centers. For H2O, CO, and C2H4, examples with binding energies from ~35-100, ~30-90, and ~40-80 kJ/mol can be seen. C2H6 has the lowest binding affinity in every case. Because of this relatively weak binding at the OMS, the C2H6 binding energies are more strongly influenced by dispersion interactions associated with linkers than the other molecular species and therefore show a wider range of binding energies among MOFs with a common metal center. Among the metals, the lowest binding energies are observed for Cr, Mo and Cu. A similar phenomena was also found in MMOF-7429, an observation attributed to the destabilization of Cr(d4), Mo(d4) and Cu(d9) metal centers.34 This destabilization is due to the s-like dz2 electronic orbitals of metal center that interact with s-like molecular orbitals of adsorbates, leading to σ* antibonding. Compared with binding energies in M-MOF-74 reported by Lee et al.,29 the energy differences between molecules are typically larger in the M-BTC materials. Although these two sets of calculations used different dispersion-corrected functionals, our previous work has shown that binding energy differences in these settings are relatively insensitive to the exchange-correlation functional, suggesting that this difference is not decisive. We speculate that the different coordination of metal sites in the two materials is the primary source of this difference. In Zn-MOF-74, for



example, Zn is in a quasi-square-pyramidal geometry, different than the square planar Zn in ZnBTC (only four basal oxygens linker to the Zn).

Figure 2. The average and the range of binding energies of (a) H2O and CO, (b) C2H2, C2H4, and C2H6 in five MOFs for 12 metal centers. For each molecule, the upper, middle, and lower symbols represent the maximum, average, and minimum binding energies in five MOFs, respectively. The horizontal axis increases from d0 to d10 for the metals. Metals marked with an asterisk (*) are from second-row transition metals.

3.1.3

Average Binding Energies Difference of Ten Pairs of Molecules with Twelve Metal Centers

In terms of adsorption selectivity, the difference between molecular binding energies of different species in a MOF are more important than the absolute binding energies.5

Figure 3 summarizes the binding energy differences for each of the ten binary pairs that can be

considered with the 5 molecules we examined. If one is interested in only binary separations, this figure captures the full range of separations that can be achieved with this set of materials. The 6 ACS Paragon Plus Environment

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variation among MOFs with a common metal center is small relative to the variation among different metal centers; more information on this is given in Table S9. The molecular pairs in Figure 3 are ordered vertically by the binding energy difference averaged over all 60 MOFs we considered. The top 4 pairs in the figure, H2O-C2H6, C2H4-C2H6, CO-C2H6, and C2H2-C2H6 may be viewed as “simple” separations to achieve in the sense that every MOF shows the same tendency to be selective for the first species listed. This outcome is not surprising given the inability of C2H6 to complex with OMS. A closer look, however, shows that significant differences in relative binding energies (and therefore selectivity) are possible by varying the metal center. For H2O-C2H6, for example, the binding energy difference is > 50 kJ/mol in Mg and Co MOFs but < 20 kJ/mol in Mo, Ru, and Cu MOFs. Similarly, for CO-C2H6, the binding energy difference is > 45 kJ/mol in Ti, Ru, and Co MOFs and < 10 kJ/mol in MOFs with Mg, Mo, or Cu OMS. The range of binary separations possible for molecular pairs in Figure 3 that do not include C2H6 is more complex. For every A-B binary separation of this kind, there is at least one metal center that is selective for A and at least one metal center that is selective for B. An extreme example is H2O-CO. For this pair, MOFs with Mg, Mn, or Ni centers having binding energies that favor H2O by more than 30 kJ/mol, a large enough energy difference to achieve an efficient separation. In MOFs with Ti or Ru metal centers, in contrast, the binding energies favor CO by more than 20 kJ/mol. This behavior can also be seen in Figure 2a. Another illustrative example is the binary mixture of H2O and C2H2. MOFs with Mg, V or Ni metal centers are seen to selectively bind H2O relative to C2H2 with binding energy differences of > 25 kJ/mol while MOFs with Ti, Fe, and Ru centers favor C2H2. These results confound simplistic descriptions of the binding energies of these molecules (e.g. “water always binds more favorably than acetylene”), but this diversity indicates that use of isostructural MOFs with a variety of metal centers has potential for achieving targeted separations outcomes. To consider separations involving more than two adsorbing components, it is useful to classify OMS in terms of the binding energy order for the entire group of molecules we studied. This concept is illustrated in Figure 3b, which shows there are five different orders of binding energies that are possible in the set of MOFs in our calculations. An expanded view of this figure with all 12 metals is shown in Figure S2. The most common situation is the one shown for Zn OMS in Figure 3, with binding energies in the order H2O > C2H4 > C2H2 > CO > C2H6. This ordering is shared by MOFs with Zn, Mg, Cr, Mo, Mn, Ni, and Cu OMS. A useful observation from our data, however, is that other orderings are possible. Two striking examples are MOFs with Ru and Ti OMS, for which our calculations predict that CO is the strongest binding species and H2O has similar or smaller binding energies to both unsaturated C2 hydrocarbons.



Figure 3. (a) Average binding energy differences of ten pairs of molecules for isostructural MOFs with 12 metal centers. Energy differences are color coded using the scale shown at the top of the figure. Metals are ordered horizontally the same as in Figure 2. Molecular pairs are ordered vertically in decreasing order of the energy difference averaged over all materials. (b) Five types of orderings of guest molecule binding energies. Co has the same ordering as V and Mg, Cr, Mo, Mn, Ni, and Cu have the same ordering as Zn.

The binding energy difference table in Figure 3a can answer a wide range of questions about possible separations of the five species we considered. With these five species, there are 26 different multicomponent feed streams that are possible (ten binary streams, ten ternary streams and so on as listed in Table S10). Figure 3a can rapidly indicate materials that could be useful for treating any of these streams. We illustrate this concept with two binary separations. First, we consider the possibility of removing acetylene from ethylene/acetylene mixtures. In most cases for this mixture, it would be desirable to adsorb the less concentrated acetylene and let ethylene pass through. Therefore, a material with higher binding energy of acetylene than ethylene is required. In

Figure 3a, Ti OMS MOFs stand out because the binding energy of C2H2 is 8 kJ/mol higher than

that of C2H4. A second example is the separation of ethylene from ethylene/ethane mixtures after gas cracking.35 The row for C2H4-C2H6 in


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Figure 3a highlights the MOFs with stronger separations in blue.

A less straightforward example is to consider how to purify a stream that initially contains all five components. Figure 3a can be used to define a sequence of separation steps suitable for this multicomponent mixture. Three examples are shown in Figure 4. In each example, each filled box indicates a packed bed or similar unit operation containing an OMS MOF with the metal center indicated. If H2O is the dominant contaminant, two possible separation sequences are shown in Figure 4a and 4b. Both sequences first use a Mg OMS MOF to remove water. This step could of course also potentially be achieved using well known hydrophilic adsorbents such as zeolite 13X. Figure 4a and 4b then show two alternative separation strategies to purify the remaining four components. Not all these steps would be necessary if, for example, non-ethylene species were recycled to a reactor. If CO is the dominant contaminant, the alternative strategy shown in Figure 4c could be used. These separation strategies illustrated in Figure 4 are of course highly schematic, but they illustrate how the information from Figure 3 can be used to select suitable adsorbents for a wide range of multicomponent separations. We emphasize that our approach is only intended to indicate the order in which components would appear in a breakthrough experiment. There are of course more detailed approaches to modeling specifics of breakthrough profiles. The predictions of our simplified approach are consistent with breakthrough experiments2c for separation of C2H2, C2H4, and C2H6 in CuBTC and models of breakthrough based on ideal adsorbed solution theory36 for C2H4/C2H6 in CuBTC.



Figure 4. Separation strategies for an inlet mixture containing five components. Each box indicated a packed bed filled with an OMS MOF. The red components represent the output of desired products at each step. The black components are the input of next separation column.

The discussion above focused on binding energy differences to characterize the capability of different MOFs for molecular separations. Although these energy differences control the selectivity of adsorption, consideration of materials for practical processes must involve multiple factors.37 Some important practical factors such as the long-term stability of materials cannot readily be addressed by the calculations we have reported. We can, however, comment on the capacity and regenerability of the MOFs we considered. All our calculations have examined molecules bound to OMS in MOFs, and our results are therefore relevant to situations corresponding to equilibrium adsorption that fills or partially fills these sites but does not substantially fill the less selective regions of each MOFs pores. Among the metal centers we studied, the volumetric capacity (i.e. OMS/unit volume) is essentially identical for each isostructural series and varies only moderately among MOFs with different linkers. The capacity is therefore approximately the same for all the materials we studied. A prototypical example is Cu-BTC, which has a capacity of 4.36 mmol/cc for adsorption at OMS assuming that a single molecule binds to each metal site. The regenerability of adsorption can be gauged from the absolute binding energy of the strongest binding species in a mixture. Very strongly binding species can necessitate aggressive desorption conditions to avoid accumulation of these species over multiple cycles, and these conditions can worsen the operating cost for the process or have negative effects on the adsorbent material.38 In general, there is a tradeoff between finding materials with large binding energy differences and finding materials with moderate absolute binding energies, simply 10 ACS Paragon Plus Environment

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because it is easier to have a large binding energy difference if one species binds very strongly. This tradeoff is illustrated in Figure 5 for ethylene-ethane mixtures. Similar data for all possible binary mixtures among the species we considered is shown in Figure 6. This figure uses a slightly different definition of the binding energy difference than in Figure 3; in Figure 6 the absolute value of the binding energy difference is used. One observation from Figure 6 is that the energy differences are always at least ~20 kJ/mol smaller than the binding energy of the more favorably bound species. It would be interesting to know if this tradeoff also exists from a broader range of molecular species and at other kinds of OMS in MOFs.

Figure 5. The difference of binding energies between ethylene and ethane as a function of the average binding energy of ethylene in MOFs with 12 metal centers. The variation in energies among the linkers in Figure 1 are indicated by error bars.

Figure 6. The binding energy difference for ten pairs of molecules as a function of the binding energy of most favorably bound species. Each data point represents the average of results for a single metal OMS among the MOFs described in Figure 1.



3.2 Tuning the Binding Affinity by Linker Functionalization Among all the materials examined above, Zn OMS MOFs have the highest predicted C2H4/C2H6 selectivity. At least 30 MOFs with Zn dimers are reported in CoRE MOF database.28 Although a number of these materials exhibit lower than expected surface areas or complete collapse after removal of solvent,39 one material, DUT-10-Zn40 stands out because it adsorbs CO2 and exhibited selective gas sorption properties for H2 and CO2 over N2. Our results above indicate that Zn-BTC has very high binding energies with water. We further performed DFT calculations examining the binding of DMF in Zn-BTC and DUT-10-Zn and obtained binding energies of 114 and 97 kJ/mol, respectively. These results hint at the challenges associated with fully activating these Zn-based MOFs and the potential challenges associated with water contaminants in feed streams. This raises the question of whether it is possible to tune the binding affinity via linker functionalization. In this section, we test a strategy to vary the binding energy of Zn sites by functionalizing the MOF linker with electron donating or withdrawing groups. This idea has of course been considered previously. For example, Burtch et al.41 studied how the polar (nitro, fluorine and chlorine) and non-polar (methyl) functional groups affected CO2 affinity in DMOF-1 and Cai et al.42 showed functionalized ligands (methyl, ethyl, methoxy, bromo, nitro, and acetamide) can affect the crystal and electronic structure in Cu-BTC derivatives. Since some functionalized benzenetricarboxylic (X-BTC, X=1F, 1CH3) ligands are commercially available, we decided to study the functionalization of BTC linkers. Figure 7 shows the structure of all the functionalized ligands we used. Although ligand functionalization can in same cases change the crystal structure that is favored,42 we assumed in our calculations that all of the ligands in Figure 7 formed MOFs with the same crystal structure as Cu-BTC.

Figure 7. Functionalized benzenetricarboxylic (X-BTC) ligands used in this study

We hypothesized that fluorine can increase the binding energies of guest molecules in Cu-BTC since fluorine is an electron withdrawing group, while methyl, an electron donating group, will decrease the binding energies. Figure 8 shows how fluorine affects the binding energies in M-BTCs. In examples where the linkers are only partially functionalized, our calculations examined OMS that were close to the functional groups to examine the impact of these groups. In a complete characterization of binding energies in these materials, some heterogeneity could exist because of the spatial arrangement of functional groups. Figure 8a shows that fluorine groups increase the binding energies at Zn OMS relative to the unfunctionalized material. More fluorine leads to stronger binding energies, although this effect is not linear in the number of fluorine groups. The binding energies are increased by at most 10 12 ACS Paragon Plus Environment

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kJ/mol higher by functionalization with fluorine. As would be expected, the polar molecules (H2O, CO) and unsaturated hydrocarbons (C2H2, C2H4) have larger energy increases associated with functionalization than nonpolar C2H6. To probe this effect, it is useful to examine the dispersion and non-dispersion contributions to the binding energies in Figure 8a as shown in Figure S3. The fluorine groups increase both the dispersion energy and non-dispersion energy of Zn OMS. To demonstrate that these outcomes are not unique to Zn, we performed similar calculations for 1F-Mg-BTC, 1F-Cr-BTC and 1F-Cu-BTC. Figure 8b shows the fluorine group increases the binding energies of all five molecules in these materials by up to 7 kJ/mol.

Figure 8. The influence of fluorine groups on the binding energies of five guest molecules as a function of the binding energy in the unfunctionalized MOF in (a) 3F-, 2F-, and 1F-Zn-BTC and (b) 1F-M-BTCs (M=Mg, Zn, Cr, Cu).

To assess the impact of electron-donating methyl groups may decrease the binding, we functionalized Zn-BTC with one methyl group on each ligand and computed the binding energies in 1CH3-Zn-BTC. The resulting binding energies are shown in Figure 9, which confirms that the methyl functionalized material has lower binding energies. Figure 9 also includes results for binding of DMF, a common solvent used in synthesis of MOFs. The decreased binding energy of DMF in the methyl functionalized materials suggests this approach may be useful in synthesizing materials that are easier to activate than the unfunctionalized MOF. The dispersion and non-dispersion contributions to the binding energies of four molecules (H2O, CO, C2H4, and C2H6) in Zn-BTC, 1CH3-Zn-BTC, and 1F-Zn-BTC are shown in Figure S4. Both fluorine and methyl groups increase the dispersion energy of Zn OMS, but fluorine increases the non-dispersion energy while the methyl group significantly decreases this energy. As a result, the methyl group decreases the overall binding energy relative to the unfunctionalized material.



Figure 9. The influence of a methyl group on the MOF ligand on binding energies of dimethylformamide (DMF), H2O, CO, C2H2, C2H4, and C2H6 in Zn-BTC.

4. SUMMARY We have used DFT calculations to investigate the influence of metal species forming open metal sites in MOFs on molecular binding energies by establishing the relative binding energies of five small molecules (H2O, CO, C2H2, C2H4 and C2H6) on a large collection of isostructural OMS MOFs. We first considered the impact of metal substitution in one specific material (CuBTC) in detail by examining twelve metal-substituted M-BTCs (M = Mg, Ti, V, Cr, Mo, Mn, Fe, Ru, Co, Ni, Cu, and Zn). We then performed similar calculations for four additional experimentally accessible MOFs with dimeric Cu OMS. Our results show that the variation in binding energies associated with changing the metal center is considerably stronger than variations due to the identity of the MOF linker. Since the differences between molecular binding energies of different molecules in a MOF are more important than the absolute binding energies, we summarized the binding energy differences for ten binary pairs of molecules with twelve metal centers. The diversity of these results indicates that use of isostructural MOFs with a variety of metal centers has potential for achieving targeted separations outcomes. We gave examples of how information on the properties of wide range of OMS MOFs can be used to suggest efficient separations strategies for multicomponent mixtures. Our results support that idea that there is a tradeoff between finding materials with large binding energy differences and finding materials with moderate absolute binding energies. One implication of the tradeoff just mentioned is that the materials with the highest selectivities for separations of interest (for example, ethylene/ethane) also tend to have strong binding energies. This may cause difficulties in regenerating sorbents or even in removing solvents from the materials when they are initially synthesized. In these instances, it is useful to understand whether molecular binding energies can be systematically changed using linker functionalization in MOFs. We tested a strategy of functionalizing BTC linkers with electron withdrawing groups (specifically, fluorine) and electron donating groups (specifically, methyl groups). Our calculations show that fluorine can increase the binding energies of guest 14 ACS Paragon Plus Environment

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molecules relative to unfunctionalized linkers, while methyl group can decrease the binding energies. These effects may be useful in creating materials with desired combinations of absolute and relative binding energies for chemical mixtures in practical settings.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]

ACKNOWLEDGEMENTS This work was supported by the Dow Chemical Company. YL and JDH were supported by the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Contract DE-SC0012577. DT was supported by the Nanoporous Materials Genome Center, funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award #DE-FG02-17ER16362, as part of the Computational Chemical Sciences Program.

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