Quantum Sieving in MetalâOrganic Frameworks: A Computational

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Quantum Sieving in MetalOrganic Frameworks: A Computational Study Dahuan Liu,* Wenjie Wang, Jianguo Mi, Chongli Zhong, Qingyuan Yang, and Dong Wu State Key Laboratory of OrganicInorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China

bS Supporting Information ABSTRACT: In this work, a systematic computational study was performed to investigate the quantum sieving in nine typical metalorganic frameworks (MOFs) for the separation of hydrogen isotope mixtures. The results show that Cu(F-pymo)2 and CPL-1 exhibit exceptional selectivity that is higher than other MOFs as well as other nanoporous materials such as carbon nanotubes, slit-shaped graphites, and zeolites studied so far. A concept named “quantum effective pore size” (QEPS) was proposed in this work, which can incorporate the effects of quantum sieving, and thus is temperature-dependent. On the basis of the new pore size, good correlations between pore size and selectivity can be established for the MOFs considered; particularly, they can explain the different selectivity performance of the two MOFs with highest selectivity at 40 and 77 K. This work indicates that MOFs are suitable candidates for the separation of hydrogen isotopes through quantum sieving.

1. INTRODUCTION Deuterium and tritium, as the heavier isotopes of hydrogen, are useful resources and have numerous applications, such as in hydrogen nuclear magnetic resonance spectroscopy, nuclear fusion, and lighting.1 Currently, the production of these isotopes of hydrogen is mainly via various isotope separation techniques including centrifugal enrichment and electromagnetic mass spectrometry. Recently, the quantum effect of hydrogen at low temperature has attracted increasing attention, and this effect could be emphasized by considering adsorption in confining space, which can be denoted as the “quantum sieving”. Beenakker et al. first proposed that quantum sieving can be used to separate the hydrogen isotope mixtures in porous solids with narrow channels.2 Following this pioneering work, many investigations have been performed1,319 and showed that this peculiar phenomenon in nanoporous materials could be considered as one of the alternative separation technologies for the separation of hydrogen isotope mixtures. During the past decade, a new family of nanoporous materials, metalorganic frameworks (MOFs), have been shown to exhibit many unique advantages over other traditional adsorbents such as carbon nanotubes and zeolites, due to their fine-tunable pore structures and adjustable chemical functionality.2023 In addition, MOF materials have also shown very promising applications for gas separation from an industrial point of view.2426 However, up to now, there are few investigations of the separation of hydrogen isotope mixtures in MOF type materials based on the quantum sieving effect. For example, Noguchi et al. studied the separation of H2 and D2 in CuBOTf using both the experimental and the simulation methods, giving direct evidence of the molecular quantum sieving for adsorption on this material.27 Garberoglio reported, for the first time, full quantum mechanical calculations of the isotopic separation factors for T2/H2 and D2/ H2 mixtures in MOFs and covalent organic frameworks (COFs) via path integral Monte Carlo simulations.28 Because most MOFs have complex structures and there are few investigations so far, r 2011 American Chemical Society

the understanding of quantum sieving behavior in MOFs and the relationship between structure and separation performance are still not very clear and deserve further study. Therefore, in this work, a computational study was performed to examine the separation performance of nine typical MOFs with channel-type structure for hydrogen isotope mixtures. The structureselectivity relationships in MOFs obtained here are expected to provide useful information for the design of new materials for such applications.

2. SIMULATION METHODS 2.1. MOF Structures. In this work, nine typical MOF materials with channel structure were selected, considering that their small channel diameters are expected to induce a large quantum effect, and the relatively well-defined channel structure makes it easier to determine the relationship between structure and separation performance for MOFs. The structures of these MOFs were constructed from the X-ray diffraction (XRD) data using Materials Studio Visualizer,29 as shown in Figure 1, and the structural properties of these materials are summarized in Table 1.3038 It should be noted that the pore sizes in Table 1 are calculated with the method developed by Gubbins et al.,39 which was defined as the largest sphere fitted inside the pore cavity without overlapping the neighboring wall atoms. 2.2. Force Fields. In this study, H2, D2, and T2 were all treated as the diatomic molecules and modeled by one Lennard-Jones (LJ) core located at its center of mass (COM). In addition, to account for the Coulombic interactions, two partial charges (q = 0.468e) were located at H (or D or T) atoms and one (q = 0.936e) at the COM. The bond length between the two Received: April 2, 2011 Accepted: December 1, 2011 Revised: November 18, 2011 Published: December 01, 2011 434

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Figure 1. Crystal structures of the MOFs used in the simulation: (a) Cu(F-pymo)2, (b) CPL-1, (c) Cu-MMOM, (d) Ni2(4,40 -bipy)3(NO3)4, (e) Mn-MOF, (f) CuBOTf, (g) MOF-2, (h) Zn(dtp), and (i) Zn-PCPs.

Table 1. Structural Properties of the MOFs Studied in This Work space groupa

materials

a

unit cella (Å)

cell anglea (deg)

dporeb (Å)

Vfreec (cm3/g)

Saccc(m2/g)

ref

Cu(F-pymo)2

I41md

a = b = 20.824, c = 10.001

α = β = γ = 90

2.9

0.21

11

30

CPL-1

P21/c

a = 4.710, b = 20.029, c = 10.770

α = γ = 90, β = 95.47

3.0

0.20

19

31 32

Cu-MMOM

P2/n

a = 18.723, b = 7.271, c = 20.481

α = γ = 90, β = 107.02

4.7

0.23

167

Ni2(4,40 -bipy)3(NO3)4

Ccca

a = 12.156, b = 18.891, c = 17.584

α = β = γ = 90

4.1

0.38

362

33

Zn(dtp)

P61

a = b = 14.163, c = 11.568

α = γ = 90, β = 120

6.2

0.38

887

34 35

MOF-2

P21/n

a = 6.718, b = 15.488, c = 12.430

α = γ = 90, β = 102.83

6.0

0.48

1409

CuBOTf

I41/acd

a = b = 28.484, c = 18.219

α = β = γ = 90

5.7

0.43

977

36

Mn-MOF Zn-PCPs

P21/n I4/m

a = 11.720, b = 10.207, c = 14.956 a = b = 15.348, c = 18.938

α = γ = 90, β = 91.44 α = β = γ = 90

4.9 5.0

0.32 0.66

799 1676

37 38

Obtained from the XRD crystal data.3038 b Calculated with the method developed by Gubbins et al.39 c Calculated with the Materials Studio package.

Table 2. LJ Potential Parameters for H2, D2, T2, and the MOFs Used in This Work LJ parameters

a

H2 (D2,T2)

MOF_O

MOF_C

MOF_H

MOF_N

MOF_Fa

MOF_Cua

MOF_Zn

MOF_Nia

MOF_Mna

MOF_Sa

σ (Å)

2.96

3.03

3.47

2.85

3.26

3.00

3.11

4.04

2.53

2.64

3.59

ε/kB

34.2

48.16

47.86

7.65

38.95

25.17

2.517

27.68

7.55

6.54

137.9

Taken from the UFF force field57 (they were missed in the Dreiding force field).

adsorbates and adsorbents. The LJ parameters for the framework atoms in MOFs were taken from Dreiding force field.41 The above set of force fields has been successfully used to describe the adsorption and separation of H2, D2, and T2 in various MOFs.28,42 The potential parameters used in this work are listed in Table 2. In this

atoms in each species is 0.74 Å. The LJ parameters for H2, D2, and T2 molecules were adopted from the values suggested by Buch.40 All of the MOFs studied here were modeled by the atomistic representation. A combination of the sitesite LJ and Coulombic potentials was adopted to calculate the interactions between 435

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Figure 2. Comparison of (a) simulated and experimental adsorption isotherms of H2 in MOF-563 and Cu-BTC64 at 77 K; (b) simulated and experimental27 adsorption isotherms of H2 and D2 in Cu-BOTf at 40 K; and (c) simulated and IAST predicted27 selectivities of D2 in the equimolar D2/H2 mixture at 40 K.

Figure 3. Selectivity for a 50:50 D2/H2 mixture: (a) 40 K, (b) 77 K.

Figure 4. Snapshots of CPL-1 with adsorbed D2 and H2 at 40 K and 0.001 MPa: (a) the perspective view down from the a axis; and (b,c) views down from the b axis and c axis, respectively (D2, green; H2, white).

these DFT calculations.51 This common method for charge calculation has been successfully performed in the studies of many MOFs.46,5254,61 All of the LJ cross-interaction potential parameters were determined by the LorentzBerthelot mixing rules. Considering that the separation of hydrogen isotope mixtures usually occurred at low temperature, the quartic Feynman Hibbs (FH) effective potential55 was used to account for the quantum effects: βp2 00 2U 0 ðrÞ U ðrÞ þ UFH ðrÞ ¼ UðrÞ þ r 24μ β2 p4 15U 0 ðrÞ 4U 000 ðrÞ 0000 þ U þ þ ðrÞ r3 r 1152μ2

work, the electrostatic charges were used as the atomic partial charges, and the ChelpG method was adopted, which has been recognized as one of the most popular and reliable electrostatic charge calculation methods.4346 The reliability of the ChelpG was also justified by Martin and Zipse in their work on comparison of methods for charge distribution calculation of water molecules.47 Density functional theory (DFT) calculations were carried out on the model clusters of all of the MOFs based on the unrestricted B3LYP functional.48,49 The basis set LANL2DZ was used for metal atoms, while 6-31+G* was used for the remaining atoms. For heavy atoms, effective core potential (ECP) is often chosen in ab initio calculations to reduce the amount of necessary computation, and LANL2DZ is a collection of double-ζ basis sets, which is one of the most common ECP basis sets for complexes involving transition metal elements.50 The Gaussian 03 soft package was used to run

ð1Þ 436



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Figure 7. Selectivity of D2 over H2 as a function of the conventional pore size of MOFs at 0.001 MPa.

where U denotes the classical LJ potential, r is the molecule molecule distance, p is Planck’s constant divided by 2π, and the prime, double prime, etc., denote the first, second, and higher order derivatives with respect to r, respectively. The second and third terms in eq 1 are the quantum correction, μ is the reduced mass: μ = m/2 for the adsorbateadsorbate interactions, while μ = m for the adsorbateadsorbent interactions, where m is mass of the adsorbate molecule. Previous studies have indicated that FH effective potential with the quadratic form56 is not accurate enough to model the fluids confined in adsorbents with the small pores, and thus higher forms were adopted in this work. 2.3. Simulation Method. Grand canonical Monte Carlo (GCMC) simulations were employed to calculate the adsorption

of H2, D2, T2, and their mixtures in MOFs. Similar to the previous works,5861 all of these MOF materials were treated as rigid frameworks with atoms frozen at their crystallographic positions, because the effects of the dynamics of MOFs become significant only when the guests are large and/or strong guesthost interactions exist in the system. For the simulations of pure components, molecules involved three types of trials: (i) to displace a molecule (translation or rotation), (ii) to create a new molecule, and (iii) to delete an existing molecule. For the simulations of mixture, an attempt to exchange molecular identity was introduced as an additional type of trial to speed up the equilibrium and reduce statistical errors. The sizes of the simulation boxes were adopted on the basis of the unit cell parameters of different 437



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data using ideal adsorption solution theory (IAST), as shown in Figure 2c. Considering the intrinsic assumptions in IAST, the agreement between the two predictions can be thought to be fine, particularly at lower pressures. On the basis of these observations as well as those in previous works,28,42 it can be concluded that the set of force fields adopted in this work are reliable that can be used to study the adsorption behaviors of hydrogen isotope mixtures in MOFs. 3.2. Selectivity of D2/H2 Mixture in MOFs. In this work, nine MOFs were collected from the literature with channel-type pore structure. A systematic computational study based on these typical MOFs with relatively well-defined structures allows us to figure out the relationship between structure and separation performance for MOFs, as well as to make a comparison between the separation performances of hydrogen isotope mixtures in MOFs with those in other traditional porous materials. The molar ratio of the two components in the D2/H2 mixture was taken as 50:50, and the simulations were performed at 40 and 77 K to be consistent with the previous work.27 CuBOTf was selected as a comparison, because this is the only MOF for which both the experimental and the simulation data are available,27 to the best of our knowledge. The adsorption selectivities for D2 from the D2/H2 mixture are shown in Figure 3, as a function of the bulk pressure up to 1.0 MPa. In all of the MOFs, D2 is more preferentially adsorbed than H2. It is due to the fact that the size parameter of the lighter H2 with larger zero-point energy2 is increased larger than that of the heavier D2 when the quantum effects are considered at low temperature,55 inducing the unfavored adsorption of H2 as compared to D2. It is obvious that the selectivities at 40 K are generally greater than those at 77 K; this is consistent with the previous observations in other nanoporous materials such as single-walled carbon and boronnitride nanotubes,12 the slit-shaped graphites,12 as well as zeolites,9 that the lower the temperature, the more evident of the quantum effects for gas adsorption.65 The results in Figure 3 demonstrate that most MOFs studied here show larger selectivity at 40 K than the two MOFs studied previously, CuBOTf and Zn-TBIP, for the D2/H2 separation.27,28 Particularly, Cu(F-pymo)2 and CPL-1 show highest selectivity for D2/H2 separation, which is larger than those in other nanoporous materials at the same conditions considered in this work, such as 15.0 in carbon slit-pore nanoporous materials at 40 K.1 For Cu(F-pymo)2, the selectivity increases with the increase of the pressure at both temperatures. At 40 K, the selectivity remains almost unchanged as the pressure is higher than 0.01 MPa, which resulted from the fact that the adsorptions of D2 and H2 almost reach saturation under these conditions. In the case of CPL-1, the variation of the D2/H2 selectivity upon the pressure consists of two stages at 40 K: a nearly constant stage up to 0.001 MPa and an increase stage with further increasing pressure (Figure 3a). To give insight into molecular-level details of the adsorption behavior of this gas mixture, the snapshots for the structures of CPL-1 with the adsorbed H2 and D2 are examined at 0.001 MPa as shown in Figure 4. Interestingly, it was observed that the H2 and D2 molecules locate in the middle of the channels to align two rows along the channels, which is similar to the O2 molecules adsorbed in CPL-166 while different from the C2H2 adsorbed in CPL-1.67 The adsorption sites are close to the CuO clusters in the frameworks. In each row, H2 and D2 molecules are packed randomly with a short intermolecular distance, and each of the molecules is pointed to the oxygen atom of the CuO clusters, which is different from the situation of O2.66

Figure 8. The classical LJ potential () and the modified quartic FH potential for H2 and C in the frameworks at 40 K ( ) and 77 K ( 3 3 3 ).

MOFs, and no finite-size effects existed by checking the simulations with larger boxes. The LJ interactions were calculated with a cutoff distance, 12.8 Å, and the long-range electrostatic interactions were handled using the Ewald summation technique with periodic boundary condition. Periodic boundary conditions were applied in all three dimensions. To speed the computational efficiency, the potential energies between the adsorbate/ adsorbent interactions were initially tabulated on a series of 3threedimensional grid points with grid spacing 0.15 Å. During the simulations, the potential energy at any position in the adsorbent was determined by interpolation. According to the fluctuation theory, the heat of adsorption Qst was directly calculated from Qst ¼ RT

ÆUff Næ ÆUff æÆNæ ÆUsf Næ ÆUsf æÆNæ ÆN 2 æ ÆNæÆNæ ÆN 2 æ ÆNæÆNæ

ð2Þ

where R is the gas constant, N is the number of molecules adsorbed, and Æ æ indicates the ensemble average. The first and second terms are the contributions from the molecular thermal energy and adsorbateadsorbate interaction energy Uff, respectively. The third term is the contribution from the adsorbent adsorbate interaction energy, Usf. For each state point, the number of steps in GCMC simulation was 2 107, where the first 107 steps were used for equilibration and the subsequent 107 steps were used for sampling the desired thermodynamics properties. A detailed description of the simulation methods can be found in the Supporting Information and in ref 62. The selectivity for component A relative to component B is defined by S = (xA/xB)(yB/yA), where xA and xB are the mole fractions of components A and B in the absorbed phase and yA and yB are the mole fractions of components A and B in the bulk phase, respectively.

3. RESULTS AND DISCUSSION 3.1. Validation of the Force Field. To further confirm the reliability of the set of force fields adopted in this work, the adsorptions of H2 in very well-known MOFs such as Cu-BTC and MOF-5, as well as the adsorptions of H2 and D2 in CuBOTf at 40 K, were simulated. The results shown in Figure 2a and b indicate that the simulated isotherms of H2 and D2 are in good agreement with experimental data27,63,64 over the whole pressure range. In addition, the selectivities of D2 from the equimolar D2/H2 mixtures at 40 K were calculated as a function of pressure and compared to the results predicted from the experimental 438



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Figure 9. Selectivity of D2 over H2 as a function of the quantum effective pore size of MOFs at 0.001 MPa.

Figure 10. (a) Contour plot of adsorption selectivities of D2 over H2 at 0.001 MPa and 40 K in Cu-MMOM (“b” are the windows between the adjacent cages). (b) Contour plot of adsorption selectivities of D2 over H2 at 0.001 MPa and 77 K in Cu-MMOM (“b” are the cages). (c) Views from the b axis corresponding to the image in (a) and (b). (d) Views from the c axis.

When the pressure is above 105 MPa, the number of adsorbate becomes nearly equal to the number of CuO clusters in the frameworks, similar to the saturated amount of adsorption of 1.0 O2 molecules per copper atom in CPL-1.66 Under this condition, the number of D2 and H2 changes a little up to 0.001 MPa, and the selectivity remains constant. With further increasing pressure, the one-dimensional array of D2 and H2 becomes more dense due to the small molecule sizes as compared to O2 molecules. The quantum effect induces that the number of adsorbed D2 molecules increases and the number of H2 molecules decreases and then the selectivity increases (Figure 5), leading to

the second stage in Figure 3a. However, the number of adsorbed D2 and H2 is small at 77 K, and the one-dimensional arrays do not form (Figure 6), leading to the small change in the selectivity as a function of pressure (Figure 3b). 3.3. Relationship between Selectivity and the Structure of MOFs. The relationships between the adsorption selectivity of hydrogen isotopes and the pore width of carbon nanotubes and the slit-shaped graphite have been addressed previously,3,12 and the general trend is that the selectivity of the heavier isotope over the lighter one increases with the decrease of the pore width. It is necessary to investigate whether MOFs follow similar relationships. 439



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Figure 11. Selectivity of T2 over H2 (a,c) and T2 over D2 (b,d) as a function of the quantum effective pore size of MOFs at 0.001 MPa.

The selectivitypore size plots at both 40 and 77 K were drawn at 0.001 MPa, as shown in Figure 7. Obviously, a similar trend was observed in the MOFs at 40 K, while at 77 K, the order for the two MOFs with high selectivity was different. To reveal this observation, further discussions should be performed. As is known, the quantum effect at low temperature could swell up the adsorbateadsorbent potential size parameters, and such an influence gradually becomes negligible with the increase of temperature.55 For instance, as compared to the classical cross LJ size parameter between H2 and the C atoms in the framework of MOFs (σH2C), the value of this parameter increases by 4% and 2% at 40 and 77 K, respectively, as shown in Figure 8. Therefore, here we introduce a new concept named “quantum effective pore size” (QEPS), that is, an effective pore size calculated by considering the swelling of the adsorbateadsorbent potential size parameters caused by quantum effects. The change of cross LJ size parameter σij can be obtained from the plots of modified quartic FH potential using eq 1 (Figure 8), and then the effective LJ size parameters of the atoms in frameworks can be calculated using the LorentzBerthelot mixing rules. Next, the QEPS can be obtained with the effective LJ size parameters using the method developed by Gubbins et al.39 SD2/H2 as a function of the quantum effective pore size of the MOFs studied in this work is shown in Figure 9 at 0.001 MPa. By correlating with the QEPS, consistent trends were observed at both 40 and 77 K. One interesting thing is that at 77 K the QEPS of CPL-1 becomes smaller than that of Cu(F-pymo)2, leading to larger selectivity in contrast to that at 40 K; this also help to explain the separation performance of the two MOFs in Figure 3. On the other hand, if conventional pore size is adopted, it is difficult to explain the phenomena observed; this highlights the usefulness of the new concept in studying quantum sieving in MOFs.

From Figure 9a, it seems there is an outlier, that is, CuMMOM, which does not fit well with the curve correctly at 40 K; the reason can be attributed to the specialty in its structure: CuMMOM has cage pores to form the channels (QEPS: ca. 4.7 Å) connected by small windows (QEPS: ca. 3.3 Å). To find out the reasons for this phenomenon, the contour plot of selectivity at 40 K and 0.001 MPa is shown in Figure 10a, indicating that highest selectivity occurs in the window areas between the adjacent cages. Therefore, in this case, the QEPS should be 3.3 Å and not the 4.7 Å of the cage areas. When we use the QEPS of 3.3 Å at 40 K (blue point), it fits well with the curve. On the other hand, at 77 K, H2 and D2 are mainly adsorbed in the cages (Figure 10b), and thus the QEPS of 4.7 Å should be used at 77 K. Moreover, this special topology does not exist in other MOFs studied in this work, leading to the outlier at 40 K in Figure 9. Furthermore, we also investigated the separations of T2/H2 and T2/D2 mixtures to validate whether the new concept is reliable. The results in Figure 11 show that similar behaviors were observed, further confirming the usefulness of the new concept. The simulation results show that, as compared to the nanoporous materials with well-defined structure such as nanotubes, the complex structures make MOFs show more complicated selectivity behavior for hydrogen isotopes mixtures, and the irregular small areas formed by the frameworks, including corners, windows, etc., may enhance the quantum effects and thus the selectivity. Therefore, MOFs are promising candidates for the separation of isotope hydrogen through quantum sieving.

4. CONCLUSIONS This work shows that MOFs are promising materials for quantum sieving-based separation of hydrogen isotope mixtures and identified two MOFs with exceptional selectivity for D2/H2 separation. On the basis of the simulation results for the nine 440



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channel-type MOFs, it seems quantum sieving-based selectivity may not correlate well with the conventional pore size. To solve this problem, a new concept, quantum effective pore size, that can include the quantum sieving effects was proposed. The results show that the separation selectivities of D2/H2, T2/H2, and T2/D2 mixtures all correlate well with this parameter, and it can also be used to explain the selectivity behaviors at different temperatures, indicating the new parameter should be useful in the future study of quantum sieving-based technologies. In addition, this work shows that the various small areas formed by the framework can enhance quantum sieving efficiency, which is a characteristic of MOF materials.

(11) Garberoglio, G.; Deklavon, M. M.; Johnson, J. K. Quantum Sieving in Single-Walled Carbon Nanotubes: Effect of Interaction Potential and Rotational-Translational Coupling. J. Phys. Chem. B 2006, 110, 1733. (12) Hattori, Y.; Tanaka, H.; Okino, F.; Touhara, H.; Nakahigashi, Y.; Utsumi, S.; Kanoh, H.; Kaneko, K. Quantum Sieving Effect of Modified Activated Carbon Fibers on H2 and D2 Adsorption at 20 K. J. Phys. Chem. B 2006, 110, 9764. (13) Kowalczyk, P.; Gauden, P. A.; Terzyk, A. P. Cryogenic Separation of Hydrogen Isotopes in Single-Walled Carbon and Boron-Nitride Nanotubes: Insight into the Mechanism of Equilibrium Quantum Sieving in Quasi-One-Dimensional Pores. J. Phys. Chem. B 2008, 112, 8275. (14) Kumar, A. V. A.; Bhatia, S. K. Is Kinetic Molecular Sieving of Hydrogen Isotopes Feasible? J. Phys. Chem. C 2008, 112, 11421. (15) Garberoglio, G. Boltzmann Bias Grand Canonical Monte Carlo. J. Chem. Phys. 2008, 128, 134109. (16) Garberoglio, G. Quantum States of Rigid Linear Rotors Confined in A Slit Pore: Quantum Sieving of Hydrogen Isotopes and Extreme One Dimensional Confinement. Eur. Phys. J. D 2009, 51, 185. (17) Kowalczyk, P.; Gauden, P. A.; Terzyk, A. P.; Furmaniak, S. Impact of the Carbon Pore Size and Topology on the Equilibrium Quantum Sieving of Hydrogen Isotopes at Zero Coverage and Finite Pressures. J. Phys.: Condens. Matter 2009, 21, 144210. (18) Wang, Y.; Bhatia, S. K. Simulation of Quantum Separation of Binary Hydrogen Isotope Mixtures in Carbon Slit Pores. Mol. Simul. 2009, 35, 162. (19) Garberoglio, G.; Johnson, J. K. Hydrogen Isotope Separation in Carbon Nanotubes: Calculation of Coupled Rotational and Translational States at High Densities. ACS Nano 2010, 4, 1703. (20) Kitagawa, S.; Kitaura, R.; Noro, S. Functional Porous Coordination Polymers. Angew. Chem., Int. Ed. 2004, 43, 2334. (21) Rowsell, J. L. C.; Yaghi, O. M. MetalOrganic Frameworks: A New Class of Porous Materials. Microporous Mesoporous Mater. 2004, 73, 3. (22) Ferey, G. Hybrid Porous Solids: Past, Present, Future. Chem. Soc. Rev. 2008, 37, 191. (23) Long, J. R.; Yaghi, O. M. The Pervasive Chemistry of Metal Organic Frameworks. Chem. Soc. Rev. 2009, 38, 1213. (24) Mueller, U.; Schubert, M.; Teich, F.; Puetter, H.; SchierleArndt, K.; Pastre, J. MetalOrganic Frameworks-Prospective Industrial Applications. J. Mater. Chem. 2006, 16, 626. (25) Czaja, A. U.; Trukhan, N.; Muller, U. Industrial Applications of MetalOrganic Frameworks. Chem. Soc. Rev. 2009, 38, 1284. (26) Li, J.-R.; Kuppler, R. J.; Zhou, H.-C. Selective Gas Adsorption and Separation in MetalOrganic Frameworks. Chem. Soc. Rev. 2009, 38, 1477. (27) Noguchi, D.; Tanaka, H.; Kondo, A.; Kajiro, H.; Noguchi, H.; Ohba, T.; Kanoh, H.; Kaneko, K. Quantum Sieving Effect of ThreeDimensional Cu-Based Organic Framework for H2 and D2. J. Am. Chem. Soc. 2008, 130, 6367. (28) Garberoglio, G. Quantum Sieving in Organic Frameworks. Chem. Phys. Lett. 2009, 467, 270. (29) Accelrys, Inc., Materials Studio, 3.0 V; Accelrys Inc.: San Diego, CA, 2003. (30) Navarro, J. A. R.; Barea, E.; Rodríguez-Dieguez, A.; Salas, J. M.; Ania, C. O.; Parra, J. B.; Masciocchi, N.; Galli, S.; Sironi, A. GuestInduced Modification of A Magnetically Active Ultramicroporous, Gismondine-Like, Copper(II) Coordination Network. J. Am. Chem. Soc. 2008, 130, 3978. (31) Kubota, Y.; Takata, M.; Matsuda, R.; Kitaura, R.; Kitagawa, S.; Kato, K.; Sakata, M.; Kobayashi, T. C. Direct Observation of Hydrogen Molecules Adsorbed onto A Microporous Coordination Polymer. Angew. Chem., Int. Ed. 2005, 44, 920. (32) Pan, L.; Sander, M. B.; Huang, X. Y.; Li, J.; Smith, M.; Bittner, E.; Bockrath, B.; Johnson, J. K. Microporous Metal Organic Materials: Promising Candidates as Sorbents for Hydrogen Storage. J. Am. Chem. Soc. 2004, 126, 1308.

’ ASSOCIATED CONTENT

bS

Supporting Information. Model clusters used for the charge calculations of the MOFs including the xyz coordinates and the corresponding atomic partial charges, spin state for the partial charges calculations, details of the GCMC simulation, and the calculated mean adsorption energy of D2 and H2 in CuBOTf at 40 K. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Tel.: +86-10-64431705. E-mail: [email protected].

’ ACKNOWLEDGMENT The financial support of the NSFC (nos. 20725622, 20821004, 20906002, 21136001) is greatly appreciated. ’ REFERENCES (1) Wang, Y.; Bhatia, S. K. Quantum Effect-Mediated Hydrogen Isotope Mixture Separation in Slit Pore Nanoporous Materials. J. Phys. Chem. C 2009, 113, 14953. (2) Beenakker, J. J. M.; Borman, V. D.; Krylov, S. Y. Molecular Transport in Subnanometer Pores: Zero-Point Energy, Reduced Dimensionality and Quantum Sieving. Chem. Phys. Lett. 1995, 232, 379. (3) Wang, Q.; Johnson, J. K. Hydrogen Adsorption on Graphite and in Carbon Slit Pores from Path Integral Simulations. Mol. Phys. 1998, 95, 299. (4) Wang, Q.; Challa, S. R.; Sholl, D. S.; Johnson, J. K. Quantum Sieving in Carbon Nanotubes and Zeolites. Phys. Rev. Lett. 1999, 82, 956. (5) Wang, Q.; Johnson, J. K. Molecular Simulation of Hydrogen Adsorption in Single-Walled Carbon Nanotubes and Idealized Carbon Slit Pores. J. Chem. Phys. 1999, 110, 577. (6) Challa, S. R.; Sholl, D. S.; Johnson, J. K. Light Isotope Separation in Carbon Nanotubes Through Quantum Molecular Sieving. Phys. Rev. B 2001, 63, 245419. (7) Challa, S. R.; Sholl, D. S.; Johnson, J. K. Adsorption and Separation of Hydrogen Isotopes in Carbon Nanotubes: Multicomponent Grand Canonical Monte Carlo Simulations. J. Chem. Phys. 2002, 116, 814. (8) Tanaka, H.; Kanoh, H.; El-Merraoui, M.; Steele, W. A.; Yudasaka, M.; Iijima, S.; Kaneko, K. Quantum Effects on Hydrogen Adsorption on Internal Nanospaces of Single-Wall Carbon Nanohorns. J. Phys. Chem. B 2004, 108, 17457. (9) Tanaka, H.; Kanoh, H.; Yudasaka, M.; Iijima, S.; Kaneko, K. Quantum Effects on Hydrogen Isotope Adsorption on Single-Wall Carbon Nanohorns. J. Am. Chem. Soc. 2005, 127, 7511. (10) Kumar, A. V. A.; Jobic, H.; Bhatia, S. K. Quantum Effects on Adsorption and Diffusion of Hydrogen and Deuterium in Microporous Materials. J. Phys. Chem. B 2006, 110, 16666. 441



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