A General Approach for Estimating Framework Charges in Metal

J. Phys. Chem. C 2010, 114, 5035–5042

5035

A General Approach for Estimating Framework Charges in Metal-Organic Frameworks Qing Xu and Chongli Zhong* Laboratory of Computational Chemistry, Department of Chemical Engineering, Beijing UniVersity of Chemical Technology, Beijing 100029, China ReceiVed: NoVember 04, 2009; ReVised Manuscript ReceiVed: February 12, 2010

Although the number of metal-organic frameworks (MOFs) is nearly infinite, the atom types included are quite limited. On the basis of this thought, this work provides a strategy for estimating framework charges in MOFs. We developed a so-called connectivity-based atom contribution method (CBAC), in which it is assumed that the atoms with same bonding connectivity have identical charges in different MOFs. The results for 43 MOFs including a training set of 30 MOFs and a test set of 13 MOFs show that the CBAC charges give nearly identical results to those from quantum mechanical calculations for adsorption isotherms of CO2, CO, and N2 in them. Since the method will readily include new atom types, it is applicable to any MOF as long as its structure is known. The strategy, which is applicable to other porous materials, paves a way for largescale computational screening of MOFs for specific applications as well as contributes to a better understanding of the structure-property relationships for MOFs and eventually contributes to the development and application of MOFs. 1. Introduction Metal-organic frameworks (MOFs) have shown potential applications in a variety of fields, such as gas storage and separation.1-18 The major advantage of MOFs over more traditional porous materials, such as zeolites or activated carbons, is the greater scope for tailoring these materials for specific applications.19 To date, thousands of MOFs have been synthesized, and this number is nearly unlimited due to the large variety of possible linker and corner units;20 this indicates that a purely experimental means for designing optimal MOFs for targeted applications is inefficient at best, for which atomiclevel simulations provide a powerful tool to complement experimental methods for screening existing and hypothetical MOFs, which can also provide a detailed picture on the molecular scale that is not easily accessible from experimental methods. Currently, the main obstacle to large-scale computational screening of MOFs for a specific application is the absence of framework charges for most MOFs since electrostatic chargequadrupole and charge-induced-dipole interactions are usually involved. To our knowledge, the number of MOFs with available framework charges is limited to less than 30, which is rather small compared with the large number of available MOFs. The commonly adopted method for estimating the atomic partial charges of framework atoms is quantum mechanical calculation, which is time-consuming and the results are method sensitive. As a result, a general approach that can quickly estimate the atomic partial charges in any MOFs, both existing and hypothetical ones, is highly needed, which can stimulate significantly the progress in computational modeling of MOFs, and thus guide experiments. To solve the above obstacle, we proposed a strategy that is called connectivity-based atom contribution method, with which the atomic partial charges of framework atoms in any MOFs can be estimated easily. With the method, standard force fields * Corresponding author: [email protected].

tel,

+86-10-64419862;

e-mail,

can be used readily to predict adsorption and diffusion in both existing and hypothetical MOFs. 2. Computational Method 2.1. Quantum Mechanical Calculation. In this work, the atomic partial charges in the frameworks were calculated using density functional theory (DFT) on the basis of the fragmental clusters by Gaussian 03 package.21 The method of constructing model clusters is the same as that adopted in our previous work.22-24 For the cleaved clusters of ZIFs and MILs, the terminations are connected with metal atoms. Thus, to consider the environmental effect of these metal atoms in the real systems, these clusters were saturated with light metal atom Li as Sagara et al.,25 which was considered to be the simple model for the cluster bound to the metal centers23-28 while for the cleaved clusters of other MOFs, the terminations are all connected with organic linkers, and therefore, were saturated with -CH3 groups as done in our previous work.22-24 Based on the ChelpG method,29 DFT calculations using the unrestricted B3LYP functional were carried out to compute the atomic partial charges, and two kinds of basis sets were adopted: for heavy atoms, such as Zn, Cu, Co, Cr, and V, the LANL2DZ basis set was used, and 6-31+G* was employed for the rest of the atoms. The ChelpG method has been generally accepted to be one of the most reliable charge fitting schemes for these types of materials.22-25,30-34 2.2. Force Field. In this work, carbon dioxide, nitrogen, and carbon monoxide were adopted as probe molecules in grand canonical Monte Carlo (GCMC) simulations. CO2 was modeled as a rigid linear triatomic molecule with three charged LennardJones (LJ) interaction sites located at each atom. The LJ potential parameters for atom O (σO) 0.305 nm and ε/kB ) 79.0 K) and atom C (σC ) 0.280 nm and ε/kB ) 27.0 K) in CO2 molecule with C-O bond length l ) 0.116 nm were taken from the TraPPE force field developed by Potoff and Siepmann.35 Partial point charges centered at each LJ site (qO ) -0.35e and qC ) 0.70e) approximately represent the first-order electrostatic and

10.1021/jp910522h  2010 American Chemical Society Published on Web 03/02/2010

5036

J. Phys. Chem. C, Vol. 114, No. 11, 2010

Xu and Zhong

Figure 1. Definition of CBAC atom types and their charges.

TABLE 1: ID and Structural Properties of the Training Set MOFs MOF ID 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

name of MOF

metal

IRMOF-1 IRMOF-3 IRMOF-8 IRMOF-10 IRMOF-12 IRMOF-14 IRMOF-16 IRMOF-18 PCN-6′ PCN-9 PCN-10 PCN-11 PCN-14 ZIF-1 ZIF-3 ZIF-10 ZIF-11 ZIF-65 ZIF-67 ZIF-69 ZIF-71 ZIF-74 ZIF-75 MOF-2 MOF-14 MOF-177 MOF-505 MOF-602 Cu-BTC MIL-53(Cr)

Zn Zn Zn Zn Zn Zn Zn Zn Cu Co Cu Cu Cu Zn Zn Zn Zn Co Co Zn Zn Zn Co Zn Cu Zn Cu Cu Cu Cr

organic linker

pore shape

ref

1,4-benzenedicarboxylate (BDC) (NH2)BDC 2,6-naphthalenedicarboxylate (2,6-NDC) biphenyldicarboxylate (BPDC) 2,5-pyridinedicarboxylate (HPDC) pyridine-3,4-dicarboxylate (PDC) triphenyldicarboxylate (TPDC) 2,3,5,6-tetramethylbenzene-1,4-dicarboxylate (TMBDC) 4, 4′, 4′′-s-triazine-2, 4, 6-triyltribenzoate (TATB) TATB azobenzene-3,3′,5,5′-tetracarboxylate (ABTC) trans-stilbene-3,3′,5,5′-tetracarboxylate (SBTC) 5,5′-(9,10-anthracenediyl)di-isophthalate (ADIP) imidazolate (IM) IM IM benzimidazolate (bIM) 2-nitroimidazolate (nIM) 2-methylimidazolate (mIM) nIM and 5-chlorobenzimidazolate (cbIM) 4,5-dichloroimidazolate (dcIM) nIM and 5,6-dimethylbenzimidazolate (mbIM) nIM and mbIM 1,3,5-benzenetricarboxylate (BTC) 1,3,5-benzenetribenzoate (BTB) BTB 3,3′,5,5′-biphenyltetracarboxylic (BPTC) 2,2′-dimethyl-4,4′-biphenlydicarboxylic (2-MeBPDC) BTC BDC

cubic cubic cubic cubic cubic cubic cubic cubic pocket/channel pore/catenation pore/channel pore/channel pore/channel pore/channel cage/channel cage/channel cage/window cage/window cage/window cage/channel cage/window pore/channel pore/channel channel pore/catenation pore/channel pore/channel pore/channel pocket/channel channel

39 39 39 39 39 39 39 40 41 42 43 43 44 45 45 45 45 46 46 46 46 46 46 49 48 50 51 51 53 53

Estimating Framework Charges

J. Phys. Chem. C, Vol. 114, No. 11, 2010 5037

Figure 2. Examples of the distribution of charge value for the same atom type in different MOFs.

second-order induction interactions, and the electrostatic interactions were handled using the Ewald summation technique. The N2 molecule was represented as a three-site model with two sites located at two N atoms and the third one located at its center of mass (COM). The site at each N atom was modeled by LJ interaction potential (σN) 0.331 nm and ε/kB ) 36.0 K). The bond length between two N atoms is 0.110 nm. Each N2 molecule was assigned a negative charge on each N atom (qN ) -0.482e) and a positive charge at the COM site (qCOM ) 0.964e). These potential parameters were also taken from the TraPPE force field.35 A four-site model developed by Piper et al.36 was used for CO. LJ sites are located on the carbon (σC ) 0.3385 nm and ε/kB ) 39.89 K) and oxygen (σO ) 0.2885 nm and ε/kB ) 61.57 K) atoms, and point charges are used to mimic the dipole moment of the molecule. In this four-site model, one charge site (qsite1 ) -0.636e) is placed on the molecular axis to the left of the carbon atom. The center of mass of the carbon atom provides the second charge site (qC ) 0.831e). The third charge site (qsite2 ) -0.195e) is located on the molecular axis between the carbon and oxygen atoms, and the center of mass of oxygen provides the fourth site without charge. The distances from the molecular center of mass to every site are lsite1 ) -0.1082 nm, lC ) -0.06446 nm, lsite2 ) -0.03256 nm, and lO ) 0.04836 nm, respectively.36 The interactions between adsorbates and MOFs are described by Dreiding force field.37 In our simulations, all the LJ cross interaction parameters were determined by the Lorentz-Berthelot mixing rules.

2.3. GCMC Simulation. In this study, the conventional GCMC simulation was performed to calculate the adsorption of CO2, N2, and CO in MOFs. The simulation box representing MOF-2, MIL-53 (Cr), and MIL-47 (V) contains 36 (6 × 2 × 3) unit cells; IRMOF-9, ZIF-1, -77, and MOF-602 contains 12 (3 × 2 × 2 for the first three and 2 × 2 × 3 for the last one) unit cells; and 8 (2 × 2 × 2) unit cells are adopted for the other MOFs. No finite-size effects existed by checking the simulations with larger boxes. A cutoff radius was set to 12.8 Å for the LJ interactions, and the long-range electrostatic interactions were handled using the Ewald summation technique with tinfoil boundary condition. For each state point, GCMC simulation consisted of 1.0 × 107 steps to guarantee the equilibration, followed by 1.0 × 107 steps to sample the desired thermodynamics properties. Gas-phase fugacities used to perform GCMC simulations were calculated with the Peng-Robinson equation of state. 3. Results and Discussion 3.1. Method Development. The central idea of our method is that although the number of MOFs is nearly infinite, the atom types involved are quite limited. Thus, if the framework charges of a MOF can be estimated solely based on the information of its atom types without requiring quantum mechanical (QM) calculations, it will make it possible for large-scale computational screening of MOFs for specific applications. Therefore, we proposed a strategy named “connectivity-based atom contribution” (CBAC) to solve this problem. We assume that the atomic partial charge of an atom in the framework of a MOF is

5038


Xu and Zhong

Figure 3. Comparison of QM-charge based and CBAC-charge based absolute adsorption isotherms of CO2 in the training set of 30 MOFs at 298 K: symbols, QM-charge-based simulations; solid lines with same color, CBAC-charge-based simulations.

Figure 4. Comparison of QM-charge-based and CBAC-charge-based absolute adsorption isotherms of (a) CO and (b) N2 in some typical MOFs at 298 K: symbols, QM-charge-based simulations; solid lines with same color, CBAC-charge-based simulations.

determined by its bonding environment, that is its bonding connectivity, and the atoms with same connectivity have identical charges in different MOFs; this is an approximate method that is similar to the well-known group contribution method that has been successfully used for calculating thermodynamic properties of fluids.38 In this work, we developed a databank that contains 35 most widely involved atom types, and the databank can be readily extended to include more atom types. Details of the categorization of atoms and the results are given in the following section. 3.2. Atom Types and Their Charges. A total of 30 MOFs with various pore sizes, topologies, and chemistries were adopted as the training set to establish the method. The structural properties of the MOFs are given in Table 1, and the atom types categorized are shown in Figure 1. To obtain the general charge values for the atoms, cluster-based QM calculations are required; for IRMOF-3, -18, PCN-9, -11, -14, ZIF-1, -11, -65, -67, -69, -71, -74, -75, MOF-2, -14, -602, MIL-53(Cr), the charges were calculated in this work, while for the other MOFs, they were taken from literature.22-24 It should be emphasized that the same methodology was used for the estimation of all the charges to keep consistency. In Figure 1, the atoms presented in yellow denote the types of the CBAC atoms, together with their connectivity. For example, Zn1 represents the CBAC atom type of Zn with atom Zn connected with four O atoms Its CBAC charge value was

estimated from the average of the 10 MOFs in the training set with this type of Zn atom, including MOF ID of 1-8, 24, and 26. The distributions of the QM charges for Zn1 in the 10 MOFs together with its average value are shown in Figure 2a. Definition and calculation methods for other CBAC atoms are the same. Figure 2 shows some examples for the distribution of charge value for the same atom type in different MOFs. Although they do have small differences, the distribution is narrow. Considering the computational uncertainties, it may be concluded that the idea of connectivity-based atom contribution is a good approximation for framework charge estimation in MOFs. The obtained atomic partial charges for the 35 atom types are shown in Figure 1, together with their definitions (connectivity). One thing that should be pointed out is that when the CBAC charges are input to a MOF, usually the framework is not exactly neutral; thus a slight adjustment is normally required. For the atoms with large charge values such as Zn1, Cu, O1, etc., the adjustment is within (3%, while for the atoms with small charge values, usually only (0.01e should be adjusted, depending on the specific value. Such slight adjustment also has to be performed when we input QM charges to a MOF material and the extent of adjustment is comparable between the two kinds of charges. On the other hand, such slight adjustment usually leads to negligible influence on calculation results.



Figure 5. Contour plots of the COM probability densities of CO2 in IRMOF-1, PCN-6′, and MOF-177 at 0.1 MPa (Zn, cobalt-blue; Cu, pink; O, red; C, gray; H, white; N, blue).

Figure 6. Comparison of QM-charge-based and CBAC-charge-based absolute adsorption isotherms of CO2 in the test set of 13 MOFs at 298 K: symbols, QM-charge-based simulations; solid lines with same color, CBAC-charge-based simulations. For clarity, the results are displayed in two figures.

To further validate the new method, CO2 was adopted as a probe molecule to study the adsorption in MOFs. Figure 3 shows the simulated CO2 adsorption isotherms in the training set of 30 MOFs, using both the QM charges and CBAC charges. Interestingly, for all the cases the two sets of charges give excellent agreement, indicating that the CBAC method is quite successful in estimating framework charges in MOFs. Furthermore, adsorption isotherms of CO and N2 in some typical MOFs

were simulated, and the results are shown in Figure 4. Again, the CBAC charges give excellent reproduction of the adsorption isotherms based on the QM charges. We further examined whether the CBAC method affects the adsorption sites in MOFs. The COM distributions of CO2 in IRMOF-1, PCN-6′, and MOF-177 at 0.1 MPa are shown in Figure 5 as examples. As expected, the adsorption sites are not affected.

5040


Xu and Zhong

Figure 7. Comparison of CBAC-charge-based simulated and experimental excess adsorption isotherms of CO2, CO, and N2 in some typical MOFs.

TABLE 2: Structural Properties of the Test Set MOFs name of MOF

metal

IRMOF-6 IRMOF-9 IRMOF-11 IRMOF-13 IRMOF-15 PCN-6 PCN-13 ZIF-8 ZIF-12 ZIF-68 ZIF-77 ZIF-78 MOP-23

Zn Zn Zn Zn Zn Cu Zn Zn Co Zn Zn Zn Cu

organic linker

pore shape

ref

(C2H4)BDC BPDC HPDC PDC TPDC TATB 9,10-anthracenedicarboxylic (ADC) mIM bIM bIM and nIM nIM nIM and 5-nitrobenzimidazolate (nbIM) 2,7-naphthalenedicarboxylate (2,7-NDC)

cubic cubic/catenation cubic/catenation cubic/catenation cubic/catenation pore/catenation pore/channel cage/window cage/window cage/channel pore/channel cage/channel pore/ catenation

39 39 39 39 39 54 55 45 45 46 46 56 51

3.3. Validation of the Method Using Test Set MOFs. A more serious validation of the method is to use it for other MOFs that are not included in establishing the method, for which a test set containing 13 MOFs were adopted, as shown in Table 2. For IRMOF-6, ZIF-8, -12, -68, -77, -78, and MOP-23, the charges were calculated in this work, while for the others they were taken from literature.23,24 For this purpose, comparisons were also made between QM-charge-based simulations and CBAC-charge-based simulations. For the test set of 13 MOFs shown in Table 2, CO2 adsorption isotherms were simulated using both QM charges and CBAC charges. Again, the results reported in Figure 6 show that both simulations agree with each other excellently, further illustrating the reliability of the CBAC method.

3.4. Comparison with Experimental Data. For some MOFs, experimental CO2, CO, or N2 adsorption isotherms are available. Therefore, we further validated our method by comparing CBAC-charge-based simulations with experimental data39,55,58-60 directly. Some typical results are shown in Figure 7. Obviously, good agreement was obtained for most systems excepting for MIL-53(Cr), in which the isotherm inflection was caused by structural change;61 this illustrates that as long as the van der Waals interactions between adsorbate and adsorbent can be described properly, the charges from the new method can be readily used to predict gas adsorption properties in MOFs. To illustrate the feasibility of the new method to include new atom types, we adopted MIL-47(V) as an example. In MIL47(V), three atom types are missing in our databank shown in



Figure 8. (a) Cluster used for calculating the atomic partial charges for atoms V, O9, and O10. (b) Comparison of simulated and experimental60 CO2 excess adsorption isotherm in MIL-47(V) (V, green; O, red; C, gray, H, white; Li, purple).

Figure 1. In this case, we made a cluster having the same connectivity as that in MIL-47(V), as shown in Figure 8a in which atoms V, O9, and O10 are the three missed atom types. Then the atomic partial charges for them were calculated using the QM method given before, as shown in Figure 8a. The three atomic partial charges together with the other atoms (such as C1 in Figure 8a) from our CBAC databank were adopted to simulate the adsorption isotherm of CO2 in MIL-47(V). The results in Figure 8b show that good agreement was obtained between simulation and experiment.60 This example shows the flexibility of the CBAC method: any new atom type can be readily included by calculating its charges with a cluster having the same connectivity, making the method a general one applicable to any MOF. 4. Conclusion In this work a strategy was proposed for estimating framework charges in MOFs. The developed connectivity-based atom contribution method gives reliable estimation for framework charges in 43 MOFs, including a training set of 30 MOFs and a test set of 13 MOFs. The simulation results for CO2, CO, and N2 adsorption isotherms show that the CBAC charges give nearly identical results to those from the QM charges, as well as good reproduction of experimental data. The new method not only is simple but also can readily include new atom types, making it possible to estimate framework charges in any MOF solely based on its structure. Therefore, this work paves the way for large-scale computational screening of MOFs for specific applications as well as contributes to a better understanding of the structure-property relationships for MOFs, contributing significantly to the development of MOFs in both scientific and practical points of view. In addition, the methodology is expected to be applicable to other families of nanoporous materials, such as the emerging family of covalent organic frameworks. Acknowledgment. This work was supported by the Natural Science Foundation of China (Nos. 20725622, 20821004). We thank Chengcheng Zheng for help in calculating framework charges in several MOFs. Supporting Information Available: Model clusters used for the QM charge calculations of the MOFs. This information is available free of charge via the Internet at http://pubs.acs.org. References and Notes (1) Rosi, N. L.; Eckert, J.; Eddaoudi, M.; Vodak, D. T.; Kim, J.; O’Keeffe, M.; Yaghi, O. M. Science 2003, 300, 1127. (2) Du¨ren, T.; Snurr, R. Q. J. Phys. Chem. B 2004, 108, 15703. (3) Snurr, R. Q.; Hupp, J. T.; Nguyen, S. T. AIChE J. 2004, 50, 1090.

(4) Du¨ren, T.; Sarkisov, L.; Yaghi, O. M.; Snurr, R. Q. Langmuir 2004, 20, 2683. (5) Pan, L.; Sander, M. B.; Huang, X.; Li, J.; Smith, M.; Bittner, E.; Bockrath, B.; Johnson, J. K. J. Am. Chem. Soc. 2004, 126, 1308. (6) Rosseinsky, M. J. Microporous Mesoporous Mater. 2004, 73, 15. (7) Garberoglio, G.; Skoulidas, A. I.; Johnson, J. K. J. Phys. Chem. B 2005, 109, 13094. (8) Keskin, S.; Sholl, D. S. J. Phys. Chem. C 2007, 111, 14055. (9) Fe´rey, G. Chem. Soc. ReV. 2008, 37, 191. (10) Bae, Y. S.; Mulfort, K. L.; Frost, H.; Ryan, P.; Punnathanam, S.; Broadbelt, L. J.; Hupp, J. T.; Snurr, R. Q. Langmuir 2008, 24, 8592. (11) Dubbeldam, D.; Galvin, C. J.; Walton, K. S.; Ellis, D. E.; Snurr, R. Q. J. Am. Chem. Soc. 2008, 130, 10884. (12) Liu, J.; Lee, J. Y.; Pan, L.; Obermyer, R. T.; Simizu, S.; Zande, B.; Li, J.; Sankar, S. G.; Johnson, J. K. J. Phys. Chem. C 2008, 112, 2911. (13) Liu, B.; Smit, B. Langmuir 2009, 25, 5918. (14) Keskin, S.; Sholl, D. S. Ind. Eng. Chem. Res. 2009, 48, 914. (15) Keskin, S.; Sholl, D. S. Langmuir 2009, 25, 11786. (16) Salles, F.; Kolokolov, D. I.; Jobic, H.; Maurin, G.; Llewellyn, P. L.; Devic, T.; Serre, C.; Ferey, G. J. Phys. Chem. C 2009, 113, 7802. (17) Babarao, R.; Jiang, J.; Sandler, S. I. Langmuir 2009, 25, 5239. (18) Babarao, R.; Jiang, J. J. Am. Chem. Soc. 2009, 131, 11417. (19) Czaja, A. U.; Trukhan, N.; Mu¨ller, U. Chem. Soc. ReV. 2009, 38, 1284. (20) Du¨ren, T.; Bae, Y. S.; Snurr, R. Q. Chem. Soc. ReV. 2009, 38, 1237. (21) Gaussian 03, ReVision D.01; Gaussian, Inc.: Wallingford, CT, 2004. (22) Yang, Q.; Zhong, C. J. Phys. Chem. B 2006, 110, 655. (23) Yang, Q.; Zhong, C.; Chen, J. J. Phys. Chem. C 2008, 112, 1562. (24) Zheng, C.; Liu, D.; Yang, Q.; Zhong, C.; Mi, J. Ind. Eng. Chem. Res. 2009, 48, 10479. (25) Sagara, T.; Klassen, J.; Ganz, E. J. Chem. Phys. 2004, 121, 12543. (26) Hu¨bner, O.; Glo¨ss, A.; Fichtner, M.; Klopper, W. J. Phys. Chem. A 2004, 108, 3019. (27) Mavrandonakis, A.; Tylianakis, E.; Stubos, A. K.; Froudakis, G. E. J. Phys. Chem. C 2008, 112, 7290. (28) Sagara, T.; Klassen, J.; Ortony, J.; Ganz, E. J. Chem. Phys. 2005, 123, 014701. (29) Francl, M. M.; Carey, C.; Chirlian, L. E.; Gange, D. M. J. Comput. Chem. 1996, 17, 367. (30) Heinz, H.; Suter, U. W. J. Phys. Chem. B 2004, 108, 18341. (31) Martin, F.; Zipse, H. J. Comput. Chem. 2005, 26, 97. (32) Liu, J.; Rankin, R. B.; Johnson, J. K. Mol. Simul. 2009, 35, 60. (33) Rankin, R. B.; Liu, J.; Kulkarni, A. D.; Johnson, J. K. J. Phys. Chem. C 2009, 113, 16906. (34) Farrusseng, D.; Daniel, C.; Gaudille`re, C.; Ravon, U.; Schuurman, Y.; Mirodatos, C.; Dubbeldam, D.; Frost, H.; Snurr, R. Q. Langmuir 2009, 25, 7383. (35) Potoff, J. J.; Siepmann, J. I. AIChE J. 2001, 47, 1676. (36) Piper, J.; Morrison, J. A.; Peters, C. Mol. Phys. 1984, 53, 1463. (37) Mayo, S. L.; Olafson, B. D.; Goddard III, W. A. J. Phys. Chem. 1990, 94, 8897. (38) Fredenslund, A.; Gmehling, J.; Michelsen, M. L.; Rasmussen, P.; Prausnitz, J. M. Ind. Eng. Chem. Proc. Des. DeV. 1977, 16, 450. (39) Eddaoudi, M.; Kim, J.; Rosi, N.; Vodak, D.; Wachter, J.; O’Keeffe, M.; Yaghi, O. M. Science 2002, 295, 469. (40) Rowsell, J. L. C.; Millward, A. R.; Park, K. S.; Yaghi, O. M. J. Am. Chem. Soc. 2004, 126, 5666. (41) Ma, S.; Sun, D.; Ambrogio, M.; Fillinger, J. A.; Parkin, S.; Zhou, H. J. Am. Chem. Soc. 2007, 129, 1858. (42) Ma, S.; Zhou, H. J. Am. Chem. Soc. 2006, 128, 11734. (43) Wang, X.; Ma, S.; Rauch, K.; Simmons, J. M.; Yuan, D.; Wang, X.; Yildirim, T.; Cole, W. C.; Lo´pez, J. J.; Meijere, A.; Zhou, H. Chem. Mater. 2008, 20, 3145.

5042


(44) Ma, S.; Sun, D.; Simmons, J. M.; Collier, C. D.; Yuan, D.; Zhou, H. J. Am. Chem. Soc. 2008, 130, 1012. (45) Park, K. S.; Ni, Z.; Coˆte´, A. P.; Choi, J. Y.; Huang, R.; UribeRomo, F. J.; Chae, H. K.; O’Keeffe, M.; Yaghi, O. M. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 10186. (46) Banerjee, R.; Phan, A.; Wang, B.; Knobler, C.; Furukawa, H.; O’Keeffe, M.; Yaghi, O. M. Science 2008, 319, 939. (47) Yaghi, O. M.; Davis, C. E.; Li, G.; Li, H. J. Am. Chem. Soc. 1997, 119, 2861. (48) Chen, B.; Eddaoudi, M.; Hyde, S. T.; O’Keeffe, M.; Yaghi, O. M. Science 2001, 291, 1021. (49) Chae, H. K.; Siberio-Pe´rez, D. Y.; Kim, J.; Go, Y. B.; Eddaoudi, M.; Matzger, A. J.; O’Keeffe, M.; Yaghi, O. M. Nature 2004, 427, 523. (50) Chen, B.; Ockwig, N. W.; Millward, A. R.; Contreras, D. S.; Yaghi, O. M. Angew. Chem., Int. Ed. 2005, 44, 4745. (51) Furukawa, H.; Kim, J.; Ockwig, N. W.; O’Keeffe, M.; Yaghi, O. M. J. Am. Chem. Soc. 2008, 130, 11650. (52) Chui, S. S.-Y.; Lo, S. M.-F.; Charmant, J. P. H.; Orpen, A. G.; Williams, I. D. Science 1999, 283, 1148.

Xu and Zhong (53) Millange, F.; Serre, C.; Fe´rey, G. Chem. Commun. 2002, 822. (54) Sun, D.; Ma, S.; Ke, Y.; Collins, D. J.; Zhou, H. J. Am. Chem. Soc. 2006, 128, 3896. (55) Ma, S.; Wang, X.; Collier, C. D.; Manis, E. S.; Zhou, H. Inorg. Chem. 2007, 46, 8499. (56) Banerjee, R.; Furukawa, H.; Britt, D.; Knobler, C.; O’Keeffe, M.; Yaghi, O. M. J. Am. Chem. Soc. 2009, 131, 3875. (57) Millward, A. R.; Yaghi, O. M. J. Am. Chem. Soc. 2005, 127, 17998. (58) Liang, Z.; Marshall, M.; Chaffee, A. L. Energy Fuels 2009, 23, 2785. (59) Wang, Q.; Shen, D.; Bu¨low, M.; Lau, M. L.; Deng, S.; Fitch, F. R.; Lemcoff, N. O.; Semanscin, J. Microporous Mesoporous Mater. 2002, 55, 217. (60) Bourrelly, S.; Llewellyn, P. L.; Serre, C.; Millange, F.; Loiseau, T.; Fe´rey, G. J. Am. Chem. Soc. 2005, 127, 13519. (61) Dubbeldam, D.; Krishna, R.; Snurr, R. Q. J. Phys. Chem. C 2009, 113, 19317.

JP910522H

A General Approach for Estimating Framework Charges in Metal

Recommend Documents