Subscriber access provided by DUKE UNIV
Article
Revealing the Structure-Property Relationships of MetalOrganic Frameworks for CO2 Capture from Flue Gas Dong Wu, Qingyuan Yang, Chongli Zhong, Dahuan Liu, Hongliang Huang, Wenjuan Zhang, and Guillaume Maurin Langmuir, Just Accepted Manuscript • DOI: 10.1021/la302223m • Publication Date (Web): 24 Jul 2012 Downloaded from http://pubs.acs.org on July 25, 2012
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Langmuir is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
Revealing the Structure-Property Relationships of Metal-Organic Frameworks for CO2 Capture from Flue Gas Dong Wu†, Qingyuan Yang,†* Chongli Zhong,† Dahuan Liu,†* Hongliang Huang,† Wenjuan Zhang†and Guillaume Maurin‡ †
State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, 100029, China.
‡
Institut Charles Gerhardt Montpellier, Université Montpellier 2, 34095, Montpellier cedex 05, France.
ACS Paragon Plus Environment
1
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 2 of 22
ABSTRACT: It is of great importance to establish quantitative structure-property relationship model that can correlate the separation performance of MOFs to their physicochemical features. In complement to the existing studies that screened the separation performance of MOFs from the adsorption selectivity calculated at infinite dilution, this work aims to build a QSPR model that can account for the CO2/N2 mixture (15:85) selectivity of an extended series of MOFs with a very large chemical and topological diversity under industrial pressure condition. It was highlighted that the selectivity for this mixture under such conditions is dominated by the interplay of the difference of the isosteric heats of adsorption between the two gases and the porosity of the MOF adsorbents. Based on the interplay map of both factors that impact the adsorption selectivity, strategies were proposed to efficiently enhance the separation selectivity of MOFs for CO2 capture from flue gas. As a typical illustration, it thus leads us to tune a new MOF with outstanding separation performance that will orientate the synthesis effort to be deployed.
ACS Paragon Plus Environment
2
Page 3 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
INTRODUCTION As a subject of widespread public concerns, the reduction of the anthropogenic CO2 emission in the atmosphere has become one of the most urgent climate issues.1 To circumvent this environmental problem, the CO2 capture technology needs to imply a minimal environmental impact and relatively low costs. Currently, the commercial carbon capture and sequestration (CCS) using aqueous amine-solutions bears several drawbacks such as the formation of corrosive species and the high energetic requirement for the solvent regeneration.2 In contrast, the technology based on pressure or vacuum swing adsorption (PSA or VSA) process involving porous adsorbents is known to be a promising way for CO2 removal from flue gas emitted from power plants with high efficiency.3 Indeed, many research efforts have been deployed to identify the most appropriate porous materials which would allow a highly selective capture and a fast release of CO2 through a physisorption mechanism.4-6 During the past decades, metal-organic frameworks (MOFs), a novel class of nanoporous crystalline solids, have received much attention in chemistry, chemical engineering and materials science.7,8 Because of their unique structural and chemical richness over the conventional porous adsorbents, MOFs could serve as an ideal platform to design materials specifically tailored for the separation of strategic gases. Indeed, both experimental9-15 and theoretical16-22 investigations have already established that some of these MOFs show great potential for CO2 capture from various gas mixtures. However, the number of materials examined in these studies was rather limited compared to thousands of MOFs with different chemical functionality, pore size and topology which have been reported so far. Thus, there is an increasing demand to expand the scope of the current methodology to efficiently screen a much larger population of MOFs. As far as we know, only a few studies have been reported so far for the large-scale screening of MOFs for gas separations purposes: two are related to the H2/CH4 mixture,23,24 one focuses on the
ACS Paragon Plus Environment
3
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 4 of 22
selectivity of CO2 over N2,25 while a more recent study deals with the separation of rare gases.26 In addition, Snurr and coworkers also developed a screening approach to explore the CH4 storage in a large variety of MOFs.27 These investigations clearly stated that molecular modeling in complement to the experimental approaches is a powerful tool to efficiently characterize and screen a large database of MOFs in a reasonable time. However, Most of them screened the separation performances of MOFs using the adsorption selectivity at infinite dilution (limiting selectivity) as criterion, similarly to most of the experimental evaluation.10 It is well established that the adsorption selectivity for certain porous materials can show very different profiles with increasing pressure,24,28,29 and the selectivity at infinite dilution often significantly deviates from those reached under industrial operating conditions, especially for the MOFs with a strong affinity for one component in the mixtures.21 Therefore, for a practical application, screening MOFs under the real conditions of a given application is essential. On the other hand, deriving a quantitative structure-property relationships (QSPR) model that could rationalize the performance of a large series of MOFs for a targeted application would be invaluable for not only predicting the performances of a given MOF, but also for further guiding the tuning/design of advanced materials with outstanding performance. However, such a statistical approach which has been successfully employed for the drug discovery is only at its early stage in the field of MOFs30 and only a few QSPR models based on a limited number of data set have been reported in the literature up to date. In light of the above considerations, post-combustion CO2 capture from CO2/N2 gas mixture in MOFs was selected as the representative application for our computational exploration in this work. The bulk composition of the mixture and the operating pressure were taken as CO2:N2=15:85 and 0.1 MPa respectively, which correspond to the typical concentration and industrial pressure condition for the separation of flue gas emitted from power plants.16
ACS Paragon Plus Environment
4
Page 5 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
MODELS AND SIMULATION METHOD MOF Structures In this work, 105 MOFs were considered in order to span a large diversity in chemistry and topology, covering the most well-known subfamilies and newly synthesized materials. Their framework structures were constructed from their corresponding experimental single-crystal diffraction data. The related structural features of the selected MOF materials are provided in Table S1 in the Supporting Information (SI). Force Fields Accurate investigation of the separation performance of MOFs requires reliable force fields for the adsorbed molecules and MOFs. In this study, CO2 was modeled as a rigid linear molecule with three charged LJ sites located on each atom. The LJ potential parameters for atom O (σO = 0.3033 nm and εO/kB = 80.507 K) and atom C (σC = 0.2757 nm and εC/kB = 28.129 K) in CO2 molecule with C-O bond length l = 0.1149 nm were taken from the EPM2 force field developed by Harris and Yung.31 Partial point charges centered at each LJ site were qO = -0.3256e and qC = 0.6512e. This potential model has been proved to give remarkable accuracy of the vapor-liquid phase equilibrium of CO2.31 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 εN/kB = 36.0 K). The bond length between two N atoms is 0.11 nm. In addition, the gas-phase quadrupole moment of N2 (Q = -4.67×10-40 C m2) was reproduced by placing point charges of -0.482e on each LJ site. To maintain charge neutrality, a point charge of +0.964e was placed at the center of mass (COM) of the N2 molecule. This TraPPE potential model for N2 was also developed to reproduce its experimental vapor-liquid coexistence curve and the critical properties.32 For the MOFs, a
ACS Paragon Plus Environment
5
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 6 of 22
combination of the LJ and Coulombic potential was employed to calculate the interactions between adsorbates and frameworks, and the LJ potential parameters for the framework atoms were taken from the Dreiding force field33 and the missing parameters for some metals were taken from the Universal force field,34 as listed in Table S2 in the SI. The Lorentz-Berthelot mixing rules were used to determine all the LJ cross interaction parameters. The partial charges for the framework atoms of MOFs were estimated by the connectivity-based atom contribution (CBAC) method35,36 with slightly adjustment to make neutral unit cells. Simulation Details Grand canonical Monte Carlo (GCMC) simulations were employed to calculate the adsorption of CO2/N2 mixture in the MOFs at 298 K. All the MOFs were treated as rigid frameworks with atoms frozen at their crystallographic positions. The numbers of the unit cells contained in the simulation box are MOF-dependent, ranging from 1×1×1 to 7×7×7, and no finite-size effects existed by checking the simulations with larger boxes. A cutoff radius was set to 1.4 nm for the LJ interactions, while the long-range electrostatic interactions were handled by the Ewald summation technique. In the simulations, molecules involve five types of trials: attempts (i) to displace a molecule (translation or rotation), (ii) to regrow a molecule at a random position, (iii) to create a new molecule, and (iv) to delete an existing molecule, (v) to exchange molecular identity. Periodic boundary conditions were considered in all three dimensions. Peng-Robinson equation of state was used to convert the pressure to the corresponding fugacity used in the GCMC simulations. A detailed description of the simulation methods can be found elsewhere.37 For each state point, GCMC simulations consisted of 2×107 steps to ensure the equilibration, followed by 2×107 steps to sample the desired thermodynamic properties. The selectivity for component A relative to component B is defined by S = ( xA / xB )( yB / yA ) , where x and y are the
ACS Paragon Plus Environment
6
Page 7 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
mole fractions of two components in the adsorbed and bulk phase, respectively. For the calculation of the isosteric heat of adsorption (Qst) at the limit of zero-coverage for CO2 and N2, configurational-bias Monte Carlo simulations in the canonical (NVT) ensemble were further performed using the revised Widom’s test particle method.38 It should be noted that some MOFs have been found to contain inaccessible regions for guest molecules due to steric hindrance or incomplete activation of the sample.26,39,40 For primarily developing a large-scale screening approach, our current study does not take these regions into account. Such an approximation seems reliable as we have demonstrated that the selectivity of the narrow windows/pores MOFtype ZIF-68 is only slightly affected whether or not the inaccessible regions are included in the calculations (see Table S3 in the SI).
RESULTS AND DISCUSSION As a first step, molecular simulations based on GCMC calculations were performed to estimate the CO2/N2 adsorption selectivities at 0.1 MPa and 298 K for all the 105 MOFs. Due to the quadrupole moment of both CO2 and N2, it is crucial to take into account the adsorbateframework electrostatic interactions. Herein, the partial charges for the framework atoms of MOFs were assigned using the connectivity-based atom contribution (CBAC) approach35 we previously developed for estimating the atomic partial charges of a wide range of MOFs, which is pre-requisite for further predicting their adsorption and diffusion performances.4,41-43 This semi-empirical strategy, which is complementary to the PQeq effective method that was also used for the charge calculations of a large-scale screened MOFs,25 presents a considerable advantage to avoid time consuming charge calculation methods including the REPEAT and DDEC approaches44 while maintain a reasonable accuracy. For the materials with the atomic types not available, additional quantum mechanics (QM) calculations were performed to derive
ACS Paragon Plus Environment
7
Langmuir
their charges, and the corresponding CBAC charges are shown in Figure S1 in the SI. This achievement represents a considerable extension of our CBAC database. To further validate the applicability of the charges obtained from the CBAC method, the calculated adsorption selectivities for 15 materials arbitrarily selected from 105 MOFs were compared with those obtained using the QM charges. It can be seen from Figure 1 that these two sets of charges lead to very similar selectivities, demonstrating that the CBAC scheme is accurate enough to considerably speed the computational scanning of a large series MOFs for CO2/N2 separation that could not be feasible in reasonable time if one would preliminarily require to determine DFTbased charges. 30 25
CBAC charges QM charges
S0.1MPa
20 15 10 5 0
IR M IR OF IR MO -1 M F IR OF -8 M -1 IR OF 0 M IR OF 11 M -1 M OF- 3 O 1 M F-1 6 OF 77 -5 PC 05 PC N-6 PC N-6 N- ' 1 ZI 0 ZI F-3 F ZI -10 Cu F-68 -B TC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 8 of 22
Figure 1. Comparison of the calculated selectivities at 298 K and 0.1 MPa for 15 MOFs based on the CBAC and QM charges. To find out the structure-property relationship for the investigated MOFs, a single-factor analysis was performed to establish a correlation between their CO2/N2 selectivities and some relevant descriptors that characterize the physicochemical properties of these materials and the strength of their interactions with the considered gases. They involve the intrinsic energetic and structural features of MOFs, including the difference of isosteric heats of adsorption (∆Qst0)
ACS Paragon Plus Environment
8
Page 9 of 22
between CO2 and N2 at infinite dilution, the specific accessible surface area (Sacc), the free volume (Vfree) and the porosity (φ). The pore size was not envisaged since some MOFs contain more than one type of pores with different sizes. The calculation details of these quantities are given in the SI. Based on the single-factor analysis, it is clear that ∆Qst0 and φ are the most correlated descriptors to the CO2/N2 selectivity evaluated under industrial pressure condition (see Figure S2 in the SI). To assess the interplay of these two descriptors, different combinations have been tested to build individual QSPR models. Figure 2 shows the best QSPR model using (∆Qst0/φ) as a variable, the good correlation coefficient (r=0.94) between the predicted (QSPR model) and simulated (molecular simulation) CO2/N2 selectivity emphasizing the quality of the so-built model. Its robustness was further internally validated by using a Leave-one-out (LOO) procedure that leads to a rather good cross validation correlation coefficient Q2 of 0.87 considering the large diversity of the MOFs included in the dataset. Interestingly, such a combination of ∆Qst0 and φ can be directly related to the concept of “adsorbility” proposed in our previous work.24 120 100 80
S0.1MPa
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
S 0.1MPa = 0.04523 × (
∆Qst0
ϕ
) 2.0765 + 1.0
r = 0.94
60 40 20 0 0.00
0.05
0.10 0
(∆Qst/ϕ)
0.15
0.20
-1
Figure 2. Relationship between the adsorption selectivity and ∆Qst0 and porosity (φ) in 105 MOFs for CO2/N2 mixture (CO2:N2=15:85) separation at 0.1 MPa and 298 K.
ACS Paragon Plus Environment
9
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 10 of 22
To further test the transferability of our model by an external validation, we selected four additional MOFs which were not included in our regression analysis: Na-Rho-ZMOF is a Na+ cation containing MOF,45 UGEPEB46 and FOHQUO47 are two MOFs with high separation performance for CO2/N2 mixture identified by Sholl et al.,25 while Ca(SDB) is a MOF reported recently.48 The predicted selectivities for the four selected MOFs using our QSPR model (Figure 2), reproduce well the values reported in the literature (either from simulation or experiment) (see Table 1), thus demonstrating that our QSPR model is satisfactorily transferable for an external set of MOFs. As far as we know, this is the first quantitative structure-property relationship of MOFs that has been built and validated for further predicting the CO2/N2 mixture adsorption selectivity under industrial pressure condition. To use this formula, the input parameters are the intrinsic properties of MOFs, the isosteric heats of adsorption of pure CO2 and N2 at infinite dilution (Qst0) and porosity of the adsorbent. Both quantities can be most often extracted from experimental data available in the literature, which could save the computational efforts. In case the values of Qst0 are still unknown, simple and fast Monte Carlo simulations in the NVT ensemble are required for each gas considering only one single particle in the simulation box, as done in this work and by others.21,38
Table 1. Comparison between the predicted CO2/N2 selectivity at 0.1 MPa and 298 K in some typical MOFs issued from the QSPR model and those reported in the literature. S0.1 MPa
φ
∆Qst0 (kJ mol-1)
Na-Rho-ZMOF
0.578
47.8a
Pred. 433
Literature 50021
UGEPEB
0.339
17.1a
157
19725
FOHQUO
0.304
19.0a
243
26925
Ca(SDB)
0.336
9.0b
43
5048
materials
a
Simulated value, b Experimental value.
ACS Paragon Plus Environment
10
Page 11 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
Beyond the predictions of the absolute values for the selectivity, it is also of interest to provide an interpretation of the QSPR model, emphasizing the impact of the individual ∆Qst0 and φ contributions on the CO2/N2 selectivity estimated for the whole set of MOFs. To that purpose, Figure 3 shows the interplay map of these two factors on the selectivity at 0.1 MPa, where the intervals between the contour lines denote the different ranges of selectivity. The interplay map was built based on our QSPR model. First, a large number of random values for ∆Qst0 and porosity were generated individually within the ranges of these two quantities considered in this work. Then, the corresponding selectivity for each (∆Qst0, porosity) pair was calculated using the QSPR model, which allowed us to represent the interplay map as shown in Figure 3. From this figure, some fundamental rules can be generalized : (i) the influence of the porosity is evident only when ∆Qst0 is large enough; in this case, decreasing the porosity is an efficient way to increase the selectivity; (ii) to achieve a selectivity above 40, it seems that a minimal ∆Qst0 value of 8 kJ mol-1 is a pre-requisite, and (iii) increasing ∆Qst0 and simultaneously decreasing φ seems to be a more appropriate route to enhance the selectivity compared to the most usual approach that consists of pursuing a larger ∆Qst0. These observations based on a careful analysis of our QSPR model are expected to be useful for further tuning new MOFs.
ACS Paragon Plus Environment
11
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 12 of 22
Figure 3. The interplay map of φ and ∆Qst0 on their impact on the selectivity at 0.1 MPa for CO2/N2 mixture in MOFs, where the design strategy based on UiO-66(Zr) is also given. To validate these conclusions, UiO-66(Zr)49,50 and its functionalized forms with -NH2 (UiO66(Zr)-NH2)50 and -CF3 (UiO-66(Zr)-(CF3)2)50 groups were considered. The calculated φ and ∆Qst0 of these three materials are reported in Table 2 where we can also see a good agreement between the predicted and simulated selectivities. One can further define in Figure 3 a triangle that clearly emphasizes the different possible routes to enhance the selectivity: route 1 denotes that by increasing ∆Qst0 with similar porosity, the selectivity can be improved largely; route 2 shows that with even similar ∆Qst0, a drastic decrease of porosity can also lead to an evident increase of selectivity. However, if we take route 3 to simultaneously increase ∆Qst0 and decrease porosity, largest enhancement of the selectivity can be reached. Starting from this conclusion, we designed a novel modified version of UiO-66(Zr) with the highly polar sulfonic groups (UiO66(Zr)-(SO3H)2). Its crystal structure was obtained by DFT geometry optimization using CRYSTAL09 code51 (details can be found in the SI). Compared to the parent UiO-66(Zr), the sobuilt UiO-66(Zr)-(SO3H)2 shows both significantly increased ∆Qst0 and decreased porosity, leading to an enhanced selectivity of 82, about 400% from the original material.
ACS Paragon Plus Environment
12
Page 13 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
Table 2.
Comparison of the predicted selectivities at 0.1 MPa for CO2/N2 mixture in the
hydroxylated UiO-66(Zr) series based on the established model with those simulated ones. materials
φ
∆Qst0 (kJ mol-1)
S0.1 MPa
UiO-66(Zr)
0.588
10.6
Pred. 19
Sim. 25
UiO-66(Zr)-NH2
0.562
13.2
33
42
UiO-66(Zr)-(CF3)2
0.432
12.2
48
57
UiO-66(Zr)- (SO3H)2
0.404
14.9
82
80
CONCLUSIONS With the aid of molecular modeling combined with CBAC method, the separation performance of 105 MOFs with a large chemical and topological diversity for CO2 capture from flue gas was examined under industrial pressure condition. A QSPR model was for the first time built from this extended series of MOFs in order to rationalize the resulting CO2/N2 selectivity. This largescale computational study shows that ∆Qst0 and φ are the main features of the MOFs that strongly impact the adsorption selectivity of the MOFs at 0.1 MPa. The results further highlight that the interplay of ∆Qst0 and φ is crucial to interpret the evolution of the selectivity along the whole range of MOFs. Indeed, simultaneously increasing ∆Qst0 and decreasing φ was revealed to be an intelligent approach to tune new MOFs. This was validated on a relatively novel class of Zrbased MOFs and we were further able to design a newly functionalized sulfonic form UiO66(Zr)-(SO3H)2 characterized by a very high CO2/N2 selectivity of 82 that makes this material very promising for CO2 capture from flue gas under industrial conditions. In addition, a synthesis effort is currently deployed to synthesize this sample. Such a predictive approach is very useful to
ACS Paragon Plus Environment
13
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 14 of 22
guide the synthesis effort toward more appropriate materials. Beyond this finding, the methodology developed in this work is expected also transferable to other gas mixtures and nanoporous materials that can open new horizon for tailoring materials specifically for a given application.
ASSOCIATED CONTENT AUTHOR INFORMATION Corresponding Author *E-mail:
[email protected],
[email protected]
Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENT This work was supported by the financial support of Natural Science Foundation (No: 21136001 and 21121064), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Contract 20110010130001). The support of Beijing Nova Program (No. 2008B15) is also greatly appreciated. Supporting Information Available. Details of the MOFs examined in this work, potential parameters for the MOFs, additional CBAC atomic types and charges, DFT calculations, structure optimization, and some results. This information is available free of charge via the Internet at http://pubs.acs.org.
ACS Paragon Plus Environment
14
Page 15 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
REFERENCES (1) Li, J.-R.; Sculley, J.; Zhou, H.-C. Metal-Organic Frameworks for Separations. Chem. Rev. 2012, 112, 869–932. (2) D'Alessandro, D. M.; Smit, B.; Long, J. R. Carbon Dioxide Capture: Prospects for New Materials. Angew. Chem., Int. Ed. 2010, 49, 6058–6082. (3) Férey, G.; Serre, C.; Devic, T.; Maurin, G.; Jobic, H.; Llwewllyn, P. L.; De Weireld, G.; Vimont, A.; Daturi, M.; Chang, J.-S. Why Hybrid Porous Solids Capture Greenhouse Gases? Chem. Soc. Rev. 2011, 40, 550–562. (4) Meek, S. T.; Greathouse, J. A.; Allendorf, M. D. Metal-Organic Frameworks: A Rapidly Growing Class of Versatile Nanoporous Materials. Adv. Mater. 2011, 23, 249–267. (5) Huang, L.; Zhang, L.; Shao, Q.; Lu, L.; Lu, X.; Jiang, S.; Shen, W. Simulations of Binary Mixture Adsorption of Carbon Dioxide and Methane in Carbon Nanotubes: Temperature, Pressure, and Pore Size Effects. J. Phys. Chem. C 2007, 111, 11912–11920. (6) Liu, X.; Newsome, D.; Coppens, M.-O. Dynamic Monte Carlo Simulations of Binary SelfDiffusion in ZSM-5. Micropor. Mesopor. Mater. 2009, 125, 149–159. (7) Banerjee, R.; Phan, A.; Wang, B.; Knobler, C.; Furukawa, H.; O’Keeffe, M.; Yaghi, O. M. High-Throughput Synthesis of Zeolitic Imidazolate Frameworks and Application to CO2 Capture. Science 2008, 319, 939–943. (8) Yanai, N.; Kitayama, K.; Hijikata, Y.; Sato, H.; Matsuda, R.; Kubota, Y.; Takata, M.; Mizuno, M.; Uemura, T.; Kitagawa, S. Gas Detection by Structural Variations of Fluorescent Guest Molecules in a Flexible Porous Coordination Polymer. Nat. Mater. 2011, 10, 787–793. (9) Chen, Z.; Xiang, S.; Arman, H. D.; Li, P.; Zhao D.; Chen, B. Significantly Enhanced CO2/CH4 Separation Selectivity within a 3D Prototype Metal–Organic Framework
ACS Paragon Plus Environment
15
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 16 of 22
Functionalized with OH Groups on Pore Surfaces at Room Temperature. Eur. J. Inorg. Chem. 2011, 2227–2231. (10) Banerjee, R.; Furukawa, H.; Britt, D.; Knobler, C.; O’Keeffe, M.; Yaghi, O. M. Control of Pore Size and Functionality in Isoreticular Zeolitic Imidazolate Frameworks and their Carbon Dioxide Selective Capture Properties. J. Am. Chem. Soc. 2009, 131, 3875–3877. (11) Liang, Z.; Marshall, M.; Chaffee, A. L. CO2 Adsorption-Based Separation by Metal Organic Framework (Cu-BTC) versus Zeolite (13X). Energy Fuels 2009, 23, 2785–2789. (12) Serra-Crespo, P.; Ramos-Fernandez, E. V.; Gascon J.; Kapteijn, F. Synthesis and Characterization of an Amino Functionalized MIL-101(Al): Separation and Catalytic Properties. Chem. Mater., 2011. 23, 2565–2572. (13) McDonald, T. M.; D’Alessandro, D. M.; Krishna, R.; Long, J. R. Enhanced Carbon Dioxide Capture upon Incorporation of N,N’-Dimethylethylenediamine in the Metal-Organic Framework CuBTTri. Chem. Sci., 2011, 2, 2022–2028. (14) Lu, W.; Yuan, D.; Sculley, J.; Zhao, D.; Krishna, R.; Zhou, H.-C. Sulfonate-Grafted Porous Polymer Networks for Preferential CO2 Adsorption at Low Pressure. J. Am. Chem. Soc. 2011, 133, 18126–18129. (15) Llewellyn, P. L.; Bourrelly, S.; Serre, C.; Filinchuk, Y.; Férey, G. How Hydration Drastically Improves Adsorption Selectivity for CO2 over CH4 in the Flexible Chromium Terephthalate MIL-53. Angew. Chem. Int. Ed. 2006, 45, 7751–7754. (16) Bae, Y.-S.; Farha, O. K.; Hupp, J. T. ; Snurr, R. Q. Enhancement of CO2/N2 Selectivity in a Metal-Organic Framework by Cavity Modification. J. Mater. Chem. 2009, 19, 2131–2134. (17) Martín-Calvo, A.; García-Pérez, E.; Castillo, J. M.; Calero, S. Molecular Simulations for Adsorption and Separation of Natural Gas in IRMOF-1 and Cu-BTC Metal-Organic Frameworks. Phys. Chem. Chem. Phys. 2008, 10, 7085–7091.
ACS Paragon Plus Environment
16
Page 17 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
(18) Yang, Q.; Xue, C.; Zhong, C.; Chen, J.-F. Molecular Simulation of Separation of CO2 from Flue Gases in Cu-BTC Metal-Organic Framework. AIChE J. 2007, 53, 2832–2840. (19) Liu, J.; Keskin, S.; Sholl, D. S.; Johnson, J. K. Molecular Simulations and Theoretical Predictions for Adsorption and Diffusion of CH4/H2 and CO2/CH4 Mixtures in ZIF. J. Phys. Chem. C 2011, 115, 12560–12566. (20) Karra, J. R.; Walton, K. S. Molecular Simulations and Experimental Studies of CO2, CO, and N2 Adsorption in Metal-Organic Frameworks. J. Phys. Chem. C 2010, 114, 15735– 15740. (21) Babarao, R.; Jiang, J. Unprecedentedly High Selective Adsorption of Gas Mixtures in rho Zeolite-like Metal-Organic Framework: A Molecular Simulation Study. J. Am. Chem. Soc. 2009, 131, 11417–11425. (22) Cessford, N. F.; Seaton, N. A.; Düren, T. Evaluation of Ideal Adsorbed Solution Theory as a Tool for the Design of Metal–Organic Framework Material. Ind. Eng. Chem. Res. 2012, 51, 4911–4921. (23) Haldoupis, E.; Nair, S.; Sholl, D. S. Efficient Calculation of Diffusion Limitations in Metal Organic Framework Materials: A Tool for Identifying Materials for Kinetic Separations. J. Am. Chem. Soc. 2010, 132, 7528–7539. (24) Wu, D.; Wang, C.; Liu, B.; Liu, D.; Yang, Q.; Zhong, C. Large-Scale Computational Screening of Metal-Organic Frameworks for CH4/H2 Separation. AIChE J. 2012, 58, 2078– 2084. (25) Haldoupis, E.; Nair, S.; Sholl, D. S. Finding MOFs for Highly Selective CO2/N2 Adsorption Using Materials Screening Based on Efficient Assignment of Atomic Point Charges. J. Am. Chem. Soc. 2012, 134, 4313–4323.
ACS Paragon Plus Environment
17
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 18 of 22
(26) Heest, T. V.; Teich-McGoldrick, S. L.; Greathouse, J. A.; Allendorf, M. D.; Sholl, D. S. Identification of Metal-Organic Framework Materials for Adsorption Separation of Rare Gases: Applicability of Ideal Adsorbed Solution Theory (IAST) and Effects of Inaccessible Framework Regions. J. Phys. Chem. C 2012, 116, 13183–13195. (27) Wilmer, C. E.; Leaf, M.; Lee, C. Y.; Farha, O. K.; Hauser, B. G.; Hupp, J. T.; Snurr, R. Q. Large-Scale Screening of Hypothetical Metal-Organic Frameworks. Nat. Chem. 2012, 4, 83– 89. (28) Liu, B.; Smit, B. Comparative Molecular Simulation Study of CO2/N2 and CH4/N2 Separation in Zeolites and Metal-Organic Frameworks. Langmuir 2009, 25, 5918–5926. (29) Zhang, Z.; Liu, J.; Li, Z.; Li, J. Experimental and Theoretical Investigations on the MMOF Selectivity for CO2 vs. N2 in Flue Gas Mixtures. Dalton Tran. 2012, 41, 4232–4238. (30) Gaudin, C.; Cunha, D.; Ivanoff, E.; Horcajada, P.; Chevé, G.; Yasri, A.; Loget, O.; Serre, C.; Maurin, G. A Quantitative Structure Activity Relationship Approach to Probe the Influence of the Functionalization on the Drug Encapsulation of Porous Metal-Organic Frameworks. Micropor. Mesopor. Mater. 2012, 157, 124–130. (31) Harris, J. G.; Yung, K. H. Carbon Dioxide's Liquid-Vapor Coexistence Curve And Critical Properties as Predicted by a Simple Molecular Model. J. Phys. Chem. 1995, 99, 12021– 12024. (32) Potoff, J. J.; Siepmann, J. I. Vapor-Liquid Equilibria of Mixtures Containing Alkanes, Carbon Dioxide and Nitrogen. AIChE J. 2001, 47, 1676–1682. (33) Mayo, S. L.; Olafson, B. D.; Goddard III, W. A. DREIDING: A Generic Force Field for Molecular Simulations. J. Phys. Chem. 1990, 94, 8897–8909.
ACS Paragon Plus Environment
18
Page 19 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
(34) Rappé, A. K.; Casewit, C. J.; Colwell, K. S.; Goddard III, W. A.; Skiff, W. M. UFF, a Full Periodic Table Force Field for Molecular Mechanics and Molecular Dynamics Dimulations. J. Am. Chem. Soc. 1992, 114, 10024–10039. (35) Xu, Q.; Zhong C. A General Approach for Estimating Framework Charges in Metal-Organic Frameworks. J. Phys. Chem. C 2010, 114, 5035–5042. (36) Zheng, C.; Zhong, C. Estimation of Framework Charges in Covalent Organic Frameworks Using Connectivity-Based Atom Contribution Method. J. Phys. Chem. C 2010, 114, 9945– 9951. (37) Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications; Academic Press: San Diego, 2002. (38) Vlugt, T. J. H.; García-Pérez, E.; Dubbeldam, D.; Ban, S.; Calero, S. Computing the Heat of Adsorption using Molecular Simulations: The Effect of Strong Coulombic Interactions. J. Chem. Theory Comput. 2008, 4, 1107–1118. (39) Babarao, R.; Dai, S.; Jiang, D.-E. Effect of Pore Topology and Accessibility on Gas Adsorption Capacity in Zeolitic_Imidazolate Frameworks: Bringing Molecular Simulation Close to Experiment. J. Phys. Chem. C 2011, 115, 8126-8135. (40) Yang, Q.; Vaesen, S.; Vishnuvarthan, M.; Ragon, F.; Serre, C.; Vimont, A.; Daturi, M.; De Weireld, G.; Maurin, G. Probing the Adsorption Performance of the Hybrid Porous MIL68(Al): A Synergic Combination of Experimental and Modelling Tools. J. Mater. Chem. 2012, 22, 10210–10220. (41) Li, J. R.; Ma, Y.; McCarthy, M. C.; Sculley, J.; Yu, J.; Jeong, H. K.; Balbuena, P. B.; Zhou, H.-C. Carbon Dioxide Capture-Related Gas Adsorption and Separation in Metal-Organic Frameworks. Coord. Chem. Rev. 2011, 255, 1791–1823.
ACS Paragon Plus Environment
19
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 20 of 22
(42) Keskin, S. Atomistic Simulations for Adsorption, Diffusion and Separation of Gas Mixtures in Zeolite Imidazolate Frameworks. J. Phys. Chem. C 2011, 115, 800–807. (43) Krishna, R.; van Baten, J. M. Hydrogen Bonding Effects in Adsorption of Water−Alcohol Mixtures in Zeolites and the Consequences for the Characteristics of the Maxwell−Stefan Diffusivities. Langmuir 2010, 26, 10854–10867. (44) Watanabe, T.; Manz, T. A.; Sholl, D. S. Accurate Treatment of Electrostatics during Molecular Adsorption in Nanoporous Crystals without Assigning Point Charges to Framework Atoms. J. Phys. Chem. C 2011, 115, 4824–4836. (45) Liu, Y.; Kravtsov, V. C.; Larsen, R.; Eddaoudi, M. Molecular Building Blocks Approach to the Assembly of Zeolite-Like Metal-Organic Frameworks (ZMOFs) with Extra-Large Cavities. Chem. Commun. 2006, 1488–1490. (46) Lin, C. Z-J.; Chui, S. S-Y.; Lo, S. M-F.; Shek, F. L-Y.; Wu, M.; Suwinska, K.; Lipkowski, J.; Williams, I. D. Physical Stability vs. Chemical Lability in Microporous Metal Coordination Polymers: A Comparison of [Cu(OH)(INA)]n and [Cu(INA)2]n: INA = 1,4(NC5H4CO2). Chem. Commun. 2002, 1642–1643. (47) De Muro, I. G.; Insausti, M.; Lezama, L.; Pizarro, J. L.; Arriortua, M. I.; Rojo, T. Structural, Spectroscopic, Magnetic and Thermal Properties in the [SrM(C3H2O4)2(H2O)5] · 2 H2O (M = Mn, Fe, Co, Ni) System: Precursors of SrMO3–x Mixed Oxides. Eur. J. Inorg. Chem. 1999, 935–943. (48) Banerjee, D.; Zhang, Z.; Plonka, A. M.; Li, J.; Parise, J. B. A Calcium Coordination Framework Having Permanent Porosity and High CO2/N2 Selectivity. Cryst. Growth. Des. 2012, 12, 2162–2165.
ACS Paragon Plus Environment
20
Page 21 of 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Langmuir
(49) Cavka, J. H.; Jakobsen, S.; Olsbye, U.; Guillou, N.; Lamberti, C.; Bordiga ,S.; Lillerud, K. P. A New Zirconium Inorganic Building Brick Forming Metal Organic Frameworks with Exceptional Stability. J. Am. Chem. Soc. 2008, 130, 13850–13851. (50) Yang, Q.; Wiersum, A. D.; Llewellyn, P. L.; Guillerm, V.; Serre, C.; Maurin, G. Functionalizing Porous Zirconium Terephthalate UiO-66(Zr) for Natural Gas Upgrading: A Computational Exploration. Chem. Commun., 2011, 47, 9603–9605. (51) Dovesi, R.; Saunders, V. R.; Roetti, R.; Orlando, R.; Zicovich-Wilson, C. M.; Pascale, F.; Civalleri, B.; Doll, K.; Harrison, N. M.; Bush, I. J.; D’Arco, P.; Llunell, M. CRYSTAL09; University of Torino, Torino, 2009.
ACS Paragon Plus Environment
21
Langmuir
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 22 of 22
Table of contents only
ACS Paragon Plus Environment
22
