Postcombustion CO2 Capture in Functionalized Porous Coordination

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Post-Combustion CO Capture in Functionalized Porous Coordination Networks Dr.Ravichandar Babarao, Yuqi Jiang, and Nikhil V. Medhekar J. Phys. Chem. C, Just Accepted Manuscript • Publication Date (Web): 04 Dec 2013 Downloaded from http://pubs.acs.org on December 8, 2013

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The Journal of Physical Chemistry

Post-Combustion CO2 Capture in Functionalized Porous Coordination Networks Ravichandar Babarao§a, Yuqi Jiangb and Nikhil V. Medhekar*b a

b

CSIRO Division of Materials Science and Engineering, Clayton, Victoria, Australia.

Department of Materials Engineering, Monash University, Clayton, Victoria, Australia.

Abstract: Motivated by recent experimental reports of zirconium Porous Coordination Networks (PCNs) [J. Am. Chem. Soc. 2012, 134, 14690-14693], which have demonstrated a good stability and CO2 adsorption capacity, we investigate the influence of flue gas impurities and functional groups on the performance of PCN frameworks in selective CO2 capture. Using a combination of grand canonical Monte Carlo (GCMC) simulations and first principles calculations, we find that O2 and SO2 impurities in flue gas have a negligible influence on CO2 selectivity in all PCN frameworks. However, due to a strong electrostatic interaction between H2O molecules and the framework, CO2 selectivity decreases in all PCN structures in the presence of water impurities in the flue gas. Our studies suggest that the PCN-59 framework can be a good candidate for selective CO2 separation from a pre-dehydrated flue gas mixture.

Keywords: Simulation, Density Functional Theory, Separation and Flue gas, Metal Oxide Frameworks, Porous Coordination Networks

E-mail: § [email protected], * [email protected] 1 ACS Paragon Plus Environment


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1. Introduction In recent years, metal organic frameworks (MOFs) have attracted a great interest for gas adsorption and separation applications, particularly for highly energy-efficient capture of CO2 from other gas mixtures such as the flue gas emitted from coal-fired power plants.1 One of the key parameters used for benchmarking the performance of MOFs in capturing CO2 from a gaseous mixture is their selectivity towards separation of CO2 over the other constituents of the mixture. Consequently, diverse strategies, which range from utilizing constricted pores2 and open metal-sites3,4 to functional groups5,6 and post-synthetic ligand and metal modifications,7,8 are being explored to enhance the CO2 selectivity in MOFs. Among the several possible strategies available to enhance CO2 selectivity, the modification of the chemistry of the porous framework using functional groups offers an attractive pathway.9 Since the chemical environment at the internal surfaces of the pores plays a crucial role in the selective separation of gases, the strength of the interaction of various species in a gaseous mixture with the framework can be effectively tuned via controlling the size and the nature of the functional groups.10,11 Accordingly, several functional groups have been employed to enhance CO2 capacity as well as selectivity, either using predesigned ligands with specific functional group or via post-synthetic modifications.12 For example, studies on several zeolitic imidazolate frameworks reported that asymmetric functional groups and functional groups with –NO2 subgroups exhibit greater CO2 selective adsorption from its binary mixtures.13,14 More recently, Jiang et al. successfully demonstrated grafting of functional groups with varied density to the pore walls of highly stable isoreticular Zr-based MOFs—known as porous coordination networks (PCNs)—using click chemistry, and showed that the presence of azide functional groups can significantly enhance the selectivity of CO2 over N2.15 2 ACS Paragon Plus Environment

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Most of the experimental studies to date investigating the potential of using chemical functionalization for greater CO2 selectivity in MOFs and PCNs have generally obtained only the pure component isotherms to calculate the selectivity of CO2 over N2 using Ideal Adsorbed Solution Theory (IAST).16 However, the typical flue gas is composed of not only CO2 and N2, but also a considerable amount of impurities such as H2O, O2, SO2, NO2, CO and NO.17 It has been suggested that depending on the chemical nature of the impurity and the framework chemistry, flue gas impurities can have a detrimental effect on CO2 selectivity in frameworks that otherwise show an excellent CO2 selectivity over N2.18 Identifying the effect of flue gas impurities on CO2 capture is, therefore, crucial for a complete evaluation of CO2 selectivity performance in porous materials. However, experimental measurement of isotherms of gas mixtures is challenging and is scarcely reported in the literature.19 Consequently, molecular simulations are increasingly being used to complement experiments in predicting mixture isotherms, and in turn to calculate adsorption selectivity for different gas compositions. In this respect, a few modelling studies have successfully investigated the effect of flue gas impurities on CO2 adsorption in Mg, Co and Ni/DOBDC MOFs, Bio-MOF-11, HKUST-1 and ZIF MOFs.18,20,21 These studies have clearly shown that the interactions among various gas molecules are highly specific to the local environments and vary greatly with the chemistry of the framework. Motivated by recent experimental reports on selective separation of CO2 over N2 in chemically and hydrothermally stable Zr-based PCNs,15 here we evaluate their performance in the selective separation of CO2 from the flue gas mixture. We consider four different PCN frameworks, termed as PCN-56, -57, -58 and -59, with varied density of functional groups. These PCN frameworks are characterized by Zr3O2(OH)2 terphenyl-4,4” dicarboxylic acid 3 ACS Paragon Plus Environment


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derived linkers with two methyl, four methyl, two methylazide and four methyl azide functional groups in PCN-56, -57, -58 and -59, respectively (see Figure 1). Using a combination of first principles density functional theory calculations and grand canonical Monte Carlo simulations, we analyze the effect of functional groups as well as flue gas impurities (H2O, O2 and SO2) on the CO2 selectivity. Among the different PCN frameworks considered, we find that PCN-59 shows the highest CO2/N2 selectivity. However, the CO2 selectivity decreases drastically in the presence of H2O impurity in the flue gas, especially in PCN-59 framework. Furthermore, no significant change in CO2 selectivity was observed in the presence of O2 and SO2 impurities. Our analysis of the binding energy and radial distributions functions reveals that the observed reduction in CO2 selectivity in the presence of water impurities can be attributed to strong electrostatic interactions between the H2O molecules and the PCN frameworks.

2. Models 2.1. PCN structures. The crystal structures for PCN-56, -57 and -58 were obtained from the available experimental crystallographic data.15 Crystal structures from the experiments included site double occupancy, and therefore were refined and optimized further using first principles calculations to obtain the lowest energy structures. Our first principles calculations were performed using density functional theory (DFT) as implemented in software package VASP.22 Electron exchange and correlation were described using the generalized gradient approximation form and the projector-augmented wave potentials were used to treat core and valence electrons.23 In all cases, we used a plane-wave kinetic energy cutoff of 500 eV and a Gammapoint mesh for sampling the Brillouin zone. The supercell vectors as well as all the ionic coordinates were relaxed until the Hellman-Feynman ionic forces were less than 0.02 eV/Å. The

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optimized lattice parameters for PCN-56, -57, -58 as obtained from our DFT calculations are in good agreement with experimental measurements (see Table S1).

Since the experimental

crystallographic data for PCN-59 is not available, the crystal structure for PCN-59 was built using the structure of PCN-58 and re-optimized using DFT methods. Figure 1 shows the relaxed structure of PCN frameworks considered in this work.

2.2. Atomic partial charge calculation. Since the unit cells of the frameworks considered in our study involve large number of atoms, we used fragmented cluster models cleaved from the unit cells (Fig. S1-S4 in the Supplementary Information) to obtain the partial charges on all atoms of the four PCN frameworks. DFT calculations on the cluster models were performed using PW91 functional along with the Double-ξ numerical polarization (DNP) basis set as implemented in the software package DMol3.24 To ensure that all the atoms of the fragmented cluster are in the same electronic states as that in the periodic unit cell, the cleaved bonds at the boundaries of the cluster were saturated by methyl groups. The atomic charges were then obtained from the electrostatic potential using the Merz-Kollman (MK) scheme.25 Table S2-S5 in SI lists the obtained atomic charges for all the PCN frameworks considered in this study.

2.3. Adsorbate. The CO2, N2, O2 and SO2 adsorbate molecules considered in our study were described as three-site rigid molecules.26-28 The H2O molecule was represented by the TIP3P (three-point transferable interaction potential) model.29 Table 1 lists the values for relevant geometric and Lennard-Jones potential parameters as well as the atomic charges for all adsorbate molecules. The gas-PCN and gas-gas interactions were modeled as a combination of pairwise Lennard-Jones and Coulombic potentials. Zr atoms in the PCN framework were described using 5 ACS Paragon Plus Environment


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the Universal force field,30 while the potential parameters for rest of the framework atoms are adopted from the Dreiding force field.31 The cross Lennard-Jones parameters were evaluated by the Lorentz-Berthelot combining rules. All the Lennard-Jones parameters were rescaled to match the experimental isotherms of pure CO2. This methodology for rescaling of the force field parameters is consistent with earlier works on simulating the isotherms for CO2, CH4 and N2 in functionalized UiO-66,32 Ti-exchanged UiO-668 and zeolitic imidazolate frameworks.33

2.4. Binding energy calculations. It is known that the generalized gradient approximation of the DFT methods does not take into account any weak dispersion interactions.34 Therefore, the static binding energies for weakly-bound adsorbate molecules in the PCN framework were computed using dispersion-corrected semi-empirical DFT-D2 method.35 The initial locations of the abdsorbate molecules in the primitive cell of framework were obtained using the classical simulated annealing technique. Static binding energies (∆E) at 0 K were calculated using the where Ex refers, respectively, to the total

expression

energies of the PCN+adsorbate complex, the PCN structure and an isolated adsorbate molecule. With this definition, negative adsorption energy denotes an exothermic reaction, where it is energetically favorable for the gas molecule to adsorb in the PCN framework. Finally, in order to highlight the importance of considering dispersion interactions, we also calculated the binding energies in the absence these interactions.

3.0. Simulation Methodology The adsorption of pure CO2 and mixture isotherms were simulated using the grand canonical Monte Carlo (GCMC) method. GCMC simulations are widely used to provide qualitative 6 ACS Paragon Plus Environment

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information on screening porous structures for a range of applications, including gas adsorption and separation for CO2 capture.36 While these methods are known to be generally accurate in systems where adsorbate molecules are physisorbed within the porous structures, it is worth noting that they may underestimate uptake at very low pressures for adsorbates that bind very strongly with the porous structure (for example, chemisorption).18 To study the effect of flue gas impurities on CO2 adsorption, we considered two, three and four component mixtures representing a typical flue gas composition, namely, CO2/N2, CO2/N2/O2, CO2/N2/H2O and CO2/N2/H2O/SO2 with bulk composition of 15:85, 15:81:4, 15:80:5 and 15:79.9:5:0.1, respectively. We kept the framework atoms frozen during simulation since the flexibility of framework has only a marginal effect on the low-energy configurations for the adsorption of small gas molecules in the framework.36 The Lennard-Jones interactions were evaluated with a spherical cutoff equal to half of the simulation box with long-range corrections, while the coulombic interactions were calculated using the Ewald summation method. In a typical GCMC simulation, we used 2×107 trial moves. The first 107 moves were used for equilibration and the subsequent 107 moves were used for calculating the ensemble averages. Where necessary, we used additional trial moves, especially for higher loadings. In all our GCMC simulations, we considered five types of trial moves, namely, (1) displacement, (2) rotation and partial regrowth at a neighboring position, (3) entire regrowth at a new position, (4) swap with reservoir, and (5) exchange of molecular identity. Unless otherwise mentioned, the uncertainties are smaller than the symbol sizes in the figures presented. The free volume V free available for the adsorption of gas molecules in the PCN frameworks

[

]

He He is estimated from the expression V free = ∫ exp − u ad (r ) / k B T dr , where u ad is the interaction V

energy between Helium and the framework with σ He = 2.58 Å and ε He / k B =10.22 K.37 The ratio 7 ACS Paragon Plus Environment


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of free volume V free to the total volume Vtotal gives the porosity φ of adsorbent. The accessible surface area is calculated based on the probe diameter of 3.3 Å. The values for the accessible surface area, free volume, the maximum and pore limiting diameters for each structure considered in this study are obtained using the program Poreblazer_v.3.0.2.38 Table 2 also shows a comparison between the computed BET accessible surface areas with the experimental measurements. The predictions are in reasonably good agreement with measurements for PCN56, and PCN-57 structures, while the predictions slightly overestimate the accessible surface area in PCN-58 and PCN-59 structures. This difference can be attributed to crystal imperfections and presence of solvent molecules inside the pores in these structures.

4.0 Results and Discussion. 4.1 Structural features. Figure 1 shows the schematic representation of the unit cell of each PCN framework. These structures are characterized by two distinct topological cages, namely, a tetrahedral-shaped cage and an octahedral-shaped cage (see Figure 2). It can be observed that in general as the framework structure changes from PCN-56 to PCN-59 upon addition of different functional groups to the linkers, the pore volume available for adsorption of gases in both tetrahedral and octahedral cages gradually reduces. We have quantified these changes in the framework structure that are relevant for gas adsorption by computing the framework density ρ f , free volume Vfree , porosity φ and accessible surface area. The values of these parameters are tabulated in Table 2. It is clear that the addition of functional groups to linkers leads to a gradual reduction of available pore volume and maximum pore diameter from PCN-56 to PCN-59 structure, while increasing the density of the porous framework. 8 ACS Paragon Plus Environment

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4.2. Adsorption isotherms of CO2. Next, we discuss the simulated adsorption isotherms of CO2 and compare them with the available experimental data. As shown in Figure 3, simulated adsorption isotherms at both 273 K and 296 K are in a reasonably good agreement with experimental results,15 thus validating the accuracy of force field parameters employed and used in subsequent calculations. We find that the predicted CO2 adsorption capacity in four PCN frameworks varies in a narrow range of 26 cm3/g to 40 cm3/g at 296 K and 1 bar pressure. These values for CO2 capacities in PCN frameworks are very similar to those observed in unfunctionalized UiO-66(Zr) at similar operating conditions.8,39 To identify the preferential CO2 adsorption sites, we next calculated radial distribution functions (RDFs) between the center of mass of CO2 molecules and specific framework atoms at 296 K and 10 kPa pressure. As shown in Figure 4, a pronounced peak in the RDFs between CO2 molecules and the hydrogen atom of the hydroxyl group in both PCN-56 and PCN-57 structures is observed at a distance of ~3.5 Å. In contrast, we find that CO2 molecules do not interact strongly with -CH3 groups present in PCN-56 and PCN-57 frameworks. Similarly, the RDFs of PCN-58 and PCN-59 are also characterized by a strong peak between CO2 and the hydrogen atom of the hydroxyl group in the framework. This behavior suggests that irrespective of the functional groups present in the framework, hydroxyl groups provide strongest adsorption sites for CO2 molecules, particularly at low pressure. This observation is also consistent with Yang et al. study where similar distinct peaks in the RDFs between CO2 molecule and hydroxyl groups attached in unfunctionalized and functionalized UiO-66 (Zr) MOFs were observed.40 To quantify the binding of CO2 molecules with hydroxyl groups of the framework, we further performed static binding energy calculations using dispersion-corrected first principles



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DFT methods as discussed in Section 2.4. Figure 5 illustrates the optimized locations of a single CO2 molecule in a cleaved cluster model for all PCN frameworks considered in this study. Consistent with the observations of RDFs (Figure 4), our calculations show a strong interaction between the CO2 molecule and the PCN framework, with the oxygen atom of the CO2 molecule located at a distance of 2.1- 2.3Å from the hydrogen atom of the hydroxyl group. While the adsorption distances shown in Figure 5 are marginally smaller than those in the radial distribution functions in Figure 4, both density functional theory calculations and GCMC simulations provide a consistent description of the binding configurations of CO2 molecules. For PCN-56, -57, and -58 frameworks, we obtained static binding energies for CO2 molecules in the range of -29 kJ/mol to -31.0 kJ/mol. These values for CO2 binding energies are consistent with the value -29.9 KJ/mol reported for CO2 in UiO-66 MOFs with similar topologies as PCN frameworks.41 In contrast, CO2 binding energy in PCN-59 is much higher (-36.3 kJ/mol), indicating a stronger binding. The observed stronger interactions between CO2 and the PCN-59 framework can be attributed to the presence of high-density azide groups attached to the linkers. We find that the chemical interactions between CO2 molecules and all PCN structures are dominated by weak dispersion interactions. Our calculations of binding energies of CO2 molecules in the absence of dispersion interactions (see Table S6 in Supporting Information) indicate that a large fraction (grater than 75%) of the binding energy is contributed by the dispersion interactions. This observation is consistent with earlier studies,42 and serves to highlight the importance of correctly accounting for these weak interactions in density functional theory calculations of physically adsorbed gas molecules in porous materials. In order to facilitate a comparison between static binding energies at 0 K obtained from our DFT calculations and the experimental values of the binding energies measured at room


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temperature, we have also estimated the enthalpy of binding by accounting for the zero-point and finite temperature corrections to calculated values of the static binding energies. A recent study has reported that the magnitude of these corrections to the binding energies calculated using DFT-D2 methods usually vary within a range of 2–4 kJ/mol.42 Consequently, using a correction of 4 kJ/mol we obtained the enthalpy of CO2 adsorption in PCN frameworks. As shown in Table 3, our calculated values for enthalpy of CO2 adsorption are in a good qualitative agreement with experimental measurements.15

4.3. Adsorption of Binary Mixtures. Next we investigated the adsorption isotherms for CO2/N2 binary mixtures with bulk composition 15:85, a typical flue gas composition ignoring other minor impurities. The selective separation of CO2 over N2 in the CO2/N2 mixture is quantified by the parameter selectivity, defined as Si / j = ( xi / x j )( y j / yi ) , where xi and yi is the mole fraction of component i in adsorbed and bulk phases, respectively. Figure 6 shows the adsorption selectivity of CO2 in the CO2/N2 mixture as a function of pressure for different PCN frameworks. It can be observed that for both PCN-56 and PCN-57 frameworks, the selectivity remains approximately constant for the pressure range considered in this study. Due to the asymmetric CH3 group in PCN-56 framework, the CO2 uptake is higher in PCN-56 than in PCN-57 framework, and consequently, the predicted CO2 selectivity is also slightly higher (~14 in PCN56) than that observed in PCN-57 (~10). A similar observation has been recently reported in zeolitic imidazolate frameworks where the asymmetry in the functionalization was shown to result in higher CO2 selectivity.43 Due to high density of azide groups in PCN-59, the CO2 selectivity is significantly larger than in other PCN frameworks. This observed increase in the CO2 selectivity in PCN-59 framework is attributed to the strong electrostatic interactions 11 ACS Paragon Plus Environment


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between CO2 and framework atoms that lead to a large negative value of CO2 adsorption enthalpy as shown in Table 3. The predicted value of CO2 selectivity in PCN-59 framework at 298 K and 1 bar is approximately 32, which is higher than in UiO-66(Zr) MOF (~25) but lower than in the functionalized UiO-66(Zr) MOF (~80).44 It is evident from Figures 3 and 6 that the strong electrostatic interactions between the CO2 molecules and the PCN frameworks are responsible for increased CO2 uptake and selectivity, especially in the PCN-59 framework. Earlier works have suggested that the presence of narrow pore size and high-density functional groups both result in an enhanced CO2 uptake and selectivity.8,45,46 In order to identify the competing influences of the pore size and the electrostatic interactions on CO2 selectivity, we calculated the CO2 selectivity by switching off the electrostatic interactions between the CO2 molecules and the framework atoms (see Figure 6). In the absence of the electrostatic interactions, the CO2 selectivity is very low (in the range of 2-3) irrespective of the type of the framework. Thus, the electrostatic interactions between the gas molecules and the PCN frameworks, rather than the pore size, play a dominant role in controlling the CO2 selectivity, particularly at low pressures.

4.4 Effect of flue gas impurities. Next, we investigated the effect of flue gas impurities on the performance of PCN frameworks in selective CO2 capture. We considered small amounts of O2, H2O and SO2 impurities typically found in flue gases and evaluated the CO2/N2 selectivity in the presence of these impurities.17 Moreover, for completeness, we have also computed the strength of interactions between the impurity molecules and the PCN frameworks using dispersion corrected density functional theory methods (see Table 4). Figure 7 shows the adsorption selectivity of CO2 over N2 in a CO2/N2/O2 mixture with a bulk composition of 15:81:4, compared 12 ACS Paragon Plus Environment

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with the selectivity in a 15:85 CO2/N2 mixture. It is clear that irrespective of the type of the framework, oxygen impurities have negligible effect on CO2 selectivity. This behavior is also consistent with the work of Yu et al., where oxygen impurities were reported to have no influence on the CO2 selectivity in Cu-BTC MOFs.21 Earlier studies on gas adsorption in various MOFs have reported a widely ranging influence of H2O impurity on selective CO2 adsorption. For instance, in rho-ZMOFs,47 even a tiny amount of H2O impurity (less than 0.1 wt %) was reported to have a detrimental effect on the CO2 selectivity, while in fully hydrated Cu-MOFs, a large concentration of H2O (greater than 5 wt %) led to an enhanced CO2/N2 selectivity.21 Similarly, a recent study using molecular simulations reported the effect of trace amount of H2O on CO2 capture in a diverse collection of MOFs, with some MOFs showing no effect while others exhibiting a large reduction in CO2 selectivity.48 It is therefore evident that the effect of H2O impurities on CO2 selectivity is highly specific to the chemistry of the framework, and needs to be evaluated on an individual case-by-case basis for all PCN frameworks under consideration. As the flue gas typically contains 5–8 wt% H2O,17 here we studied the effect of H2O on CO2 selectivity with 5% H2O concentration representing a typical flue gas condition. As shown in Figure 8, the effect of water on the predicted CO2/N2 selectivity is similar in PCN-56, PCN-57 and PCN-58 frameworks—the selectivity at 1 bar pressure decreases by magnitude in the range of 6 to 8. In contrast, the effect of water on CO2 selectivity in PCN-59 is much more dramatic, with the CO2/N2 selectivity decreasing from 30 to 8. In order to further investigate the behavior of different PCN frameworks at 5% H2O concentration, we plotted CO2 isotherms in CO2/N2 and CO2/N2/H2O mixtures for a bulk composition of 15:85 and 15:80:5, respectively (Figure 9). It is evident that a significant



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reduction in CO2 uptake is observed in PCN-59 for pressures even below 10 kPa. With increasing pressure, CO2 capacity decreases more drastically in all the PCN frameworks as the partial pressure of H2O in the mixture reaches above the saturation pressure of H2O at room temperature (~3.2 kPa). This observation can be explained by the presence of large number of azide groups (-CH2N3) groups in PCN-59 framework. These groups lend PCN-59 framework a stronger hydrophilic character than the other PCN frameworks, and hence H2O condenses more readily in PCN-59 and in turn reduces the CO2 capacity and selectivity. We have confirmed this hypothesis by calculating static binding energies of H2O molecules in all PCN frameworks using dispersion-corrected DFT methods (see Figure 10). Indeed, the binding energies for H2O molecule in PCN-56, PCN-57 and PCN-58 are similar (~ -46 kJ/mol), while the binding energy in PCN-59 is approximately 6 kJ/mol higher (~ -52 kJ/mol), indicating a stronger interaction with the PCN-59 framework. This observation is further validated by analyzing the structural correlations between CO2 and H2O molecules with the hydroxyl group in all four PCN frameworks. Figure 11 shows the radial distribution function between CO2 and H2O molecules with hydroxyl group in a CO2/N2/H2O mixture with a bulk composition of 15:80:5. Both PCN-56 and PCN-57 frameworks are characterized by a distinct peak between CO2 and hydroxyl group as well as between H2O and hydroxyl group indicating a competing adsorption of CO2 and H2O in the framework (Figure 11a and 11b). In contrast, for PCN-59 structure, a sharp peak is observed in the radial distribution function between H2O and hydroxyl group, whereas a peak between CO2 and hydroxyl group is absent, again signifying the stronger interaction of H2O molecules with the PCN-59 framework.


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Finally, we have also analyzed the effects of SO2 impurities on CO2 capture in PCN frameworks for CO2/N2/H2O/SO2 mixture with a bulk composition 15:79.9:5:0.1. Figure 12 presents a comparison of the predicted CO2 selectivity in the CO2/N2/H2O/SO2 mixture with the selectivity in a 15:85 CO2/N2 mixture. It is clear that SO2 impurities do not lead to any significant changes in the selectivity in both PCN-58 and PCN-59 frameworks, while in PCN-56 and PCN-57 frameworks, SO2 impurities result in a very small increase in the selectivity at very low pressures. This observed marginal effect of SO2 impurities on the selective uptake of the CO2 in the flue gas mixture is expected due to very low concentrations of SO2. This behavior is also consistent with an earlier report on Cu-BTC MOFs where SO2 impurities were shown to have a negligible effect on the CO2 selectivity.21



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5.0 Conclusions In summary, using a combination of first principles calculations and Monte Carlo simulations, we have investigated the influence of functional groups and flue gas impurities on the postcombustion CO2 capture in Zr-based PCN-56, -57, -58 and PCN-59 frameworks. We obtained a reasonably good agreement between the simulation results and experimental measurements of the adsorption isotherms of CO2 in all PCN frameworks considered in this study. Radial distribution functions between CO2 and framework atoms show that the hydroxyl group in all PCN frameworks provides the strongest binding site for CO2 molecules. For two-component CO2/N2 mixture with a bulk composition of 15:85, PCN-59 structure showed the highest selectivity among the various PCN frameworks, consistent with the strong electrostatic interactions between CO2 and the framework. Furthermore, we also analyzed the influence of H2O, O2 and SO2 impurities in the flue gas on CO2 capture performance in PCN frameworks. Among the various impurities, only the water impurity adversely affects CO2 selectivity in all PCN frameworks, and particularly so in the PCN-59 framework due to a stronger electrostatic interaction between the framework and water molecules. In light of these results, our study highlights the potential of PCN-59 framework for selective post-combustion CO2 separation from the flue gas mixture that is free of any water impurities.


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Acknowledgement. RB gratefully acknowledges the support from the Science and Industry Endowment Fund from the Government of Australia. Authors also acknowledge computational support from CSIRO Burnet Server, Monash Sun Grid, MASSIVE, IVEC and the National Computing Infrastructure.

Supporting Information Available: Optimized lattice parameters of PCN framework, Atomic partial charges for the framework atoms, and binding energies of flue gas impurities without including dispersion correction. This material is available free of charge via the Internet at http://pubs.acs.org.



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Figure 1. Optimized unit cells of Zr-based porous coordination networks (PCNs). Hydrogen atoms are omitted for clarity. Color code for atoms: Zr, violet; C, grey; O, red; N, blue.


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PCN-56

PCN-57

PCN-58

PCN-59

Figure 2. Tetrahedral (top) and octahedral cages (bottom) comprising zirconium clusters and organic linkers in PCN frameworks. Hydrogen atoms are omitted for clarity. Color code for atoms: Zr, Violet; C, grey; O, red; N, blue. The large colored spheres represent the largest cavity that can fit inside the pores.



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Figure 3. Adsorption isotherms of CO2 molecules at 273 K and 296 K in four PCN frameworks considered in this study. Closed symbols denote experimentally measured values,15 while the solid lines indicate predicted values.


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Figure 4. Radial distribution function g(r) computed between the centre of mass of CO2 molecules and various framework atoms at 296 K and 0.1 bar pressure.



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PCN-56

PCN-57

∆E = -29.9 kJ/mol

PCN-58

Page 28 of 40

∆E = -31.0 kJ/mol

PCN-59

∆E = -31.1 kJ/mol

∆E = -36.3 kJ/mol

Figure 5. DFT-D2 optimized locations of an isolated CO2 molecule in PCN frameworks. In each case, the distance between the oxygen atom of the CO2 molecule and the H atom of the hydroxyl group of the framework (in Å) is also shown. ∆E denotes the binding energy of the CO2 molecule.


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Figure 6. Adsorption selectivity of CO2 over N2 in PCN frameworks for 15:85 CO2/N2 mixture at 298 K (open symbols). Closed symbols denote the predicted selectivity values in the absence of framework charges.



CO2 - N2 (PCN-56) CO2 - N2 - O2 (PCN-56) CO2 - N2 (PCN-57) CO2 - N2 - O2 (PCN-57)

selectivity CO2/N2

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

P (kPa)

Page 30 of 40

CO2 - N2 (PCN-58) CO2 - N2 - O2 (PCN-58) CO2 - N2 (PCN-59) CO2 - N2 - O2 (PCN-59)

P (kPa)

Figure 7. Effect of oxygen impurity on the adsorption selectivity of CO2 over N2 for a 15:81:4 CO2/N2/O2 mixture at 298 K. The selectivity of CO2 over N2 for a 15:85 CO2/N2 mixture is also shown for a comparison.


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Figure 8. Effect of H2O on the adsorption selectivity of CO2 over N2 for a 15:80:5 CO2/N2/H2O mixture at 298 K.



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Figure 9. Adsorption isotherms of CO2 in 15:85 CO2/N2 and 15:80:5 CO2/N2/H2O mixtures at 298 K.


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Figure 10. DFT-D2 optimized locations of an isolated H2O molecule in PCN frameworks. The distance between the O atom of the H2O molecule and H atom of the hydroxyl group of the framework is also shown. ∆E denotes the binding energy of the H2O molecule.



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Figure 11. Radial distribution function g(r) computed between the center of mass of CO2 and H2O molecules with the hydrogen atom of the OH group in the PCN framework for 15:80:5 CO2/N2/H2O mixtures at 298 K and 0.1 bar pressure.


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Figure 12. Effect of SO2 impurities on the adsorption selectivity of CO2 over N2 in PCN frameworks for 15:80:5 CO2/N2/H2Oand 15:79.9:5:0.1 CO2/N2/H2O/SO2 mixtures at 298 K.



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Table 1. Force field parameters for various adsorbates, COM indicates the center of mass of the adsorbate molecule. adsorbate CO2

site

σ (A)

ε / k B (K)

q (e-)

dCO = 1.18 Å

C

2.79

29.66

0.576

O

3.01

82.96

−0.288

N

3.32

36.40

−0.482

COM

0.00

0.00

0.964

O

3.05

54.40

-0.112

COM

0.00

0.00

0.224

dSO = 1.43 Å

S

3.62

145.90

0.471

∠OSO = 119.5°

O

3.01

57.40

-0.235

dOH = 0.9572 Å

O

3.15

76.47

-0.834

∠HOH = 104.52°

H

0.00

0.00

0.417

dNN = 1.10 Å N2

dOO = 1.21 Å O2 SO2

H2O

o

geometry


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Table 2. Framework density ρf, free volume at room temperature Vfree, porosity Ø, pore limiting and maximum pore diameter, and accessible surface area.

a

Ø

pore limiting diameter (Å)

maximum pore diameter (Å)

BET surface area (m2/g)a

1.57

0.79

6.65

16.82

4153 (3741)

0.548

1.36

0.74

5.12

17.46

2884 (2572)

PCN-58

0.591

1.21

0.71

5.64

13.92

2967 (2185)

PCN-59

0.715

0.85

0.61

4.44

12.55

1814 (1279)

ρf

PCN

(g/cm3)

Vfree (cm3/g)

PCN-56

0.501

PCN-57

values in parentheses indicate experimentally determined BET surface area.15



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Table 3. Comparison of calculated adsorption energies (in kJ/mol) of a CO2 molecule in PCN frameworks with experimental data.a

a

PCN

b

from experiments

PCN-56

-29.90

-25.90

-20

PCN-57

-31.00

-27.00

-22

PCN-58

-31.06

-27.06

-24

PCN-59

-36.30

-32.30

-32

a

refers to the static 0 K adsorption energy, b is the adsorption enthalpy including zero-point and finite temperature contribution of 4 kJ/mol. Adsorption enthalpy from experiments are obtained from the measured heat of adsorption at low CO2 coverage.15


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Table 4. Calculated adsorption energies (in kJ/mol) of various flue gas molecules in PCN frameworks using dispersion-corrected density functional theory.

PCN-56

N2 -34.152

O2 -38.540

∆Ea H2O -46.403

PCN-57

-24.831

-28.911

-45.815

-51.744

PCN-58

-36.969

-45.130

-46.143

-61.479

PCN-59

-46.598

-60.144

-52.463

-81.577

PCN

a

SO2 -52.381

∆E refers to the static 0 K adsorption energy



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Page 40 of 40

TABLE OF CONTENT GRAPHIC

1.870 Å

2.282 Å

C O Zr N H Binding Energy = - 32.30 kJ/mol

Binding Energy = -52.46 kJ/mol


Postcombustion CO2 Capture in Functionalized Porous Coordination

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