Ionic-Functionalized Polymers of Intrinsic Microporosity for Gas

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Ionic-Functionalized Polymers of Intrinsic Microporosity for Gas Separation Applications Shalini J. Rukmani,† Thilanga P. Liyana-Arachchi,‡ Kyle E. Hart,§ and Coray M. Colina*,†,‡ †

Department of Materials Science and Engineering and ‡Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States § Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States S Supporting Information *

ABSTRACT: Ionic-functionalized microporous materials are attractive for energy-efficient gas adsorption and separation processes and have shown promising results in gas mixtures at pressure ranges and compositions that are relevant for industrial applications. In this work, we studied the influence of different counterions (Li+, Na+, K+, Rb+, and Mg2+) on the porosity, carbon dioxide (CO2) gas adsorption, and selectivity in ionic-functionalized PIM-1 (IonomIMs), a polymer belonging to the class of linear and amorphous microporous polymers known as polymers of intrinsic microporosity (PIMs). It was found that an increase in the concentration of ionic groups led to a decrease in the free volume, resulting in a less porous polymer framework, and Mg2+-functionalized IonomIMs exhibited a relatively larger porosity compared to other IonomIMs. The CO2 adsorption capacity was affected by the different counterions for IonomIM-1, and a higher loading capacity for pure CO2 was observed for Mg2+. Furthermore, the IonomIMs showed an enhanced CO2 selectivity in CO2/CH4 and CO2/ N2 gas mixtures at conditions used in pressure swing adsorption and vacuum swing adsorption applications. It was also observed that the concentration of ionic groups plays a vital role in changing the CO2 gas adsorption and selectivity. counterions in MOFs,12,13 zeolites,14,15 and polymers,16,17 where they are demonstrated to have an enhanced adsorption and separation performance as compared to nonionic moieties. It has also been shown that the size and charge of counterions, pore size, and concentration of ions are some of the factors that play a significant role in the tunability of these materials for gas adsorption and separation applications.8,18,19 Molecular simulations (MS) are valuable tools in predicting the structure−property relationship of solid adsorbents. Several groups have contributed significantly in designing materials with high CO2 adsorption capacity and selectivity over other gases (such as N2 and CH4).20,21 Combined experimental and molecular modeling approaches have also been effectively employed to screen hypothetical MOFs with high CO2 uptake.22 With respect to ionic-functionalized frameworks, the first MS were performed by Babarao et al.23 in 2009 on a positively charged soc-MOF framework with NO3− anions. The predicted CO2/CH4 adsorption selectivity was calculated by S = (xi/xj)(yj/yi) (where xi and yi are the mole fractions of component i in the adsorbed and bulk phases, respectively) at 298 K and 1 bar. The selectivity for the charged soc-MOF was 27, an order of magnitude higher than those of other nonionic

1. INTRODUCTION Carbon dioxide (CO2) capture and sequestration1 technology plays a pivotal role in tackling climate change as we still rely heavily on power supply from fossil fuels which release a significant amount of carbon dioxide (CO2) gas, a major contributor to global warming.1,2 Three of the most abundant types of CO2 capture are (i) postcombustion, (ii) precombustion, and (iii) oxy-fuel combustion.1 To this end, a myriad of carbon capture materials have been developed for energyefficient CO2 gas separation processes, out of which solid adsorbents is an area that is widely studied. Various solid adsorbents3,4 have been developed over the years ranging from the traditionally used activated carbons, carbon molecular sieves, and zeolites to more recently developed metal−organic frameworks (MOFs),5 covalent organic frameworks,6 polymers of intrinsic microporosity (PIMs),7 porous organic polymers (POPs),8 porous aromatic frameworks (PAFs),9 and conjugated microporous polymers.10 One of the major challenges in microporous materials is to design a framework with high gas selectivity so as to have a high solubility of the gas to be separated out while decreasing the solubility of other gases.1,11 One of the ways to achieve this criterion for CO2 separation is by incorporating ionic functional groups into the framework, thereby making it attractive for CO2 gas adsorption.2,3,11 Several experimental studies have been done on the synthesis of structures with extra framework © XXXX American Chemical Society

Received: December 21, 2017 Revised: February 22, 2018

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DOI: 10.1021/acs.langmuir.7b04320 Langmuir XXXX, XXX, XXX−XXX

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Figure 1. Molecular structures of (a) PIM-1, (b) carboxy-PIM-1, (c) IonomIM-1 (100% ionic concentration), and (d) co-IonomIM-1-x % (x % IonomIM-1 and 1-x % PIM-1). Here, M represents the metal counterions (1c and 1d). There are two counterions present per monomer for monovalent ions (Li+, Na+, K+, and Rb+) and one counterion per monomer in the case of divalent Mg2+ to preserve charge neutrality.

The motivation for this work comes from the tunable porosity and gas adsorption properties observed in other microporous12,16,18,34 materials due to the change in size/ charge of counterions, thereby optimizing the framework−CO2 interactions to tailor the properties for specific gas separation applications. Here, we present the porosity and structural properties and evaluate the gas adsorption and separation performance for IonomIMs with different counterions (Na+, Li+, K+, Rb+, and Mg2+). Insights into different factors that influence the adsorption and mixed gas separation performance, including pore size, counterion size and charge, and concentration of ions, which help in designing energy-efficient ionic-functionalized materials for gas separation applications, are also provided. The rest of the article is organized as follows. In section 2, the details of our computational methodology are described. In section 3, the results for porosity and gas adsorption properties are discussed, and finally the conclusions are presented in section 4.

MOFs such as IRMOFs, Cu-BTCs, and PCNs (S = 2.5 to 4.5). Subsequently, MS were also performed on anionic rho-ZMOFs with metal cations.24,25 Chen et al.26 performed simulations on rho-ZMOFs with mono-, di-, and trivalent extra framework cations and evaluated their CO2 gas loading and selectivity in CO2/H2 gas mixtures. The highest selectivity values observed were between 800 and 3000 for the di- and trivalent cations, with the isosteric heat of adsorption of CO2 being 84.42 and 89.91 kJ/mol, respectively, at 298 K. In short, the adsorption and selectivity of these ordered systems could be tuned by changing the size and charge of the counterions in these systems. PIMs7 are a class of amorphous microporous polymers having rigid contorted backbone units that pack inefficiently, which results in a significant amount of free volume. There are several advantages of PIMs: versatile structures that can be synthesized simply by the choice of monomer precursors,7,27,28 ease of chemical functionalization of the monomers, chemical and thermal stability,29,30 and solution processability of linear PIMs.7 When the nitrile functionality in PIM-1 was converted to carboxylic acid groups, the resulting structure of carboxy PIM-1 showed reduced porosity and improved CO2 gas selectivity compared to PIM-1 as shown by Hart and Colina.31 Similar trends were shown experimentally by Guiver’s group32,33 and were attributed to the stronger intermolecular interactions in the framework. This was an important step from the synthetic point of view to subsequently get an ionicfunctionalized backbone with COO−M+ groups, where M is the cation. Ionic functionalization of PIMs with a carboxylate backbone and extra framework metal counterions (Na+), known as IonomIMs, resulted in an enhanced CO2 adsorption performance as compared to PIM-1 from MS by Hart and Colina.31 In their work, it was shown that the incorporation of Na+ ions enhanced the CO2 separation performance in CO2/ CH4 gas mixtures because of the increased strength of Na+− CO2 interactions and the ability to maintain the pore sizes required for effective adsorption and separation of CO2 from CH4. The selectivity of IonomIMs was predicted to be much higher than those of MOFs, POPs, and zeolites. Recently, Zhao et al.17 synthesized a series of postmodified PIM-1 membranes with Na+, Mg2+, Ca2+, and Al3+ ions. They studied the effect of changing the ions on the ideal gas selectivity of O2/N2, CO2/ N2, and CO2/CH4 mixtures and found that the selectivity coefficient increased from 13.5 for monovalent Na+ to 30.8 for trivalent Al3+ under the same conditions of hydrolysis.

2. COMPUTATIONAL METHODOLOGY 2.1. Molecular Models. Bonded and nonbonded interactions in the polymer framework were modeled using the generalized amber force field35 and the united atom transferable potential for phase equilibria,36,37 respectively. The universal force field (UFF)38 was chosen to model the counterions (Li+, Na+, K+, Rb+, and Mg2+) after validating the results for Na+ with the DREIDING39 force field from our previous work31 (see Supporting Information Table S4 and Figure S2). The partial charges for the polymer framework were derived using ab initio calculations at the HF/6-31G* level, followed by a restrained electrostatic potential charge-fitting procedure.31,40 MP2/631G* method was used for structure optimization. Following the previous work by Hart and Colina,31 the monomer used in the ab initio calculation in this study was defined as the repeating structure between two spirocenters (see Figure S1) with capping methyl groups placed on the carbon number 15, allowing to obtain electroneutrality for a repeating unit of the polymer. Partial charges for the polymer framework atoms and the counterions are given in the Supporting Information (see Tables S1 and S2). The TraPPE36,41 force field was used to model CO2, CH4, and N2 gas molecules. The molecular models used in this study were developed by Hart and Colina31 and are also publicly available on our group website (https://colina. chem.ufl.edu/research/force-field-database). B


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Langmuir 2.2. Structure Generation. The open source code Polymatic42,43 was used to create the polymer in a two-step fashion: (1) packing and polymerizing monomers at a low density between 0.2 and 0.4 g/cm3 and (2) subjecting it to a compression/decompression scheme to obtain realistic densities. Two ionic concentrations were chosen (100 and 17%) in this study based on the conclusions presented in our previous work.31 For 100% ionic concentration, 150 monomers of IonomIM-1 were packed in a periodic simulation box (see Figure 1). In the case of the random copolymer with 17% ionic concentration, 25 monomers of IonomIM-1 and 125 monomers of PIM-1 were used. During the polymerization steps, “reactive atoms” on neighboring monomers within a cutoff distance of 7 Å were subjected to successive steps of artificial bonding.42,43 During the polymerization step, artificial charges of ±0.5e were used for chain ends to accelerate the polymerization process. Next, the 21-step molecular dynamics (MD) compression/ decompression simulation scheme was used to obtain an equilibrated density at 300 K and 1 bar (the scheme is presented in Table S3).42−44 This scheme systematically compresses and relaxes the low-density polymer by applying successive higher external temperatures and pressures (Tmax = 1000 K and Pmax = 5 × 104 bar), followed by a stepwise decompression to the desired conditions (Tfinal = 300 K and Pfinal = 1 bar). The long relaxation time of high-Tg polymers makes it challenging to obtain an equilibrated structure close to experimental densities. Hofmann et al.45 presented a scheme consisting of NVT and NPT MD simulations employing high temperatures and pressures. However, a slow decompression was suggested for this method following the abrupt pressure changes in the final step that resulted in a structure with higher residual stresses, as observed by Karayiannis et al.46 The 21-step compression/decompression scheme developed by Larsen et al.44 was shown to be effective for achieving equilibration in glassy polymers and obtaining consistent final densities. The particle−particle particle mesh method was used to compute long-range electrostatic interactions. A cutoff distance of 15 Å was used for the nonbonded interactions. LAMMPS47 software package was used to run all MD simulations. The velocity Verlet algorithm was employed with a time step of 1 fs. The results presented are an average of five independent simulations. 2.3. Structure Characterization. Bulk density, surface area, and fractional free volume (FFV) were calculated for the in silico synthesized samples. Bulk density was calculated as the total mass of the polymer over the total volume of the simulation box. The surface area, pore volume for FFV, and geometric pore size distributions (PSDs) were calculated using Pore Blazer.48 Accessible surface area (ASA) of a nitrogen probe was calculated considering the surface enclosed by the center of the probe. FFV was calculated as the difference between the specific volume (Vsp) and the volume occupied by the polymer molecules (FFV = Vsp − 1.3Vw),49 where Vw was geometrically calculated from the van der Waals volume of each atom in the polymer sample. A factor of 1.3 was taken because of the packing density associated with a molecular crystal at absolute zero temperature.50 We also calculated the geometric PSDs, which are the derivative of the cumulative pore volume of the polymer framework with respect to the probe radius. 2.4. Unary and Binary Gas Adsorption Simulations. CO2 adsorption isotherms of IonomIMs were calculated using Gibbs ensemble Monte Carlo (GEMC)51,52 simulations in the

isothermal−isobaric (NPT) ensemble using the Cassandra Monte Carlo (MC) simulation software53 at 294.15 K. The mixed gas separation performance for industrially significant applications in CO2/CH4 and CO2/N2 gas mixtures was studied for four cases, as described by Bae and Snurr.3 All GEMC adsorption simulations were performed with trial moves for particle swapping between two simulation boxes, translation, and rotation of gas molecules, and translation of counterions. The polymer molecules were held rigid during the simulations. The simulations consist of equilibration and production periods with at least 2.5 × 106 and 2.5 × 106 MC steps, respectively.

3. RESULTS AND DISCUSSION The porosity properties of IonomIM-1 and co-IonomIM-1 (17%) are shown in Table 1. In the IonomIM-1 systems, the Table 1. Porosity Characterization of IonomIM-1 with Different Counterionsa polymer

ion

ASA (m2/g)

IonomIM-100%

Li+

264 (47)

IonomIM-100%

Na+

270 (35)

IonomIM-100%

K+

239 (41)

IonomIM-100%

Rb+

235 (32)

IonomIM-100%

Mg2+

379 (46)

co-IonomIM-17%

Li+

co-IonomIM-17%

Na+

649 (139) 726 (98)

co-IonomIM-17%

K+

650 (53)

co-IonomIM-17%

Rb+

co-IonomIM-17%

Mg2+

Carboxy-PIM-1

650 (117) 832 (144) 353 (49)

PIM-1

595 (85)

ρsim (g/cm3) 1.08 (0.02) 1.14 (0.02) 1.16 (0.01) 1.30 (0.02) 1.07 (0.02) 0.92 (0.03) 0.91 (0.02) 0.93 (0.01) 0.95 (0.03) 0.89 (0.01) 1.02 (0.01) 0.93 (0.02)

FFV % 17.1 (1.2) 17.4 (1.3) 16.5 (0.8) 17.9 (1.5) 19.6 (1.3) 26.2 (2.9) 27.4 (1.7) 26.1 (1.2) 26.4 (2.3) 28.9 (2.5) 20.8 (0.8) 24.3 (1.3)

Ecoul/Evdw 4.12 (0.02) 3.88 (0.05) 3.10 (0.04) 2.89 (0.02) 4.57 (0.02) 0.43 (0.01) 0.36 (0.02) 0.12 (0.01) 0.05 (0.01) 0.82 (0.01) 0.16 (0.01) 1.19 (0.03)

a

Numbers in parentheses represent the standard deviation of the results. ASAaccessible surface area, ρsimsimulation density, FFV %fractional free volume, Ecoul/Evdwabsolute ratio of total Coulombic energy to the van der Waals energy. PIM-1 and carboxyPIM-1 results are from ref 31.

ASA and FFV decreased and the bulk density increased as compared to co-IonomIM-1 (17%). This is attributed to the higher concentration of ions in the former, which leads to stronger polymer−polymer Coulombic interactions, thereby reducing the microporosity. On the other hand, in coIonomIM-1 (17%), the concentration of ionic species is low enough to promote space-inefficient packing to have a higher microporosity closer to that of PIM-1, as shown in Table 1. This was also evident from the absolute ratio of the total Coulombic energy to the van der Waals energy (see Table 1), in which significantly higher values were observed for all IonomIMs. It was also evident that changing the size of counterions (Li+, Na+, K+, and Rb+) with the same charge altered the porosity properties in both IonomIM-1 and coIonomIM-1 (17%) to a lesser degree while displaying a slightly C


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Figure 2. Comparison of the geometric PSDs (dV/dW) of (a) IonomIM-1 and (b) co-IonomIM-1 (17%) with different counterions: black square, Li+; red circle, Na+; blue triangle, K+; pink nabla, Rb+; and green diamond, Mg2+.

Figure 3. Comparison of the RDFs g(r) of oxygen atoms present in the carboxylate groups of (a) IonomIM-1 and (b) co-IonomIM-1 (17%) with different counterions: black dashed lines, Li+; red dashed lines, Na+; blue dashed lines, K+; pink dashed lines, Rb+; and green dashed lines, Mg2+.

higher increase in the average ASA and FFV for polymers with divalent Mg2+. To investigate further, additional properties such as gas adsorption were compared to assess the counterion effect on the overall performance. It is also worthwhile to note at this point that the porosity properties such as Brunauer−Emmett−Teller (BET) surface area, micropore volume, and pore size of PAFs18 decrease with the increase in size of monovalent ions (Li+, Na+, and K+) and increase again with Mg2+. Studies were done by different groups on cation-exchanged Y and X types of zeolites, MCM-22 zeolites, and zeolite beta, and it was shown that increasing the cation size decreased the BET surface area and pore volume, with the exception of Li+, which was attributed to the effect of crystallinity.14,54,55 In the case of zeolite-like MOFs, with rho and sod topologies, the porosity increased from Li+ to Na+ and then decreased with the increase in cation size for the same charge of +1.12,26 With divalent ions, the porosity increased again. In crystalline materials, there is a well-defined approach of tailoring the pore size/geometry by changing ions, whereas in amorphous polymers, a precise control on the molecular arrangement of the ions is more challenging. In Table 1, the comparison of porosity properties of PIM-1 and its different functionalized derivatives is also shown in addition to the ionic systems discussed above. The porosity

properties reflect the strength of different interactions in these systems. The order of interaction strength reflected by the ASA, FFV, and bulk density is IonomIM-1 > carboxy-PIM-1 > PIM-1 > co-IonomIM-1 (17%). As the nitrile functionality in PIM-1 changes to carboxylic acid, polymer−polymer interactions become stronger, and the microporosity decreases consequently. The trends observed in our simulations were consistent with the experimental work done by Du et al.32 and Weber et al.33 on carboxylated PIMs. When the functionality changes to negatively charged carboxylate ions in the framework (IonomIM-1), the strong polymer−polymer Coulombic interactions decrease the porosity further. It is worth noting that the microporosity of co-IonomIM-1 (17%) was closer to PIM-1, as seen from Table 1. The geometric PSDs for IonomIM-1 and co-IonomIM-1 (17%) with different counterions are shown in Figure 2a,b. The peak positions of PSDs for IonomIM-1 were 2.55 (0.21) Å for Li+, 2.41 (0.45) Å for Na+, 2.15 (0.38) Å for K+, 2.61 (0.2) Å for Rb+, and 2.98 (0.19) Å for Mg2+. For co-IonomIM-1 (17%), the values were 3.59 (0.69) Å for Li+, 3.59 (0.62) Å for Na+, 3.59 (0.43) Å for K+, 3.39 (0.31) Å for Rb+, and 3.89 (0.26) Å D


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Figure 4. Simulated structure factors of samples of PIM-1 and IonomIM-1 with Na+ (brown left pointing-pointer) and Mg2+ (green diamond). The solid lines show the first peak positions with 2θ = 13.5°, 14.5°, and 14.6° for PIM-1, IonomIM-1 (Mg2+), and (Na+), respectively, and the dashed lines show their respective second peak positions at 2θ = 18.3°, 18.75°, and 18.8°. The PIM-1 samples were obtained from refs 31 and 58. The error bars represent standard deviations of five independent simulations.

Table 2. Comparison of Simulated Structure Factors of PIM1 and IonomIM-1 (Na+ and Mg2+) Studied in This Work with Experimental Data17 first peak position

second peak position

polymer

2θ (°)

d-spacing (Å)

2θ (°)

d-spacing (Å)

PIM-1 IonomIM-1 (Mg2+) IonomIM-1 (Na+)

13.5 14.5 14.6

6.55 6.10 6.06

18.3 18.75 18.8

4.84 4.73 4.71

for Mg2+. Numbers enclosed in parentheses represent error bars. It is important to note that taking into account the error bars, the differences observed in peak positions for different systems were not significant. However, compared to coIonomIM-1 (17%), IonomIM-1 showed narrower PSDs with smaller average peak pore sizes consistent with the lower porosity observed in the IonomIM-1 frameworks. The trends obtained here were similar to the ones observed for other amorphous materials such as PAFs18 and POPs,8 where the pore sizes did not shift significantly with different ions and between two different percentages of lithiation, respectively. However, in both cases, the gas adsorption performance was greatly improved with the introduction of ions. In crystalline microporous materials, the differences in pore size and geometry are well-defined because there is a narrow pore size rather than a broad distribution, which occurs in the case of amorphous materials. For example, in type A zeolites, the exchange of ion from K+ to Na+ increased the pore size from 3 to 4 Å because of the bigger size of K+ ions blocking the pores.56 Cation exchange with different ions alters the pore window size, which in turn affects gas adsorption properties.57 The radial distribution function (RDF) of the oxygen atoms in the carboxylate group was computed for IonomIM-1 and coIonomIM-1 (17%) (see Figure 3). The coordination numbers corresponding to the RDF of the oxygen atoms were also plotted (see Figure S3a,b). The oxygen atoms were chosen to study the effect of different cations on the structure as the ions

Figure 5. Simulated CO2 adsorption isotherms at 294.15 K for (a) IonomIM-1 and (b) co-IonomIM-1 (17%) with different counterions (black square, Li+; red circle, Na+; blue triangle, K+; pink nabla, Rb+; and green diamond, Mg2+) using GEMC simulations. The errors bars represent standard deviations of five independent simulations.

are located close to the negatively charged oxygen atoms. Hence, an RDF of oxygen atoms on different molecules gives a measure of the effect of size/charge of the cation in proximity to them. For these calculations, the oxygen atoms located on the same carboxylate groups (1−3 bonds) were excluded. From Figure 3, it is evident that there are two peaks at approximately 4 and 7 Å. The first peak corresponds to the closest oxygen atoms between two different IonomIM-1 units. This peak slightly shifted to larger distances with the increasing size of the counterions from Li+ to Rb+. This can be related to the presence of larger ions, which cause the oxygen atoms to separate from each other. The second peak at 7 Å corresponds to the oxygen atoms of the carboxylate group located on the other side of the aromatic ring. Interestingly, Mg2+ showed a higher intensity at the first peak (subsequently lower intensity at the second peak) in the case of co-IonomIM-1 (17%), which was also reflected in the coordination number (Figure S3b), which doubles in the case of Mg2+ at this distance. The same trend was not observed for Mg2+ in IonomIM-1. This could be attributed to the packing effects in co-IonomIM-1 (17%), a random copolymer, thereby introducing some heterogeneity in E


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might also lead to differences in the RDF and coordination number between co-IonomIM-1 (17%) and IonomIM-1 systems. Simulated structure factors were calculated for pure IonomIM-1 samples with Na+ and Mg2+ ions and also for PIM-1 simulation boxes obtained from previous works.31,58 The results presented here are an average of five independent simulations. The peak positions were compared with the experimental work done by Zhao et al.,17 where wide-angle Xray diffraction patterns were studied for postmodified PIM-1 membranes with Na+, Mg2+, and Ca2+ ions with different degrees of hydrolysis. The simulated structure factors were compared with experimental samples having the highest ion content in them (PIM-(Na 2d) with 95.5% ions and PIM-(Na 2d)-Mg with 95.3% ions). Simulated scattering patterns of PIM-1 presented in Figure 4 were consistent with the peaks experimentally observed by Zhao et al.17 (2θ ≈ 13°, 18.3°, and 23°) and have also been compared previously by our group with the available experimental data.58,59 The d-spacing values are given by Bragg’s law: d = λ/2 sin θ, where λ is the wavelength (1.54 Å) and θ is the scattering angle. The peak positions at 2θ = 13.5° and 18.3° correspond to d-spacings of 6.5 and 4.8 Å, respectively. These d-spacing values reflect intersegment distances between loosely packed chains containing the free volume and distances between chains that are packed in a space-efficient manner, respectively.59−61 Table 2 lists the 2θ and d-spacing values of these peak positions for PIM-1 and IonomIM-1 with Na+ and Mg2+ ions. The second peak at 2θ = 18.3° (dashed lines in Figure 4) shifted to higher 2θ corresponding to a decrease in the d-spacing from 4.84 to 4.71 Å from PIM-1 to IonomIM-1 (Na+). This is attributed to the decrease in average chain-to-chain distance due to the incorporation of ions, thereby increasing polymer−polymer interactions in the framework. This trend was also consistent with the experimental work of Zhao et al.,17 where the peak shifted to a lower d-spacing moving from PIM-1 to the postmodified samples containing Na+ and Mg2+ ions. It is worthwhile to note that the shift in this peak between Na+ and Mg2+ ions was very small in both simulations and experimental data. This was consistent with the observations in the current simulations, where the RDF of the carboxylate oxygen atoms (Figure 3) did not change significantly with changing the ions in pure IonomIM-1. The first peak (solid lines in Figure 4) also shifted to lower d-spacing values (from 6.55 Å in PIM-1 to 6.06 Å in IonomIM-1 with Na+ ions), indicating that the free volume decreased moving from PIM-1 to pure IonomIM-1. This was also corroborated by the porosity properties listed in Table 1 and PSD (Figure 2), where the porosity of pure IonomIM-1 decreased in comparison to PIM-1. There was a slight shift in this peak for PIM-(Na 2d)-Mg in the experimental data; however, this may be attributed to the presence of some fraction of unhydrolyzed PIM-1 in these samples. The CO2 adsorption isotherms of IonomIM-1 and coIonomIM-1 (17%) with different counterions at 294.15 K are shown in Figure 5a,b. Changing the size/charge of counterions affected the adsorption capacity in IonomIM-1. Mg2+ showed the highest adsorption capacity followed by the alkali metals Li+, Na+, K+, and Rb+, where a decreasing trend was observed with increasing counterion size. However, for co-IonomIM-1 (17%), the adsorption capacity reflected the effect of changing counterions only at very low pressures (0.1 kPa) after which the same adsorption capacity was obtained. The adsorption isotherms of IonomIMs in this study were also compared to

Figure 6. Ideal permeation selectivity vs CO2 permeability plots for (a) CO2/CH4 and (b) CO2/N2 gas mixtures from simulations of IonomIM-1 with Na+ (red circle), Mg2+ (green diamond), and PIM-1 (orange left pointing-pointer).31 Open symbols represent the experimental data from Zhao et al.17

Table 3. Mixed-Gas Separation Conditions Relevant to PSA and VSA Studied in This Work3 case

application

1

natural gas purification by PSA landfill gas purification by PSA landfill gas purification by VSA flue gas separation by VSA

2 3 4

gas mixture composition

adsorption pressure (bar)

desorption pressure (bar)

CO2/CH4 = 10:90

5

1

CO2/CH4 = 50:50

5

1

CO2/CH4 = 50:50

1

0.1

CO2/N2 = 10:90

1

0.1

the structure. co-IonomIM-1 (17%) also has a significant amount of microporosity, comparable to that of PIM-1, as shown earlier. This is not the case with IonomIM-1, where the molecules are packed closely, reducing the porosity, which F


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Figure 7. Sorbent selection parameter (Ssp) versus working capacity of gas (ΔNCO2) for IonomIMs with different counterions: black diamond, Li+; red diamond, Na+; blue diamond, K+; pink diamond, Rb+; and green diamond, Mg2+ (diamonds and half circles of the same colors represent IonomIM-1 and co-IonomIM-1 (17%), respectively). All symbols marked in red represent the experimental data points for comparison from ref 3 and respective references mentioned below(red cross) MOFs, (red triangle) zeolites, (red plus) POPs, and (red nabla) activated carbons. Lavender star represents the microporous material in each case showing the best performance(a) case 1, Mg-MOF-74;13 (b) case 2, HKUST-1;66 (c) case 3, CUK-1;67 and (d) case 4, ZIF-78.68 The error bars represent the standard deviation of five independent simulation boxes.

It is evident from Figure 6a,b that the incorporation of ions in PIM-1 enhanced the permselectivity in CO2/CH4 and CO2/ N2 gases, which was supported by both experiments and simulations. It is well-known that the permeabilities of PIM-1 membranes vary widely depending on the processing history of samples, including solvents used for post-treatment, film thickness, the presence of moisture, aging time, and temperature.64,65 This leads to significant changes in permeability when comparing experimental results with the neat simulation samples. Nevertheless, the trends observed here from simulation and experiments upon introducing ionic groups were similar. Introduction of Mg2+ ions led to increased permeability at the cost of reduced permselectivity as compared to Na+ ions. This was also supported by the increase in free volume for Mg2+ as compared to other monovalent ions in this study (Table 1). In the experimental studies, the introduction of Mg2+ slightly improved the permselectivity compared to Na+, which was attributed to the interplay between the increase in free volume and electron vacancy offered by divalent ions. No further properties relating free volume or electron distribution were analyzed further to support the conclusion. To evaluate the CO2 separation performance of the IonomIMs studied in this work in gas mixtures relevant to PSA (pressure swing adsorption) and VSA (vaccum swing adsorption) applications on an industrial scale, mixed-gas adsorption simulations were carried out for four cases, as described by Bae and Snurr.3 The pressure ranges and

those of PIM-1 samples from the work of Hart and Colina (see Supporting Information Figure S4).31 Pure IonomIM-1 showed higher pure CO2 adsorption capacity at low pressures (up to 100 kPa) compared to PIM1. At pressures above 100 kPa, PIM-1 began to show an increased adsorption capacity, as shown in Figure S4a. This was also consistent with the previous work done by Hart and Colina,31 where the increasing ion content correlated with increased adsorption only at low pressures. Also, it was shown that the CO2 adsorption capacity of co-IonomIMs increased at higher pressures, greater than that of PIM-1, as shown in Figure S4b. Ideal gas separation performances for CO2/CH4 and CO2/ N2 gas mixtures were evaluated for pure IonomIM-1 with Na+ and Mg2+ ions to compare with the available experimental data.17 Figure 6a,b shows the permselectivity versus CO2 permeability in CO2/CH4 and CO2/N2 gas mixtures. Previous work by the Colina group calculated the permeability and permselectivity in an efficient manner for PIM-like polymers.62 The permeability coefficient (P) is calculated according to the solution-diffusion model, P = solubility (S) × diffusivity (D). The simulation data in ionic and nonionic PIMs were fitted to an empirical relationship in the work done previously by Hart and Colina,31 which is given by P = α* exp(βf), where α* and β are empirical constants, the latter related to the size of the gas molecule.63 The α* and β values are31 0.56993 and 37.212 for CO2, 0.00308 and 46.928 for CH4, and 0.01096 and 40.726 for N2, respectively. G


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Figure 8. Enlarged view of sorbent selection parameter (Ssp) versus working capacity of gas (ΔNCO2) for IonomIM-1 with different counterions black diamond, Li+; red diamond, Na+; blue diamond, K+; pink diamond, Rb+; and green diamond, Mg2+ from Figure 7.

Figure 9. Enlarged view of sorbent selection parameter (Ssp) versus working capacity of gas (ΔNCO2) for co-IonomIM-1 (17%) with different counterionsblack circle left solid, Li+; red circle left solid, Na+; blue circle left solid, K+; pink circle left solid, Rb+; and green circle left solid, Mg2+ from Figure 7. H


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compared to Na+ and Mg2+ ions. In general, Mg2+ showed a poor separation performance as compared to other cations, as observed in all four test cases described above for both IonomIM-1 and co-IonomIM-1 (17%). This can be due to the relatively higher FFV and slightly wider PSDs observed for Mg2+ frameworks, resulting in a higher selectivity also toward CH4 or N2 compared to other ion-based frameworks. Thus, there are three factors that play a key role in determining the success of these systems in CO2 gas adsorption and separation performance: (1) concentration of ions, (2) size and charge of counterions at a sufficient ion concentration that ensures a change in gas separation performance, and (3) pressure range that is being studied for a particular application such as PSA and VSA for a fixed concentration of ions.

compositions of the four cases are presented in Table 3. The criteria suggested by Bae and Snurr were calculated as follows: ΔNi = Niads − Nides

(1)

R = (ΔNi /Niads) × 100

(2)

⎛ N ads ⎞⎛ y ⎞ j αijads = ⎜⎜ iads ⎟⎟⎜⎜ ⎟⎟ y ⎝ N j ⎠⎝ i ⎠

(3)

Ssp = (αijads)2 /(αijdes)(ΔNi /ΔNj)

(4)

where N is the absolute gas loading capacity, ΔN is the working capacity of the gas, R is the regenerability, αij is the adsorption selectivity of component i over j, S is the sorbent selection parameter, and y is the molar ratio in the gas phase. Subscripts i and j denote CO2 and CH4 or N2, respectively. Superscripts ads and des imply adsorption and desorption conditions, respectively. S versus ΔNCO2 was plotted, as shown in Figure 7. When a microporous material is toward the upper right, the gas selectivity performance is improved because both S and ΔNCO2 need to be maximized. Mixed-gas separation simulations were performed at 294.15 K using GEMC simulations for both IonomIM-1 and coIonomIM-1 (17%). All IonomIMs from this study are predicted to have enhanced selectivity over many of the experimentally studied MOFs, zeolites, POPs, and activated carbons reported in the literature.16,64−66 From our previous work,31 it was also shown that the mixed-gas separation performance of IonomIMs at all concentrations (17, 20, 33, 50, and 100 % by weight) improved as compared to PIM-1. The interaction of CO2 with IonomIMs is favored because of its quadrupolar moment (−14.27 ± 0.61 × 10−40 cm2) due to which more CO2 is attracted toward the ionic sites. Although, the strong polymer− polymer Coulombic interactions reduce the microporosity, the pore size (∼3.5 Å) is sufficient to allow CO2 separation (kinetic diameter 3.3 Å). The synergy between these two effects helps in separating out CO2 from (i) nonpolar and larger CH4 molecules (kinetic diameter 3.8 Å) and (ii) less quadrupolar (−4.65 ± 0.08 10−40 cm2) and larger N2 molecules (kinetic diameter 3.68 Å). IonomIM-1 showed better CO2 gas separation performance than co-IonomIM-1 (17%) in all cases, as shown in Figure 7. The change in the separation performance of IonomIMs with different counterions depends on the concentration of ions in the framework. The pure IonomIMs showed a better change in gas separation performance with different counterions in most cases. Figures 8 and 9 show the enlarged view of Figure 7 for different ions in IonomIM-1 and co-IonomIM-1 (17%), respectively. For IonoMIM-1 shown in Figure 8, the separation performance of ions was more tunable in cases where the partial pressure of CO2 was lower (cases 1, 3, and 4). K+ performed slightly better in cases 1 and 3, and Na+ showed a better separation performance on an average in cases 2 and 4. For co-IonomIM-1 (17%) (Figure 9), in general, the difference in the separation performances were less as compared to pure IonomIM-1 and did not show a strict dependence on the partial pressure of CO2 gas. Li+ performed slightly better in case 1. Rb+ and K+ showed a good separation performance on an average in case 4. Li+ showed a slightly higher separation performance in case 2, as shown in Figure 9. In case 3, considering the error bars, Li+, K+, and Rb+ showed higher separation performance

4. CONCLUSIONS The effect of changing counterions (Li+, Na+, K+, and Mg2+) on porosity, CO2 gas adsorption capacity, and separation performance of carboxylate-functionalized PIM-1 was studied for IonomIM-1 and co-IonomIM-1 (17%). The effect of the different functionalizations of PIM-1, namely, carboxylic acid groups, carboxylate groups with different counterions (Li+, Na+, K+, Rb+, and Mg2+), and ionic concentrations (100 and 17%) on the porosity properties was discussed. For the completely hydrolyzed/ionic systems, the strength of interactions was of the order IonomIM-1 > carboxy-PIM-1 > PIM-1 and the porosity followed a reverse trend, which also reflected the strength of interactions. Interestingly, the microporosity of coIonomIM-1 (17%) systems was similar to or slightly greater than that of PIM-1. The IonomIMs chosen in this study showed an enhanced CO2 gas adsorption and separation performance in CO2/CH4 and CO2/N2 gas mixtures and outperformed several materials such as MOFs, POPs, zeolites, and activated carbons, which was due to the combined effect of electrostatic interactions and optimum pore size for CO2 separation. The effect of changing the size and charge of the counterions to manipulate porosity, gas adsorption, and selectivity depends on the concentration of ions. Although the porosity and structural properties between different ions did not show remarkable differences, CO2 adsorption capacity and mixed gas selectivity were affected by ion size and charge for IonomIM-1. This effect was lost for co-IonomIM-1 (17%), as shown by CO2 adsorption isotherms and mixed-gas separation performance in CO2/CH4 and CO2/N2 mixtures. Even though a divalent ion such as Mg2+ showed promising pure CO2 gas adsorption because of its larger porosity, it was less effective at CO2-mixed gas separations. This study has outlined several design principles to help in designing energy-efficient ionicfunctionalized polymeric materials for gas separation applications, especially for PSA and VSA technologies.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.7b04320. Monomeric repeat unit of IonomIM-1, UFF parameters, partial charges for polymer framework atoms and metal ions, 21-step MD compression/decompression simulation scheme, comparison of porosity properties of coIonomIM-1 (17%) with Na+ counterions using UFF and DREIDING, comparison of geometric PSD of coI


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IonomIM-1 (17%) with Na+ for DREIDING and UFF, comparison of the RDFs of different counterions and oxygen atoms in CO2 gas molecules in IonomIM-1, simulated CO2 adsorption isotherms at 294.15 K for IonomIM-1 and co-IonomIM-1 (17%) with different counterions and PIM-1, and RDFs of Na+ and oxygen atoms in CO2 gas molecule in co-IonomIM-1 (PDF)

(11) D’Alessandro, D. M.; Smit, B.; Long, J. R. Carbon Dioxide Capture: Prospects for New Materials. Angew. Chem., Int. Ed. 2010, 49, 6058−6082. (12) Chen, C.; Kim, J.; Yang, D.-A.; Ahn, W.-S. Carbon Dioxide Adsorption over Zeolite-like Metal Organic Frameworks (ZMOFs) Having a Sod Topology: Structure and Ion-Exchange Effect. Chem. Eng. J. 2011, 168, 1134−1139. (13) Dietzel, P. D. C.; Besikiotis, V.; Blom, R. Application of Metal− organic Frameworks with Coordinatively Unsaturated Metal Sites in Storage and Separation of Methane and Carbon Dioxide. J. Mater. Chem. 2009, 19, 7362−7370. (14) Joshi, U. D.; Joshi, P. N.; Tamhankar, S. S.; Joshi, V. V.; Rode, C. V.; Shiralkar, V. P. Effect of Nonframework Cations and Crystallinity on the Basicity of NaX Zeolites. Appl. Catal., A 2003, 239, 209−220. (15) Zhang, J.; Singh, R.; Webley, P. A. Alkali and Alkaline-Earth Cation Exchanged Chabazite Zeolites for Adsorption Based CO2 Capture. Microporous Mesoporous Mater. 2008, 111, 478−487. (16) Farha, O. K.; Bae, Y.-S.; Hauser, B. G.; Spokoyny, A. M.; Snurr, R. Q.; Mirkin, C. A.; Hupp, J. T. Chemical Reduction of a Diimide Based Porous Polymer for Selective Uptake of Carbon Dioxide versus Methane. Chem. Commun. 2010, 46, 1056−1058. (17) Zhao, H.; Xie, Q.; Ding, X.; Chen, J.; Hua, M.; Tan, X.; Zhang, Y. High Performance Post-Modified Polymers of Intrinsic Microporosity (PIM-1) Membranes Based on Multivalent Metal Ions for Gas Separation. J. Membr. Sci. 2016, 514, 305−312. (18) Ma, H.; Ren, H.; Zou, X.; Meng, S.; Sun, F.; Zhu, G. PostMetalation of Porous Aromatic Frameworks for Highly Efficient Carbon Capture from CO2 + N2 and CH4 + N2 Mixtures. Polym. Chem. 2014, 5, 144−152. (19) Nouar, F.; Eckert, J.; Eubank, J. F.; Forster, P.; Eddaoudi, M. Zeolite-like Metal-Organic Frameworks (ZMOFs) as Hydrogen Storage Platform: Lithium and Magnesium Ion-Exchange and H(Rho-ZMOF) Interaction Studies. J. Am. Chem. Soc. 2009, 131, 2864− 2870. (20) Düren, T.; Bae, Y.-S.; Snurr, R. Q. Using Molecular Simulation to Characterise Metal-Organic Frameworks for Adsorption Applications. Chem. Soc. Rev. 2009, 38, 1237−1247. (21) Smit, B.; Maesen, T. L. M. Molecular Simulations of Zeolites: Adsorption, Diffusion, and Shape Selectivity. Chem. Rev. 2008, 108, 4125−4184. (22) Yazaydin, A. Ö .; Snurr, R. Q.; Park, T.-H.; Koh, K.; Liu, J.; LeVan, M. D.; Benin, A. I.; Jakubczak, P.; Lanuza, M.; Galloway, D. B.; Low, J. J.; Willis, R. R. Screening of Metal-Organic Frameworks for Carbon Dioxide Capture from Flue Gas Using a Combined Experimental and Modeling Approach. J. Am. Chem. Soc. 2009, 131, 18198−18199. (23) Babarao, R.; Jiang, J.; Sandler, S. I. Molecular Simulations for Adsorptive Separation of CO2/CH4 Mixture in Metal-Exposed, Catenated, and Charged Metal−Organic Frameworks. Langmuir 2009, 25, 5239−5247. (24) 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. (25) Nalaparaju, A.; Khurana, M.; Farooq, S.; Karimi, I. A.; Jiang, J. W. CO2 Capture in Cation-Exchanged Metal−organic Frameworks: Holistic Modeling from Molecular Simulation to Process Optimization. Chem. Eng. Sci. 2015, 124, 70−78. (26) Chen, Y. F.; Nalaparaju, A.; Eddaoudi, M.; Jiang, J. W. CO2 Adsorption in Mono-, Di- and Trivalent Cation-Exchanged Metal − Organic Frameworks: A Molecular Simulation Study. Langmuir 2012, 28, 3903−3910. (27) McKeown, N. B.; Budd, P. M.; Msayib, K. J.; Ghanem, B. S.; Kingston, H. J.; Tattershall, C. E.; Makhseed, S.; Reynolds, K. J.; Fritsch, D. Polymers of Intrinsic Microporosity (PIMs): Bridging the Void between Microporous and Polymeric Materials. Chem.Eur. J. 2005, 11, 2610−2620.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]fl.edu; Phone: 352-294-3488. ORCID

Kyle E. Hart: 0000-0002-8158-038X Coray M. Colina: 0000-0003-2367-1352 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Foundation (DMR-1604376). The authors would like to thank Michael E. Fortunato and Grit Kupgan for their inputs and helpful discussions. Computational resources were supported in part by the Cyberinfrastructure unit of Information Technology Services, The Pennsylvania State University, and the UF Research Computing, University of Florida.

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REFERENCES

(1) Kenarsari, S. D.; Yang, D.; Jiang, G.; Zhang, S.; Wang, J.; Russell, A. G.; Wei, Q.; Fan, M. Review of Recent Advances in Carbon Dioxide Separation and Capture. RSC Adv. 2013, 3, 22739−22773. (2) Boot-Handford, M. E.; Abanades, J. C.; Anthony, E. J.; Blunt, M. J.; Brandani, S.; Dowell, N. M.; Fernández, J. R.; Ferrari, M.-C.; Gross, R.; Hallett, J. P.; Haszeldine, R. S.; Heptonstall, P.; Lyngfelt, A.; Makuch, Z.; Mangano, E.; Porter, R. T. J.; Pourkashanian, M.; Rochelle, G. T.; Shah, N.; Yao, J. G.; Fennell, P. S. Carbon Capture and Storage Update. Energy Environ. Sci. 2014, 7, 130−189. (3) Bae, Y.-S.; Snurr, R. Q. Development and Evaluation of Porous Materials for Carbon Dioxide Separation and Capture. Angew. Chem., Int. Ed. 2011, 50, 11586−11596. (4) Dawson, R.; Cooper, A. I.; Adams, D. J. Chemical Functionalization Strategies for Carbon Dioxide Capture in Microporous Organic Polymers. Polym. Int. 2013, 62, 345−352. (5) 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. (6) El-Kaderi, H. M.; Hunt, J. R.; Mendoza-Cortés, J. L.; Côté, A. P.; Taylor, R. E.; O’Keeffe, M.; Yaghi, O. M. Designed Synthesis of 3D Covalent Organic Frameworks. Science 2007, 316, 268−272. (7) McKeown, N. B.; Budd, P. M. Polymers of Intrinsic Microporosity (PIMs): Organic Materials for Membrane Separations, Heterogeneous Catalysis and Hydrogen Storage. Chem. Soc. Rev. 2006, 35, 675−683. (8) Farha, O. K.; Spokoyny, A. M.; Hauser, B. G.; Bae, Y.-S.; Brown, S. E.; Snurr, R. Q.; Mirkin, C. A.; Hupp, J. T. Synthesis, Properties, and Gas Separation Studies of a Robust Diimide-Based Microporous Organic Polymer. Chem. Mater. 2009, 21, 3033−3035. (9) Ben, T.; Ren, H.; Ma, S.; Cao, D.; Lan, J.; Jing, X.; Wang, W.; Xu, J.; Deng, F.; Simmons, J. M.; Qiu, S.; Zhu, G. Targeted Synthesis of a Porous Aromatic Framework with High Stability and Exceptionally High Surface Area. Angew. Chem., Int. Ed. 2009, 48, 9457−9460. (10) Dawson, R.; Laybourn, A.; Khimyak, Y. Z.; Adams, D. J.; Cooper, A. I. High Surface Area Conjugated Microporous Polymers: The Importance of Reaction Solvent Choice. Macromolecules 2010, 43, 8524−8530. J


Article

Langmuir (28) Budd, P. M.; Ghanem, B. S.; Makhseed, S.; McKeown, N. B.; Msayib, K. J.; Tattershall, C. E. Polymers of Intrinsic Microporosity (PIMs): Robust, Solution-Processable, Organic Nanoporous Materials. Chem. Commun. 2004, 2, 230−231. (29) Budd, P. M.; Elabas, E. S.; Ghanem, B. S.; Makhseed, S.; McKeown, N. B.; Msayib, K. J.; Tattershall, C. E.; Wang, D. SolutionProcessed, Organophilic Membrane Derived from a Polymer of Intrinsic Microporosity. Adv. Mater. 2004, 16, 456−459. (30) Budd, P. M.; Msayib, K. J.; Tattershall, C. E.; Ghanem, B. S.; Reynolds, K. J.; McKeown, N. B.; Fritsch, D. Gas Separation Membranes from Polymers of Intrinsic Microporosity. J. Membr. Sci. 2005, 251, 263−269. (31) Hart, K. E.; Colina, C. M. Ionomers of Intrinsic Microporosity: In Silico Development of Ionic- Functionalized Gas-Separation Membranes. Langmuir 2014, 30, 12039−12048. (32) Du, N.; Robertson, G. P.; Song, J.; Pinnau, I.; Guiver, M. D. High-Performance Carboxylated Polymers of Intrinsic Microporosity (PIMs) with Tunable Gas Transport Properties. Macromolecules 2009, 42, 6038−6043. (33) Weber, J.; Du, N.; Guiver, M. D. Influence of Intermolecular Interactions on the Observable Porosity in Intrinsically Microporous Polymers. Macromolecules 2011, 44, 1763−1767. (34) Walton, K. S.; Abney, M. B.; LeVan, M. D. CO2 Adsorption in Y and X Zeolites Modified by Alkali Metal Cation Exchange. Microporous Mesoporous Mater. 2006, 91, 78−84. (35) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25, 1157−1174. (36) Martin, M. G.; Siepmann, J. I. Transferable Potentials for Phase Equilibria. 1. United-Atom Description of N -Alkanes. J. Phys. Chem. B 1998, 102, 2569−2577. (37) Rai, N.; Siepmann, J. I. Transferable Potentials for Phase Equilibria. 9. Explicit Hydrogen Description of Benzene and FiveMembered and Six-Membered Heterocyclic Aromatic Compounds. J. Phys. Chem. B 2007, 111, 10790−10799. (38) Rappe, A. K.; Casewit, C. J.; Colwell, K. S., III; Goddard, W. A.; Skiff, W. M. UFF, a Full Periodic Table Force Field for Molecular Mechanics and Molecular Dynamics Simulations. J. Am. Chem. Soc. 1992, 114, 10024−10035. (39) Mayo, S. L.; Olafson, B. D., III; Goddard, W. A. DREIDING: A Generic Force Field for Molecular Simulations. J. Phys. Chem. 1990, 94, 8897−8909. (40) Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R., Jr; Merz, K. M.; Ferguson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J. Am. Chem. Soc. 1995, 117, 5179−5197. (41) Potoff, J. J.; Siepmann, J. I. Vapor−liquid Equilibria of Mixtures Containing Alkanes, Carbon Dioxide, and Nitrogen. AIChE J. 2001, 47, 1676−1682. (42) Abbott, L. J.; Hart, K. E.; Colina, C. M. Polymatic: A Generalized Simulated Polymerization Algorithm for Amorphous Polymers. Theor. Chem. Acc. 2013, 132, 1334. (43) Abbott, L.; Colina, C. M. Polymatic: A Simulated Polymerization Algorithm. https://nanohub.org/resources/17278. (44) Larsen, G. S.; Lin, P.; Hart, K. E.; Colina, C. M. Molecular Simulations of PIM-1-like Polymers of Intrinsic Microporosity. Macromolecules 2011, 44, 6944−6951. (45) Hofmann, D.; Fritz, L.; Ulbrich, J.; Paul, D. Molecular Simulation of Small Molecule Diffusion and Solution in Dense Amorphous Polysiloxanes and Polyimides. Comput. Theor. Polym. Sci. 2000, 10, 419−436. (46) Karayiannis, N. C.; Mavrantzas, V. G.; Theodorou, D. N. Detailed Atomistic Simulation of the Segmental Dynamics and Barrier Properties of Amorphous Poly(ethylene Terephthalate) and Poly(ethylene Isophthalate). Macromolecules 2004, 37, 2978−2995. (47) Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995, 117, 1−19.

(48) Sarkisov, L.; Harrison, A. Computational Structure Characterisation Tools in Application to Ordered and Disordered Porous Materials Materials. Mol. Simul. 2011, 37, 1248−1257. (49) Bondi, A. Van Der Waals Volumes and Radii. J. Phys. Chem. 1964, 68, 441−451. (50) Van Krevelen, D. W. Properties of Polymers: Their Correlation with Chemical Structure; Their Numerical Estimation and Prediction from Additive Group Contributions; Elsevier Sci. Pub. Co.: Amsterdam Netherlands, 1990. (51) Panagiotopoulos, A. Z. Direct Determination of Phase Coexistence Properties of Fluids by Monte Carlo Simulation in a New Ensemble. Mol. Phys. 1987, 61, 813−826. (52) Panagiotopoulos, A. Z.; Quirke, N.; Stapleton, M.; Tildesley, D. J. Phase Equilibria by Simulation in the Gibbs Ensemble. Mol. Phys. 1988, 63, 527−545. (53) Shah, J. K.; Maginn, E. J. A General and Efficient Monte Carlo Method for Sampling Intramolecular Degrees of Freedom of Branched and Cyclic Molecules. J. Chem. Phys. 2011, 135, 134121. (54) Yang, S.-T.; Kim, J.; Ahn, W.-S. CO2 Adsorption over IonExchanged Zeolite Beta with Alkali and Alkaline Earth Metal Ions. Microporous Mesoporous Mater. 2010, 135, 90−94. (55) Zukal, A.; Pawlesa, J.; Č ejka, J. Isosteric Heats of Adsorption of Carbon Dioxide on Zeolite MCM-22 Modified by Alkali Metal Cations. Adsorption 2009, 15, 264−270. (56) Breck, D. W.; Eversole, W. G.; Milton, R. M.; Reed, T. B.; Thomas, T. L. Crystalline Zeolites. I. The Properties of a New Synthetic Zeolite, Type A. J. Am. Chem. Soc. 1956, 78, 5963−5972. (57) Lozinska, M. M.; Mangano, E.; Greenaway, A. G.; Fletcher, R.; Thompson, S. P.; Murray, C. A.; Brandani, S.; Wright, P. A. Cation Control of Molecular Sieving by Flexible Li-Containing Zeolite Rho. J. Phys. Chem. C 2016, 120, 19652−19662. (58) Hart, K. E.; Abbott, L. J.; Colina, C. M. Analysis of Force Fields and BET Theory for Polymers of Intrinsic Microporosity. Mol. Simul. 2013, 39, 397−404. (59) McDermott, A. G.; Larsen, G. S.; Budd, P. M.; Colina, C. M.; Runt, J. Structural Characterization of a Polymer of Intrinsic Microporosity: X-Ray Scattering with Interpretation Enhanced by Molecular Dynamics Simulations. Macromolecules 2011, 44, 14−16. (60) Weber, J.; Su, Q.; Antonietti, M.; Thomas, A. Exploring Polymers of Intrinsic Microporosity − Microporous, Soluble Polyamide and Polyimide. Macromol. Rapid Commun. 2007, 28, 1871−1876. (61) Du, N.; Robertson, G. P.; Pinnau, I.; Guiver, M. D. Polymers of Intrinsic Microporosity Derived from Novel Disulfone-Based Monomers. Macromolecules 2009, 42, 6023−6030. (62) Hart, K. E.; Colina, C. M. Estimating Gas Permeability and Permselectivity of Microporous Polymers. J. Membr. Sci. 2014, 468, 259−268. (63) Thornton, A. W.; Nairn, K. M.; Hill, A. J.; Hill, J. M. New Relation between Diffusion and Free Volume: I. Predicting Gas Diffusion. J. Membr. Sci. 2009, 338, 29−37. (64) Budd, P. M.; McKeown, N. B.; Ghanem, B. S.; Msayib, K. J.; Fritsch, D.; Starannikova, L.; Belov, N.; Sanfirova, O.; Yampolskii, Y.; Shantarovich, V. Gas Permeation Parameters and Other Physicochemical Properties of a Polymer of Intrinsic Microporosity: Polybenzodioxane PIM-1. J. Membr. Sci. 2008, 325, 851−860. (65) Rose, I.; Bezzu, C. G.; Carta, M.; Comesaña-Gándara, B.; Lasseuguette, E.; Ferrari, M. C.; Bernardo, P.; Clarizia, G.; Fuoco, A.; Jansen, J. C.; Hart, K. E.; Liyana-Arachchi, T. P.; Colina, C. M.; McKeown, N. B. Polymer Ultrapermeability from the Inefficient Packing of 2D Chains. Nat. Mater. 2017, 16, 932−937. (66) Wang, Q. M.; Shen, D.; Bülow, M.; Lau, M. L.; Deng, S.; Fitch, F. R.; Lemcoff, N. O.; Semanscin, J. Metallo-Organic Molecular Sieve for Gas Separation and Purification. Microporous Mesoporous Mater. 2002, 55, 217−230. (67) Yoon, J. W.; Jhung, S. H.; Hwang, Y. K.; Humphrey, S. M.; Wood, P. T.; Chang, J.-S. Gas-Sorption Selectivity of CUK-1: A Porous Coordination Solid Made of Cobalt ( II ) and Pyridine-2, 4Dicarboxylic Acid. Adv. Mater. 2007, 19, 1830−1834. K


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