Screening and design of covalent organic framework membranes for

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Screening and design of covalent organic framework membranes for CO2/CH4 separation Tongan Yan, Youshi Lan, Minman Tong, and Chongli Zhong ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b04858 • Publication Date (Web): 15 Nov 2018 Downloaded from http://pubs.acs.org on November 16, 2018

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ACS Sustainable Chemistry & Engineering

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Screening and design of covalent organic framework membranes

2

for CO2/CH4 separation

3

Tongan Yan, †, # Youshi Lan, †, # Minman Tong, *, ‡ and Chongli Zhong *, †, §

4 5

†State

6

Technology, No.15, North Third Ring Road, Chaoyang District, Beijing 100029, China.

7

‡School

8

Road, Tongshan District, Xuzhou 221116, China.

9

§State

Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical

of chemistry and materials science, Jiangsu Normal University, No.101, Shanghai

Key Laboratory of Separation Membranes and Membrane Processes, Tianjin

10

Polytechnic University, No. 399, Bin Shui Xi Road, Xi Qing District, Tianjin 300387, China.

11

*Corresponding authors. E-mail address: [email protected]; [email protected].

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Abstract

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Membrane based CO2/CH4 separation is an effective and energy saving way for highly

3

demanded natural gas upgrading. Herein, high-throughput computational screening of

4

covalent organic frameworks (COFs) for CO2/CH4 membrane separation is realized by using

5

the 298 COF structures in CoRE COF database. Based on the study of structure-performance

6

relationships, structural features of the outperformed COFs were identified and classified,

7

followed by the structural design by decorating 11 CoRE COFs with 10 kinds of familiar

8

functional groups. By evaluating the 290 designed COFs as membrane materials, the

9

effectiveness and universality of -F and -Cl groups in improving the performance of various

10

COFs were identified. The well-performed COFs were also computationally evaluated as

11

fillers to make COF@polymer mixed matrix membranes, and improved separation

12

performance was obtained.

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KEYWORDS:

14

Functionalization, Molecular simulations.

COF,

Membrane

separation,

Structure-performance

15

2


relationship,

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Introduction

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Efficient separation of CO2 from CH4 is highly desirable in natural gas purification1,2 and

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landfill gas recovery3 as CH4 becomes an attractive substitute to petroleum due to its

4

environmental friendliness, abundant natural reserves and economic advantages.4 The most

5

widely used conventional method is aqueous amine absorption, which bears several

6

drawbacks such as the formation of corrosive species and the high energetic requirement for

7

the solvent regeneration.5 In contrast, the technology of membrane-based gas separation

8

processes is known to be a promising way in reducing the environmental impact and

9

operational costs of industrial processes.6 Currently, membrane market for gas separation is

10

dominated by polymeric membranes partially due to their low production costs and high

11

mechanical flexibility.7 However, polymeric membranes are often limited by a permeability

12

and selectivity trade-off. With the on-going demand for advanced membrane materials,

13

membranes based on crystalline materials with well-defined pore systems like zeolites8,9 or

14

metal-organic frameworks (MOFs)10,11, and composite membranes are actively studied and

15

exhibit very good separation performance.

16

Covalent organic frameworks (COFs) are an emerging class of crystalline networks that are

17

covalently built from organic components and featured by merits of low density, tunable pore

18

size and structure, facilely tailored functionality, large surface area, and so on.12 COFs can be

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fabricated into pure membranes13-19 and composite membranes (with support of MOFs,

20

grapheme oxides, etc.),15,20-22 or added as fillers into polymers to produce mixed matrix

21

membranes (MMMs)23-27 that exhibit high selectivity, high permeability and durable stability.

22

For example, COF-30015, CTF-1 membranes19 and COF-MOF composite membranes15,21 can

23

efficiently separate H2 from CO2 with high permeance that can surpass the Robeson's upper

24

bound. Recently, ACOF-1 membrane was prepared to show a high selectivity of 86.3 in

3



1

CO2/CH4 mixed gas separation with a CO2 permeance of about 9.9 × 10-9 mol/(m2·s·Pa)

2

owing to the molecular sieving effect resulted from the crystal intergrowth in the pores of

3

ACOF-1.20 Usually, these COF membranes are thermally stable up to 300 ○ C or even higher;

4

besides, the CTF-120 and ACOF-119 membranes also show long-term running stability during

5

90 and 120 h test. On the other hand, the addition of ACOF-125, SNW-126, TpPa-127 and

6

TpBD27 as active phase to several polymers (Matrimid25, PIM-126 and PBI-BuI27) can lead to

7

100~700% increase of the CO2 permeability and certain increase in CO2/CH4 selectivity,25,26

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In the meanwhile, the prepared membranes still exhibit satisfactory degradation temperature.

9

Thus, the future performance of more COFs associated in membrane separations is worth

10

expecting. To date, ~350 COFs have been experimentally reported and less than ~20 COFs

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have been fabricated into membrane materials. In this context, pre-screening of the membrane

12

performance prior to experimental synthesis is essential as fabrication of membranes is not an

13

easy work.28 To fast evaluate the CO2/CH4 membrane separation performance of COFs,

14

computational methods can be a powerful instrument instead of expensive and time-

15

consuming, trial and error experimental synthesis and characterization. Previous molecular

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simulation studies have shown that COF membranes can effectively separate H2/CH4, CO2/N2

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mixtures and desalinate seawater by evaluating a limited number of COFs.29-35 For example,

18

Keskin studied the membrane separation performance of COF-5, COF-6, and COF-10 for

19

H2/CH4 mixtures, which present much better performance than traditional zeolites CHA, LTA,

20

and ITQ-29.30 Cao and coworkers investigated the diffusion and separation behavior of H2

21

and CH4 in COF-102, COF-103, COF-105, COF-108 and Li-doped counterparts.31 Our earlier

22

work shows that 2D-COFs can be fabricated into ultrathin membranes with CO2/N2 separation

23

performance far above the 2008 Robeson's upper bound.32

24

However, to the best of our knowledge, membrane separation of CO2/CH4 mixture using

25

COFs is unexploited computationally. In this work, with updating the CoRE COF 4


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database36,37 that contains the most experimentally synthesized COFs, a computational

2

screening of 298 COFs membranes for CO2/CH4 separation was performed. Relationships

3

between performance and geometric and chemical properties are discussed, from which the

4

knowledge to design COFs with better membrane separation performance was obtained.

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Moreover, considering the mature processing of polymer membranes38 and that MMMs using

6

COFs could reduce the cost of pure COF membranes39, the performance of COF@polymer

7

MMMs were also computationally examined.

8

Computational methods

9

COF structures

10

The CoRE COF database was updated to 309 structures in this work, including 41 3D-COFs

11

with ctn, bor, dia or pts topology, and 268 2D-COFs with triangular, square, hexagonal,

12

octagonal, heteromorphic or hybrid pores. (https://core-cof.github.io/CoRE-COF-Database/).

13

Pore limiting diameter (PLD), largest cavity diameter (LCD), accessible surface area (Sacc)

14

and free volume (Vfree) were calculated using the open source software Zeo++ version 0.3.44,45

15

For each material, Sacc was calculated using a probe molecule with size equal to the kinetic

16

diameter of N2 (3.68 Å). Vfree was computed with a probe size of 0.0 Å, which was the

17

absolute amount of volume unoccupied by the framework atoms. Considering the diffusion

18

rates, 298 CoRE COFs with PLD larger than 3.3 Å (the kinetic diameters of CO2) were

19

included in the assessment of the membrane separation performance.

20

Force fields

21

A combination of the site-site Lennard-Jones (LJ) and Coulombic potentials was employed to

22

calculate the intermolecular interactions between adsorbates and adsorbates as well as

23

between adsorbates and COFs. Potential parameters of CO2 and CH4 gas molecules were

24

taken from TraPPE force field.40 CH4 was modeled as a single Lennard-Jones (LJ) interaction

5



1

site with the potential parameters of 𝜎CH4 = 3.73 Å and 𝜀CH4 𝑘B = 148.0 K. CO2 was modelled

2

as a rigid linear molecule (C−O bond length of 1.16 Å) with three charged LJ interacting sites

3

located on each atom. The LJ potential parameters are 𝜎O = 3.05 Å and 𝜀O 𝑘B = 79.0 K for

4

atom O and 𝜎C = 2.80 Å and 𝜀C 𝑘B = 27.0 K for atom C. Partial point charges centred at each

5

LJ site are 𝑞O = −0.35e and 𝑞C = 0.70e. An atomistic representation was used for all the

6

COFs. The choice of force field is critical for an accurate description of framework atoms.

7

Before the screening of the 298 CoRE COFs, both the DREIDING41 force field and the

8

universal force field (UFF)42 have been evaluated in our work. The resulting single

9

component adsorption isotherms from GCMC simulations were compared with the

10

experimental data in Figures S1 and S2. As can be seen, the GCMC results using DREIDING

11

force field can better reproduce the experimental data than the UFF. Therefore, the

12

DREIDING force field was used in the further calculations throughout this work. Also, the

13

calculated adsorption energies of CH4 (Figures S3) derived from our computational results

14

agree well with the measured data, further illustrating the reliability of the force fields adopted.

15

Partial point charges were assigned to frameworks using charge equilibration method (QEq).43

16

The Lorentz−Berthelot mixing rules were used to describe all of the LJ cross potential

17

parameters.

18

Simulation details

19

To predict membrane-based separation performance of CoRE COFs, both mixture adsorption

20

and mixture diffusion data were used. GCMC simulations were employed to calculate the

21

adsorption behaviors of CO2/CH4 (50:50) mixtures in each COF at 298 K and 10 bar. During

22

the simulations, molecules involve five types of trials: attempts (i) to randomly displace a

23

molecule (translation or rotation), (ii) to regrow a molecule at a random position, (iii) to

24

create a new molecule, (iv) to delete an existing molecule, and (v) to exchange molecular

25

identity. All of the COFs were treated as rigid frameworks with atoms frozen at their 6


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1

crystallographic positions. The numbers of the unit cells contained in the simulation box are

2

COF-dependent, and no finite-size effects existed by checking the simulations with larger

3

boxes. A cutoff distance was set to 14.0 Å for the LJ interactions, while the long-range

4

electrostatic interactions were handled using the Ewald summation technique. Periodic

5

boundary conditions were considered in all three dimensions. The numbers of the unit cells

6

contained in the simulation box are COF-dependent, ranging from 1 × 1 × 1 to 3 × 3 × 8.

7

Peng-Robinson equation of state was used to convert the pressure to the corresponding

8

fugacity used in the GCMC simulations. For each state point, GCMC simulations consisted of

9

2 × 107 steps to ensure the equilibration, followed by 2 × 107 steps to sample the desired

10

thermodynamic properties.

11

MD simulations using rigid frameworks in canonical (NVT) ensemble were applied to

12

examine the diffusion of CO2/CH4 mixtures in CoRE COFs. The whole MD simulations were

13

performed at 298 K and 10 bar. The Nosé-Hoover chain (NHC) thermostat as formulated by

14

Martynaet al.44 was applied to maintain the constant-temperature condition. The velocity

15

Verlet algorithm was used to integrate Newton’s equations of motion. Each MD simulation

16

was performed using 5 × 106 steps (i.e., 5 ns) with a time step of 1 fs, preceded by 1 × 106

17

steps (i.e., 1 ns) of equilibration. Self-diffusivities were calculated from the results of MD

18

simulations at the loadings obtained from GCMC simulations by averaging the results of 10

19

independent trajectories. All the simulations were performed using our in-house code HT-

20

CADSS. For COFs with high CO2/CH4 adsorption selectivity, simulation box are enlarged to

21

increase the number CH4 molecules to increase the statistical accuracy of MD simulations.

22

Membrane performance evaluation metrics

23

The adsorption selectivity for component A relative to component B in the mixture is defined

24

by

25

ads S A/B =  xA / xB  yB / yA 

1 7



1

where x and y are the mole fractions of the components in the adsorbed and bulk phases,

2

respectively.

3

The self-diffusivities of each species in the adsorbed mixture were related to the mean square

4

displacements (MSD) of the tagged particles by the Einstein equation,45 which were then

5

computed by taking the slope at long time

6

1 d Ds  lim 2dN t  dt

N

 r t   r  0 i 1

i

2

 2

i

7

where 〈⋯〉 denotes an ensemble average, 𝐫𝑖(𝑡)is the center-of-mass (COM) position vector of

8

the diffusing molecule i at time t, N is the number of molecules of the species in the

9

simulation system, and d corresponds to the dimension of the system examined. Usually, d is

10

equal to 3 by considering the orientationally averaged self-diffusivity from those in x, y and z

11

directions, except for the COFs that have one-dimensional pores (d = 1, only in the z

12

direction).

13

The membrane selectivity of COFs can be predicted once the adsorption and diffusion

14

properties were obtained. Assuming that the permeate side is close to vacuum, the membrane

15

selectivity for a binary mixture of A and B can be approximated as the product of adsorption

16

selectivity and diffusion selectivity46

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mem ads diff S A/B  S A/B  S A/B 

18

where NA and NB are the loadings of the two components at the feed side of the membrane

19

under adsorption conditions, respectively; 𝐷s,

20

components at the adsorbed loadings of the mixture, which is in units of m2/s. Permeabilities

21

of each gas species were then calculated as suggested in the literature47

22

PA =

  Ds,A  cA fA

 xA / xB   Ds,A  N A , N B   yA / yB  Ds,B  N A , N B 

， PB =

(3)

A

  Ds,B  cB

and 𝐷s,

B

are the self-diffusivities of the two

 4

fB

8


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1

where PA (PB) is the permeability of component A (B) in units of mol/m/s/Pa, ϕ is the

2

porosity of the material, 𝑐𝐴 (𝑐𝐵) is the corresponding concentration of component A (B) at the

3

feed side of the membrane in units of mol/m3, and 𝑓𝐴 (𝑓𝐵) is the bulk phase fugacity of

4

component A (B) in units of Pa. To demonstrate the reliability of our computational approach

5

in estimation of membrane separation performance, we compared the simulation results with

6

the widely studied MOF membranes in Figure S4. The results show that the calculated

7

permeability and the experimental permeability are well matched, demonstrating the

8

simulation results are reliable.

9

For modeling the separation performance of COF MMMs, the permeability of CO2/CH4

10

mixtures through polymeric membranes were taken from the experimental data available in

11

the literature, while those through the COF phase were taken from the simulation results

12

obtained in this work. Then, the Maxwell model48 was used to predict gas permeabilities

13

through the MMMs, as given by

14

Pr 

15

where P is the permeability of the COF/polymer MMM, λdm is the permeability ratio Pd/Pm

16

(Pd and Pm are the gas permeabilities in dispersed and continuous phases), Pr is the relative

17

permeability, φ is the volume fraction of the dispersed COF particles in the polymer matrix.,

18

Through the comparison of a large set of experimental data, Keskin and coworkers49 found

19

that the Maxwell model is the best predicting model among those based on the ideal

20

morphology concept, which has been successfully used to explore the membrane properties of

21

various MOF-based MMMs.50 It is valid for low filler loadings (0 < φ < 0.2) since this model

22

was developed based on the assumption that the streamlines around spherical particles are not

23

affected by the presence of nearby particles. Hence, φ was set as 20% in this work. The

24

membrane selectivity was calculated as the ratio of the permeabilities of the two gas

P  2 1     1  2  dm    Pm   2     1    dm 

 5

9



1

components. The reliability of Maxwell model was also validated by comparing our

2

simulation permeability of ACOF-1@Matrimid@ with the experimental permeability, as

3

shown in Figure S5 in supporting information.

4

Result and discussions

5

CoRE COFs for CO2/CH4 membrane separations. Using the methods described above,

6

membrane selectivity and CO2 permeabilities were firstly calculated to assess the performance

7

of the 298 CoRE COFs. Distinctly, CO2 permeabilities of these COFs exceed 104 Barrer,

8

which is a common case in nanoporous materials like MOFs and zeolites but not for polymers.

9

There are 12 COFs, including 8 2D-COFs and 4 3D-COFs, showing performance over the

10

2008 Robeson's upper bound with membrane selectivity ascending from 3.3 to 11458 (see

11

Figure 1a). Among them, CTF-DCN, TpMA and CTF-2-AB outperform some well-known

12

zeolite and MOF membranes such as NaX, MFI, SSZ-13, CAU-1, Cu-BTC, IRMOF-1 and

13

MIL-53. An explicit relation can be found between the membrane separation performance and

14

PLD, that is, membrane selectivity generally increases with the decreasing PLD, while the

15

CO2 permeabilities arrive at the maximum around PLD of 10 Å (see Figure 1b). For the COFs

16

with relatively good performance, several structural features can be generalized as follows.

17

Type I: structure with extremely small pores (3.3 Å < PLD < 3.8 Å, 3.8 Å the kinetic diameter

18

of CH4), such as CTF-DCN in which small CO2 molecules diffuse much faster than CH4

19

molecules ( Ds, CO2 = 1.30 × 10-4 cm2/s, Ds, CH4 = 9.31 × 10-8 cm2/s), leading to the molecular sieve

20

behavior. Type II: 2D-COFs in staggered stacking modes and 3D-COFs with interpenetrating

21

configurations. 2D-COFs of CTF-2-AB, DMTA-TPB4 and 3D-COFs of NPN-2, COF-300,

22

PI-COF-4 fall into this category. Table S1 lists the geometric characteristics and membrane

23

separation performance of the 5 eclipsed-staggered and noninterpenetrating-interpenetrating

24

counterparts. The pore size can be reduced to a considerable extent depending on the structure

10


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1

of the original 2D-sheet or the 3D-skeleton and the degree of stagger (AB or ABC) and

2

interpenetration (2, 4, or 5-fold). Although the gas diffusivities are decreased in the staggered

3

and interpenetrated configurations, the adsorption selectivity and diffusion selectivity are

4

increased, and their membrane selectivities show 3-18 times improvement over the

5

eclipsed/noninterpenetrated counterparts. Specially, CO2 diffuses slightly faster than CH4 in

6

CTF-2-AB, DMTA-TPB4 and NPN-2, suggesting both the thermodynamics and kinetics

7

factor t plays a role in separation. Type III: structures with CO2 favorable interaction sites,

8

like TpMA with hydroxyl group (-OH), TpPa-NO2 with nitro group (-NO2), and ATFG-COF

9

and NUS-2 with carbonyl group (-C=O-). With suitable interaction sites, the pore size effect

10

becomes less important. For example, TpMA with PLD of 4.5 Å show the second highest

11

membrane selectivity of 35 in the 298 CoRE COFs, even much higher than the membrane

12

selectivity of 8 for NPN-2 (PLD: 4.1 Å) and 6 for NPN-1 (PLD: 4.2 Å). In these COFs,

13

diffusion favors CH4 over CO2 (the diffusion selectivities are respectively 0.85, 0.43, 0.32 and

14

0.51 for TpMA, TpPa-NO2, ATFG-COF and NUS-22, respectively) because CO2 are strongly

15

adsorbed by the polar functional groups. Hence, the thermodynamics factor plays a

16

predominant role in the separation performance of COFs with structural features of type III. 107

2D CoRE COF

CoRE COF Robeson's upper bound

CTF-DCN TpMA CTF-2-AB DMTA-TPB4 ATFG-COF NUS-2 TpPa-NO2 TPE-COF-II

105

103

3D CoRE COF

Zn-ATZ DDR

SSZ-13

ERI CHA

Bio-MOF-13 CAU-1 NaX

101

17

103

104 (Barrer)

1.20E+04 1.08E+05 2.04E+05

103

3.00E+05 3.96E+05

2

10

101

MIL53 Cu-BTC IRMOF-1

105

PCO2 (Barrer)

104

NaY

MFI

10-1 102

NPN-2 PI-COF-4 COF-300 BF-COF-2

(b) 105

S mem CO2/CH4

(a)

S mem CO2/CH4

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


100

106

2

PCO2

10

100

PLD (Å)

18

Figure 1. (a) CO2/CH4 permeation selectivity and CO2 permeability of CoRE COFs and some

19

MOFs51-57 and zeolites58,59. (b) Relationship between CO2/CH4 permeation selectivity, PLD

20

and CO2 permeability of CoRE COFs. 11



1

Functionalization of CoRE COFs. Design of new COFs with improved separation

2

performance was further performed. According to the above structure-property relationships

3

derived from CoRE COFs, it is beneficial to incorporate the structural features of type I, II or

4

III into new COFs. The favorable performance of type I and II structural features are in fact

5

based on the pore size confining mechanism. For type I, it requires that the PLD < 3.8 Å of

6

the original 2D-sheet or 3D-skeleton; for type II, the stacking modes of the 2D-sheets or the

7

interpenetration degree of 3D-skeletons are needed to be precisely controlled to get an

8

idealized small pore size. Compared with the relatively easy computational design of COFs

9

with type I and II structural features, it is much more difficult to realize them experimentally.

10

Therefore, we focused on designing new COFs by incorporating structural feature of type III,

11

which is much more experimentally realistic.

12

The key of the structural feature of Type III is to produce CO2 favorable interaction sites in

13

the framework; hence, we selected 10 kinds of familiar functional groups (-Br, -CF3, -CH3, -

14

Cl, -COOH, -F, -NH2, -NO2, -OH and -SO3H) to decorate 11 2D-COFs (HPB-COF, CTF-

15

FUM, CTF-1, COF-6, BLP-2H, HAT-COF, CTF-2, HBC-COF, ACOF-1, TAPB-TFP and

16

TRIPTA). According to the obtained structure property relationship, structures with pore size

17

smaller than ~10 Å are expected to show good performance. Hence, we selected CoRE COFs

18

that have modifiable sites for functional groups and pore size of 3.8~12 Å (PLD of 5.8~11.9

19

Å and LCD of 7.6~12.4 Å) to ensure most of the modified structures have pore size not larger

20

than 10 Å. In addition, CoRE COFs that have already been functionalized, like TpMA (-OH),

21

TpPa-NO2 (-NO2), etc., were not considered for further design. In our design, a COF was only

22

modified by one kind of functional group, while the number of the functional group is

23

variable as long as there are modifiable sites for them. For instance, the 4 -H atoms of

24

benzene in the ligand can be 1, 2 or 4 replaced by the functional groups. If the functional

25

group has close contact with the framework atom or adjacent functional group, this modified 12


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1

structure is discarded. In this condition, large functional groups are harder to be modified on

2

COFs than the small ones although they have been tried to be employed in each COF with the

3

equal probability. Finally, we got 303 modified COFs, which were further optimized by using

4

the Forcite module in Material Studio 7.0. Among them, 290 modified COFs show PLD > 3.8

5

Å, which were further evaluated as membrane materials for CO2/CH4 separation. The lake

6

blue bars in Figure 2a depict the distribution of the functional groups held by the 290

7

modified COFs. Besides, the performance of 7 modified COFs with 3.3 Å < PLD < 3.8 Å are

8

given in the Table S2.

9 10 11

Table 1. Geometric characteristics and membrane separation performance of the 11 CoRE COFs and their functionalized configurations with the highest membrane selectivities. COF Name

Functional Groupa

LCD (Å)

PLD (Å)

Ds, CO2

Ds, CH 4

(10-5 cm2/s) (10-5 cm2/s)

ads

diff

S CO2 /CH 4 S CO2 /CH 4

mem

S CO2 /CH 4

PCO

2

PCH

4

(Barrer)

(Barrer)

∆ Qst (kJ/mol)

HPB-COF

/

7.6

5.8

3.50

3.91

2.60

0.89

2.32

54593

23508

4.7

CTF-FUM

/

6.7

6.0

1.52

0.96

2.76

1.59

4.38

26297

6000

4.1

CTF-1

/

8.9

8.4

4.21

4.77

2.68

0.88

2.36

77207

32673

4.1

COF-6

/

9.1

8.6

1.43

2.02

5.50

0.71

3.88

48671

12559

7.0

BLP-2H

/

9.5

9.0

4.43

4.70

2.73

0.94

2.57

83745

32573

4.3

HAT-COF

/

9.9

9.1

7.38

10.17

2.40

0.73

1.74

104929

60235

3.8

CTF-2

/

11.0

10.6

8.95

9.90

2.05

0.90

1.85

127089

68666

3.2

HBC-COF

/

10.8

10.6

9.77

14.34

2.17

0.68

1.48

125180

84780

3.7

ACOF-1

/

11.6

11.1

7.44

10.16

2.63

0.73

1.93

152353

78992

4.7

TAPB-TFP

/

12.0

11.6

3.38

9.55

4.91

0.35

1.74

109939

63270

6.4

TRIPTA

/

12.4

11.9

2.87

10.51

6.44

0.27

1.76

108927

61878

7.3

HPB-COF_No18

-Cl

6.2

5.5

0.35

0.48

43.11

0.72

31.18

13086

420

10.9

CTF-FUM _No6

-F

5.8

5.6

0.61

0.51

9.73

1.20

11.63

15573

1339

8.3

CTF-1_No7

-Cl

6.7

6.4

0.15

0.16

197.72

0.95

187.87

6516

35

18.9

COF-6 _No8

-NO2

7.8

7.3

0.73

1.10

59.92

0.67

39.91

29728

745

16.0

BLP-2H _No5

-F

8.9

8.6

1.24

1.49

40.78

0.83

34.00

57383

1688

12.5

HAT-COF _No25

-F

9.5

8.8

2.12

2.36

18.21

0.90

16.35

94672

5791

10.6

CTF-2 _No8

-Cl

8.9

8.7

0.51

1.60

84.23

0.32

26.77

24562

918

13.9

HBC-COF _No15

-Cl

9.4

9.2

2.84

3.68

10.99

0.77

8.49

115423

13603

9.3

ACOF-1_No4

-Cl

8.0

7.7

1.26

1.66

20.18

0.76

15.28

51575

3375

11.4

13



Page 14 of 25

TAPB-TFP_No7

-Cl

10.1

9.7

1.91

4.04

18.11

0.47

8.58

85361

9949

9.6

TRIPTA _No7

-Cl

9.9

9.6

2.11

3.44

16.68

0.61

10.22

89872

8795

10.1

The given modified structures may have different kinds, numbers and sites of functional groups as long as they have the highest membrane selectivities in each the 11 series of modified CoRE COFs a

1

The membrane separation performance of the 290 modified COFs is shown in Figure 2b,

2

which have been classified into 11 groups marked by the name of the pristine CoRE COFs.

3

As can be seen from Figure 2b, functional groups play a quite positive role in improving the

4

membrane separation performance, making 137 of the 290 modified COFs show performance

5

over the Robeson's upper bound with membrane selectivity ranging from 3.0 to 194 and CO2

6

permeability ranging from 5095 to 518940. Membrane selectivities of the 137 modified COFs

7

were divided into 4 intervals of 3 ≤ S CO

8

S CO2 /CH 4 ≥ 100. The number of modified COFs in each interval were counted and subdivided

9

according to the functional groups they hold, as shown in Figure 2a. Through investigating

10

the diffusivities of the 290 modified COFs, we found that diffusion of CO2 and CH4 slows

11

down in 256 modified COFs compared with that in the pristine CoRE COFs. Interestingly, -

12

NH2 and -CH3 are the major functional groups that can make the diffusion of CO2 and CH4

13

faster than the unmodified COFs, as shown in Table S3. However, the incorporation of

14

electron-donating groups of -NH2 and -CH3 only elevate one COF and even none over the

15

Robeson's upper bound (see Figure 2a), suggesting that they contribute little to improve the

16

CO2/CH4 separation performance although they can promote the gas diffusions. In the interval

17

of 20 ≤ S CO

18

and -CF3 show good separation performance (yellow bars in Figure 2a). In the interval of 50

19

≤ S CO

20

Figure 2a). In the interval of S CO

21

them stand out (navy bars in Figure 2a). Take a careful look at the top 26 modified COFs with

22

S CO2 /CH 4 ≥ 20, it is found that 13 of them are derived from the decoration of -F and -Cl. Take

23

another look at the 11 CoRE COFs, the highest membrane selectivities they can achieve by

24

functionalization are present in Table 1. The introduction of functional groups contributes

25

more to improve the adsorption selectivity than the diffusion selectivity. Inevitably, diffusion

26

of CO2 is slowing down after functionalization due to the reduced pore and the attraction from

mem 2

/CH 4

mem

< 20, 20 ≤ S CO

2

/CH 4

mem

< 50, 50 ≤ S CO

2

/CH 4

< 100 and

mem

mem

mem 2

/CH 4

2

/CH 4

< 50, 22 modified COFs with functional groups of -F, -OH, -Cl, -Br, -NO2

< 100, only 1 modified COF thanks to -Br making its contribution (blue bars in mem 2

/CH 4

≥ 100, 3 modified COFs with -Cl, -CF3 and -SO3H make

mem

14


Page 15 of 25

1

the electronegative functional groups. CH4 also diffuses slower in modified structures owning

2

to the reduced pore and the hindrance from the more adsorbed CO2 molecules. Hence, in most

3

modified COFs, the preferable adsorptive interaction contributes to the high membrane

4

selectivity. Impressively, -F and -Cl groups account for 10 of the best COFs. With the

5

observation of adsorption snapshots, it is found that the small single-atom groups of -F and -

6

Cl with strong electronegativity can cooperate well with the surrounding framework atoms to

7

interact with the CO2 molecules. Compared with other functional groups, -F and -Cl are more

8

adaptable to various pores to create synergy, as shown in Figure S6, which are promising

9

functional groups for COF modification. In addition, we can see that CTF-1_No7 and CTF-

10

2_No8 have obviously larger CO2/CH4 adsorption selectivities, especially in comparison with

11

other -Cl functionalized structures. To reveal the underlying mechanism, the isosteric heats of

12

adsorption (Qst) were calculated. As can be seen from Table 1, the differences of Qst (△Qst)

13

between CO2 and CH4 for CTF-1_No7 and CTF-2_No8 are larger than the other -Cl

14

functionalized structures even though some of them have smaller PLD values. This also holds

15

true for -F functionalized COFs. Since the modified structures all show PLDs larger than 3.8

16

Å, that is, molecular sieving effect cannot happen in these modified COFs. In this condition,

17

the interactions between adsorbents and gas molecules play the predominant role in the

18

separation performance. The larger △Qst in CTF-1_No7 and CTF-2_No8 suggest a better

19

synergy effect between the -Cl and the frameworks, which is not only dependent on pore size

20

but also the other factors constituting the pore environment like pore shape and the element

21

distribution, etc. (a) Number of modified COFs

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


50

Total modified COFs mem 3 ≤SCO

Screening and design of covalent organic framework membranes for

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