High-Throughput Computational Screening of ... - ACS Publications

Subscriber access provided by University of Sussex Library

Article

High-Throughput Computational Screening of Metal-Organic Frameworks for Thiol Capture zhiwei qiao, Qisong Xu, Anthony K. Cheetham, and Jianwen Jiang J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b07758 • Publication Date (Web): 19 Sep 2017 Downloaded from http://pubs.acs.org on September 19, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

The Journal of Physical Chemistry C is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 20

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

The Journal of Physical Chemistry

High-Throughput Computational Frameworks for Thiol Capture

Screening

of

Metal− −Organic

* Zhiwei Qiao,† Qisong Xu,† Anthony K. Cheetham,‡§ and Jianwen Jiang † †Department of Chemical and Biomolecular Engineering, National University of Singapore, 117576, Singapore ‡Department of Materials Science and Engineering, National University of Singapore, 117576, Singapore §Department of Materials Science and Metallurgy, University of Cambridge, CB3 0FS, United Kingdom

ABSTRACT: We report high-throughput computational screening of 137953 hypothetical metal-organic frameworks (hMOFs) and 4764 computation-ready experimental MOFs (CoREMOFs) for the capture of thiols (methanethiol and ethanethiol) from air. To minimize the competitive adsorption of moisture, 31816 hydrophobic MOFs are first identified on the basis of a threshold criterion in the Henry constant of water, and then used to assess the adsorption capacity of thiol (NSH) and the selectivity of thiol over air (SSH/Air). The highest NSH and SSH/Air are predicted to be 70.86 mg/g and 2.6 × 107, respectively. Most of the high-performance MOFs are found to be hMOFs. The structure-property relationships are derived for NSH and SSH/Air with MOF descriptors (including the isosteric heat, the largest cavity diameter, surface area and void fraction). While the relationship with isosteric heat tends to be monotonic, there exist optimal ranges in the other relationships. Principal component analysis is applied to assess the interrelationships among the four descriptors, then multiple linear regression is used to quantitatively determine the respective effects of descriptors on NSH and SSH/Air. It is revealed that the isosteric heat is a key descriptor governing thiol capture. Moreover, decision tree modelling is employed to define a clear effective path to screen high-performance MOFs. Finally, the best MOFs are identified. The microscopic insights obtained from our bottom-up approach are useful towards the development of MOFs and other nanoporous materials for thiol capture from air or in a variety of environmental and industrial situations.

* [email protected] 1

ACS Paragon Plus Environment


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

1. Introduction Organic thiols, also known as mercaptans, are used in wide variety of industrial applications.1 For example, methanethiol, CH3SH (MeSH), is primarily used to produce methionine, which is a dietary component in poultry and animal feed. It is also used in the plastics industry as a moderator for free-radical polymerization and as a precursor in pesticide manufacturing. In addition, MeSH and ethanethiol, CH3CH2SH (EtSH), can be used for communication due to their pungent odor during mining operation, as their release into a ventilation system will alert mining workers in the event of an emergency. Moreover, a small amount of thiol is added as an odorant into colorless and odorless natural gas to impart a noticeable smell in the event of a gas leak, which may pose a threat of fire, explosion or asphyxiation. Since these thiols have a distinctive putrid smell, their presence in the environment can be a matter of serious concern, often affecting the quality of life. In air, minor quantities of thiols are the origin of undesirable odors from foulsmelling fruits (e.g. durian) and vegetables (e.g. onion, garlic and leek), as well as body odors.2 Indeed, EtSH is the smelliest known substance according to the latest edition of the Guinness World Records, with a threshold for human detection as low as one part in 2.8 billion parts of air (0.00019 mg/L).3 A handful of techniques exist for the removal of odorous thiols, such as chemical reaction, solvent absorption, porous solid adsorption and their combinations.4,5 Among these, adsorption is commonly adopted because it is energetically and economically efficient. In the past, porous materials such as carbons and zeolites have been examined for thiol adsorption. However, these materials do not possess sufficiently high adsorption capacities or are difficult to regenerate.6 There is therefore a continuous quest for new adsorbents with improved performance. During the last two decades, metal−organic frameworks (MOFs) have emerged as a special class of hybrid porous materials, attracting considerable attention.7 They can be assembled from a very wide range of inorganic and organic building blocks, and the diversity and multiplicity of MOF structures are far more extensive than any other porous materials. Consequently, MOFs are considered as versatile materials for gas storage, separations, catalysis, drug delivery, etc.8 A great deal of effort has focused on gas storage and separations; for instance, computational screening of MOFs for the storage of low-carbon footprint energy carriers (e.g. CH4 and H2) and the separations of CO2-containing gas mixtures have been widely studied.9,10 Snurr and coworkers simulated the adsorption of pure CO2, N2 and CH4 in 137953 hypothetical MOFs 2


Page 2 of 20

Page 3 of 20

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


(hMOFs), then proposed relationships between structural characteristics (e.g. pore size, volume, surface area) and adsorbent evaluation criteria for CO2/CH4 and CO2/N2 separations.11 From simulations at infinite dilution and pore size analysis, Sholl and coworkers screened 504 MOFs for H2/CH4 separation12 and 1163 MOFs for CO2/N2 separation.13 Using advanced machine learning algorithms, Woo and coworkers recognized high-performance MOFs among 292050 hypothetical candidates for CO2 adsorption at 0.15 and 1 bar, respectively.14 Smit and coworkers screened hundreds of thousands of zeolitic MOFs, as well as zeolites, and identified potential adsorbents for CO2/N2 separation.15 Based on parasitic energy, they evaluated over 60 different materials including MOFs for carbon capture.16 Furthermore, they compared CH4 uptakes in over 650000 materials and identified the performance limits for CH4 storage.17 We screened 4764 computation-ready experimental (CoRE) MOFs for CO2/CH4 and CO2/N2 separations, and established quantitative relationships between metal type and adsorbent evaluation criteria,18 and further screened 137953 HMOFs for membrane separation of a CO2/N2/CH4 mixture.19 By contrast, only a few studies have been reported on the adsorption of thiols in MOFs. For instance, both IRMOF-3 and MOF-199 were tested in a fixed column to measure the breakthrough performance of EtSH.20,21 Selective adsorption of tert-butylthiol (CH3)3CSH (TBM) from natural gas was examined in Cu-BTC, MIL-53, UiO-66 and ZIF-8, and compared with that in zeolite NaY.22 Several CoRE-MOFs were identified to be good candidates for TBM removal.23 In this study, we computationally screen a large collection of 142717 MOFs for the capture of MeSH and EtSH from air. As mentioned above, a minor quantity of thiols in air can cause undesirable odors and thus should be removed. Considering the fact that moisture (water) is normally present in air and it competes with thiols for adsorption, hydrophobic MOFs are first identified; from this subset, the best MOFs are found for thiol capture. Following this introduction, the molecular models of 142717 MOFs, MeSH and EtSH, as well as air and water, are described in Section 2; the screening methods are also outlined. In Section 3, the Henry constants of water in MOFs are presented to identify hydrophobic MOFs; then, univariate analysis is used to explore the relationships of adsorption capacity and selectivity with several descriptors of MOFs (e.g. the largest cavity diameter, surface area, void fraction and isosteric heat). Finally, multivariate analysis is conducted, including principal component analysis (PCA) to assess the interrelationships among the descriptors and multiple linear regression (MLR) to quantitatively estimate the key descriptors governing thiol capture. Moreover, decision tree (DT) 3



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 20

modelling is employed to define a clear path to identify high-performance MOFs. It is worthwhile noting that PCA, MLR and DT methods are rarely used in the literature for MOFs. The concluding remarks are summarized in Section 4.

2. Models and Methods 2.1. Molecular Models The MOFs considered in the present work include 137953 hypothetical MOFs (hMOFs) and 4764 CoRE-MOFs. The hMOFs were computational generated by Wilmer et al.24 using a library of 102 building blocks derived from the crystallographic data of known MOFs with 6 topologies. By contrast, the 4764 CoRE-MOFs have been experimentally synthesized with over 350 topologies, and their structures were compiled from the Cambridge Structural Database (CSD) by Chung et al.25 The atoms of the MOFs were described by Lennard-Jones (LJ) plus electrostatic potentials

 σ uLJ+elec ( r ) = ∑ 4ε ij  ij  rij 

12 6   σ ij   qi q j  −    + ∑ 4πε 0 rij   rij  

(1)

where εij and σij are the well depth and collision diameter, rij is the distance between atoms i and j, qi is the atomic charge of atom i, and ε0 = 8.8542 × 10-12 C2 N-1 m-2 is the permittivity of vacuum. As listed in Table S1, the LJ potential parameters were adopted from the universal force field (UFF).26 While the UFF can fairly well predict gas adsorption in a wide variety of MOFs,27 we should note that it may be not reliable for polar gases such as MeSH and EtSH in MOFs with open metal sites or nonframework ions. As discussed below, these MOFs are highly hydrophilic with strong affinity for water, which would compete with thiols for adsorption, thus are excluded from our screening. The atomic charges of MOFs were estimated using the MEPO-QEq method, which can rapidly generate atomic charges and has been validated for CO2 adsorption in thousands of MOFs.28 The structural descriptors of the MOFs were characterized by the largest cavity diameter (LCD), the volumetric surface area (VSA), and the void fraction φ. The LCD was estimated using Voronoi tesselation by Zeo++;29 the VSA and φ were determined by RASPA30 using a probe with diameter of 3.64 Å (for nitrogen) and a probe with diameter of 2.58 Å (for helium), respectively.

4


Page 5 of 20

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


The adsorbates MeSH and EtSH were represented by a united-atom model with CHx as a single interaction site (see Figure S1). In addition to the LJ and electrostatic potentials, there exist intramolecular bending potentials in MeSH and EtSH, as well as a torsion potential in EtSH,

ubending (θ ) = 0.5 kθ (θ − θ o ) 2 u torsion (ϕ ) = c0 + c1 [1 + cos ϕ ] + c2 [1 − cos(2ϕ )] + c3 [1 + cos(3ϕ )]

(2) (3)

where θ and ϕ are the bending and torsional angles, respectively; kθ and ci are force constants. These parameters are listed in Table S2, as adopted from the transferable potentials for phase equilibria (TraPPE) force field.31 Air was mimicked by a mixture comprising primarily N2 and O2, which were modelled separately as a three-site molecule with a partially charged center-ofmass. To represent moisture, water was described by the TIP4P-Ew model.32 Table S3 lists the LJ parameters and charges of N2, O2 and water. The cross interactions between unlike species were estimated by the Lorentz-Berthelot combining rules. 2.2. Screening Methods To minimize the competition of water with thiol for adsorption, hydrophobic MOFs were identified by calculating the Henry constants KH of water in all the 142717 MOFs at 298 K. The calculation in each MOF was conducted by the canonical Monte Carlo (MC) simulation using a single water molecule. Based on a threshold KH, the hydrophobic MOFs were identified and then further examined for the selective adsorption of MeSH and EtSH from air. Air was represented by a mixture of 78% N2, 22% O2, 10 ppm MeSH and 10 ppm EtSH, reflecting the low levels of thiols likely to be present in air. The mixture adsorption in each hydrophobic MOF was simulated by the grand canonical MC method at 298 K and 1 bar. In addition, the isosteric heats o st

Q

of MeSH and EtSH at infinite dilution were estimated in the hydrophobic MOFs. All the

simulations were carried out using RASPA.30 The frameworks were assumed to be rigid with atoms frozen. The simulation cells were expanded to at least 24 Å along each dimension with periodic boundary conditions imposed in three dimensions. A spherical cut-off of 12 Å with long-range correction was used to calculate the LJ interactions, whereas the electrostatic interactions were calculated using the Ewald summation. Each simulation was run for 10000 cycles, with the first 5000 cycles for equilibration and the last 5000 cycles for ensemble averages. Each cycle consisted of n trial moves (n: the number of adsorbate molecules), including

5



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

translation, rotation, regrowth and swap. Further increase in the number of cycles was found to have an insignificant effect on the simulation results.

3. Results and Discussion 3.1. Henry Constants of Water Figure 1 shows the Henry constants KH of water in 142717 MOFs. At a small porosity φ, the KH is small due to the limited pore volume leading to unfavourable water-framework interactions. With increasing φ, the KH first increases and then drops to a certain extent; finally, the KH tends to level off at a large φ because of weak water-framework interactions in highly porous MOFs. The KH in CoRE-MOFs is generally larger than in the hMOFs because a large portion of CoREMOFs contain open metal sites or nonframework ions.18 We also simulated the isotherm and KH of water in ZIF-8. As illustrated in Figure S2, the predicted water isotherm agrees well with experiment. Based on the fact that ZIF-8 is a hydrophobic MOF, KH = 2.6 × 10-6 mmol/g/Pa in ZIF-8 is used as a threshold criterion to determine the hydrophobicity of MOFs. This scenario, in addition to water condensation occurring in ZIF-8 at 80% relative humidity, was used by Snurr and coworkers to identify hydrophobic MOFs.33 Here, any MOF with KH < 2.6 × 10-6 mmol/g/Pa is considered hydrophobic. Overall, 31816 MOFs are hydrophobic, comprising 31399 out of 137953 (22.8%) hMOFs and 417 out of 4764 (8.8%) CoRE-MOFs. With negligible water adsorption, these hydrophobic MOFs are unlikely to possess open metal sites or nonframework ions, and further screened for the capture of MeSH and EtSH from air, as described below. It is worthwhile to note that open metal sites or nonframework ions in MOFs can also be detected from geometrical consideration.

6


Page 6 of 20

Page 7 of 20

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


Figure 1. KH ~ φ relationship. The red dashed line represents KH = 2.6 × 10-6 mmol/g/Pa in ZIF-8, as a threshold criterion for hydrophobicity.

3.2. Univariate Analysis From the adsorption capacity of MeSH and EtSH (NSH = NMeSH + NEtSH) and selectivity (SSH/Air = NSH⋅yAir/NAir⋅ySH where yi is the mole fraction in air) in the 31816 hydrophobic MOFs, univariate analysis is conducted on the basis of a single descriptor of MOFs. As shown in Figure 2, the NSH ~ φ and SSH/Air ~ φ relationships are largely similar on a semi-logarithmic scale, and are also similar to NCH4 ~ φ for CH4 storage.24,34 However, the NCH4 ~ φ relationship was plotted on a linear scale because the NCH4 was determined at a very high pressure (i.e. 35 bar). The patterns of the two sets of MOFs, hMOFs and CoRE-MOFs, are broadly similar. As pointed out earlier, there are over 350 topologies in CoRE-MOFs, compared with only 6 topologies in hMOFs; therefore, the diversity of topologies appears not to be crucial here. The highest NSH and SSH/air are 70.86 mg/g and 2.6 × 107 in hMOFs, respectively, and 50.7 mg/g and 8.1 × 105 in CoRE-MOFs. For NSH, approximately 13.1% of the 31816 MOFs have NSH > 0.3 mg/g, 1.2% have NSH > 10 mg/g, and 0.1% have NSH > 40 mg/g when φ is in the range 0.23 – 0.74; moreover, most of those with NSH > 40 mg/g are hMOFs. For SSH/air, 0.1% of MOFs have SSH/Air > 106 when

φ is in the range 0.09 – 0.35, and they are all hMOFs. The optimal φ for NSH and SSH/air are intermediate values. The reason for this is that thiol in air is very dilute (20 ppm) and its adsorption is primarily governed by thiol-framework interaction. In MOFs with a small φ, the interaction is not favorable for adsorption; on the other hand, the interaction is weak in MOFs

7



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 20

with a large φ. Only in the intermediate range of φ, the interaction is optimal leading to high NSH and SSH/air. (a)

(b)

■ ■

■ ■

hMOFs CoRE-MOFs

hMOFs CoRE-MOFs

Figure 2. (a) NSH ~ φ (b) SSH/Air ~ φ relationships. o st

Figure 3 shows the NSH ~ Q adsorbed, thus the

o Q st

o

and SSH/Air ~ Q

st

relationships. EtSH is the major species o st

refers to EtSH rather than MeSH. In most of the MOFs, the Q

is > 20

kJ/mol. The relationships resemble those of CO2 adsorption.11 On a semi-logarithmic scale, NSH o

and SSH/Air, particularly the latter, appear to increase monotonically with Q st. The highest NSH is o st

at Q

o st

of 52 – 69 kJ/mol, while the highest SSH/air is at 63 – 78 kJ/mol. Q

appears to be a key

descriptor governing thiol adsorption due to the very low pressure of thiol in air. From both Figures 2 and 3, SSH/Air may seem to be simply proportional to NSH. To a certain extent this is true, as shown in Figure S3, but it is not always the case. For instance, Table S4 lists the performance of two hMOFs, as also indicated on Figure 3. The NSH are 70.86 and 26.85 mg/g, and the SSH/Air are 7.85 × 105 and 1.36 × 107, respectively. In other words, the first hMOF (#1004479) has a larger NSH but a lower SSH/Air. This is because the first hMOF possesses a larger φ and LCD, and the NSH is higher therein; nevertheless, the NAir also increases thus leading to a lower SSH/Air.

8


Page 9 of 20

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


#

(a)

#

1004479

(b)

■ ■

# ■ ■

hMOFs CoRE-MOFs

#

7000853

1004479

hMOFs CoRE-MOFs

7000853

Figure 3. (a) NSH ~ Qost (b) SSH/Air ~ Qost relationships.

The NSH ~ LCD and SSH/Air ~ LCD relationships are plotted in Figure 4. The trend of NSH ~ LCD is similar to the adsorption of CH434 and CO2,11 while SSH/Air ~ LCD resembles that for CO2 separation.11,18 The optimal LCD is 4.5 – 8.5 Å for NSH and 4.5 – 7.5 Å for SSH/Air. If the LCD is very small, adsorption is impeded due to unfavourable potential overlap with the framework; on the other hand, if the LCD is too large, the interaction with the framework is weak. In both cases, the NSH and SSH/Air are low. Figure 5 further illustrates the NSH ~ VSA and SSH/Air ~ VSA relationships. If the VSA is close to zero, it indicates that the framework has a negligible surface area and cannot accommodate a guest molecule. On a logarithmic scale, similar trends were also observed for CO2 separation.11 For NSH, the optimal VSA ranges from approximately 150 – 1550 m2/cm3; whereas the optimal VSA for SSH/Air is 80 – 640 m2/cm3. o st

From Figures 2 – 5, it is clear that Q

has more distinct relationships with NSH and SSH/Air o st

compared to φ, LCD and VSA. This suggests that Q

is a better descriptor for the screening of

MOFs for thiol capture under the conditions of the current study. It is also clear that the best MOFs are almost all hMOFs, although the CoRE-MOFs span a substantially broader variety of topologies. Again, this implies that a wide range of textural properties play a more important role than topologies in governing thiol capture.

9



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

(a)

(b)

Page 10 of 20

■ ■

■ ■

hMOFs CoRE-MOFs

hMOFs CoRE-MOFs

Figure 4. (a) NSH ~ LCD (b) SSH/Air ~ LCD relationships.

(a)

(b)

■ ■

■ ■

hMOFs CoRE-MOFs

hMOFs CoRE-MOFs

Figure 5. (a) NSH ~ VSA (b) SSH/Air ~ VSA relationships.

3.3. Multivariate Analysis To further elucidate the relationships between NSH and SSH/Air and the structural descriptors (φ, LCD and VSA) as well as energetic descriptor (Qost) of MOFs, a series of multivariate statistical tools are utilized, including PCA, MLR and DT, to quantitatively assess and determine these relationships towards the screening of MOFs for thiol capture. Firstly, the PCA is applied to assess the interrelationships among the four descriptors. It is a useful statistical technique for exploring the relationships among a set of variables without any prior knowledge.35 This technique allows dimensionality reduction of multivariate data to be projected into a lower dimensional space, thereby providing a concise representation of essential information. To do this, the values of φ, Qost, LCD and VSA are mean-centered, scaled and then 10


Page 11 of 20

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


decomposed into principal component score and eigenvector matrices. As listed in Table S5, the first principal component (PC) can describe approximately 75.7% of the data variance of the original four descriptors, while the first two PCs together are able to account for 92.1%. Therefore, the four descriptors are projected onto a two-dimensional space. The score matrix contains the coordinates for MOFs in a new coordinate system defined by the first two PCs (PC1 and PC2), while the eigenvector matrix contains the coefficients for eigenvectors V1, V2, V3 and V4 in the new coordinate system (see Table S6). This information can be comprehended visually

by the biplots in Figure 6, where the respective MOFs are displayed as scattered points and the four original descriptors are represented as the eigenvectors. The origin of the PCA biplots is the location of averages of the four descriptors. The coloration of the points is classified into ten scales, in which the higher the scale, the better is the performance. (a) NSH

(b) SSH/Air

o

Figure 6. Biplots for eigenvectors V1, V2, V3 and V4 (corresponding to φ, Q st, LCD and VSA, respectively).

Based on the biplots in Figure 6, the eigenvectors are well spread out, indicating that the four descriptors are fairly uncorrelated. Given that most data variance is described by PC1, its coefficients can be indicative of the trends among the descriptors. In particular, the sign of the coefficients of PC1 suggests a negative correlation between the structural descriptors (φ, LCD and VSA) and energetic descriptor (Qost). These results imply that MOFs with high Qost tend to have low φ, LCD and VSA. In Figure 6, the data points for MOFs with large NSH and SSH/Air are located in the positions where Qost (V2) are high. Therefore, the PCA analysis suggest that MOFs with good performance for thiol capture typically display high Qost and relatively low φ, LCD and VSA. 11



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 20

In view of the uncorrelated descriptors, MLR is then used to quantitatively determine the effects of descriptors on the performance of MOFs for thiol capture. MLR utilizes a linear model to relate two or more predictor variables with a response variable.36 At the same time, MLR suggests the relative importance of each descriptor. The values of φ, Qost, LCD, VSA, log(NSH) and log(SSH/Air) are mean-centered and scaled by the respective standard deviations. The standardized regression coefficients of the descriptors are obtained for log(NSH) and log(SSH/Air), respectively, by least-squares fitting. A large absolute value of standardized regression coefficient indicates the high importance of the particular descriptor. As listed in Table 1, Qost has the highest absolute coefficient for both log(NSH) and log(SSH/Air), and it is thus the most influential descriptor. Figure S4 shows that the R-squared values (R2) using the Qost of EtSH are 0.83 and 0.86, which are higher than those using the Qost of MeSH. This justifies the use of Qost of EtSH, the major adsorbed species, as stated above. Among the four descriptors, Qost, φ and VSA have larger standardized regression coefficients compared with LCD. This indicates that Qost, φ and VSA are more important than LCD because the latter carries only very specific structural information (i.e. the largest cavity). Consequently, they are used in the DT method to screen high-performance MOFs. Table 1. Standardized regression coefficients of descriptors φ, Qost and VSA. Descriptor

log(NSH)

log(SSH/Air)

0.16

−0.09

1.15

1.08

LCD

−0.04

0.05

VSA

0.25

0.29

φ Q

o st

A DT model uses a tree-like graph and binary rules to analyze multiple variables and reach a target.37 It has been used in the analysis of CH4 storage in MOFs.34 Figure 7 illustrates the DT graph to screen 31816 hydrophobic MOFs for thiol capture. With two targets NSH > 5 mg/g and SSH/air > 15000, 712 MOFs (2.24 % of 31816) can be identified. The decision rules contain three o

o st

levels in terms of Q st, φ and VSA. In the range Q

> 55.0 kJ/mol, φ < 0.35 and VSA > 270 o st

m2/cm3, 81.1% of MOFs can be found with targeted performance. Meanwhile, in the range Q

>

55.0 kJ/mol, φ > 0.35 and VSA < 690 m2/cm3, 78.1% of MOFs have NSH > 5 mg/g and SSH/air > 12


Page 13 of 20

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


o st

15000. The DT model further suggests that Q

has an important influence on MOF performance.

o

The combination of Q st, φ and VSA in the DT model provides a simple and remarkably effective pathway to screen MOFs for thiol capture, so the high- and low-performance MOFs can be clearly discriminated by the three-level decision rules.

Figure 7. DT graph for NSH > 5 mg/g and SSH/air > 15000 in hydrophobic MOFs. The optimal and suboptimal routes are highlighted in red and green, respectively.

3.4 Best MOFs Based on the performance of NSH > 40 mg/g and SSH/air > 106, six best hMOFs can be identified as listed in Table 2. Notably, Nos. 1 and 2 exhibit the highest NSH of 70.74 mg/g and the highest SSH/air of 1.87 × 106, respectively. All these six MOFs are hMOFs, although we also include in Table 2 the eight top CoRE-MOFs with both NSH > 30 mg/g and SSH/air > 105. For o st, EtSH,

these fourteen MOFs, φ, Q

LCD and VSA are in the ranges of (0.12 – 0.41), (56.48 –

68.75 kJ/mol), (4.62 – 7.39 Å) and (83 – 639 m2/cm3), respectively. These ranges are consistent with the optimal values observed in Figures 2 – 5, and they are also located in the optimal or suboptimal route of the DT model. The gravimetric capacity of EtSH in the fourteen hMOFs ranges from 20.87 to 69.45 mg/g, which is close to, or higher, than those in zeolites,6 although the partial pressure of EtSH in our study is only 1 Pa (10 ppm). On a volumetric base, the 13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 20

capacity ranges from 33.79 to 120.99 mg/cm3, and they are about 1.5 times those in zeolites.6 Consequently, the performance of MOFs for thiol capture is far superior to zeolites on both gravimetric and volumetric bases.

Table 2. Best MOFs. o

No.

a

ID

Density (kg/m3)

φ

Q

N (mg/g)

(kJ/mol)

N (mg/cm3)

LCD (Å)

VSA (m2/cm3)

MeSH

EtSH

MeSH

EtSH

MeSH

EtSH

SSH/air (×105)

st

1

5066294a

2141.76

0.26

5.18

272.19

52.23

68.75

0.74

42.35

1.58

90.70

18.70

2

5070946

1328.53

0.30

5.53

339.13

51.36

66.76

1.28

69.45

1.70

92.27

17.53

3

5051028

1609.42

0.26

5.38

291.92

50.54

65.52

1.00

39.79

1.61

64.04

11.94

4

1004323

2911.31

0.41

7.39

620.59

55.39

67.87

2.01

41.56

5.85

120.99

11.83

5

5051025

1609.41

0.27

5.38

276.19

50.70

65.38

1.05

39.62

1.69

63.76

10.53

6

5045512

1635.78

0.27

4.62

255.27

52.71

67.84

1.22

43.22

2.00

70.70

10.20

1

CUMDIYb

2011.73

0.27

5.06

433.98

47.46

59.07

2.82

47.85

5.67

96.26

8.15

2

FALQOA

2004.41

0.12

5.06

83.14

58.12

68.29

8.19

24.15

16.42

48.41

7.38

3

TAFCIO

1584.78

0.14

5.01

102.34

54.97

64.93

7.23

26.64

11.46

42.22

5.37

4

HAZNON

2012.40

0.24

4.93

281.93

48.46

59.72

1.74

35.08

3.50

70.59

4.46

5

WAHREE

1607.08

0.14

4.97

97.44

55.82

65.10

8.02

24.09

12.89

38.71

4.40

6

ESIPUR

1619.14

0.13

4.97

93.44

56.32

64.62

9.89

20.87

16.01

33.79

4.38

7

GEWXAJ

1696.69

0.24

4.83

394.05

46.17

56.66

2.27

28.56

3.85

48.46

2.46

8

AMAFOK

1492.47

0.38

6.60

638.95

37.00

56.48

1.01

30.17

1.51

45.03

1.96

ID for hMOF, bCSD code for CoRE-MOF.

Finally, we assess the effect of the charge estimation method on the adsorption of MeSH and EtSH. In addition to the MEPO-QEq charges, for the eight top CoRE-MOFs, the DensityDerived Electrostatic and Chemical (DDEC) charges are available.38 Based on density functional theory calculations, the DDEC charges are presumably more accurate. As shown in Figure 8, NMeSH, NEtSH, and SSH/air for the eight top CoRE-MOFs using both methods are close. This indicates that the MEPO-QEq charges are reliable and the choice of the charge estimation method has an insignificant effect.

14


Page 15 of 20

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


Figure 8. NMeSH, NEtSH, and SSH/air in eight best CoRE-MOFs using the DDEC and MEPO-QEq methods.

4. Conclusions We have performed molecular simulations to screen 142717 MOFs for the capture of thiols from air. The highest adsorption capacity is 70.86 mg/g at a set of optimal values for descriptors: o st

Q

of (52 − 69) kJ/mol and LCD of (4.5 − 8.5) Å, but for the φ and VSA, the highest capacity is

not simply in a narrow range. Meanwhile, the highest selectivity is 2.6 × 107 at different optimal o st

values: φ of (0.09 − 0.35), Q

of (63 − 78) kJ/mol, LCD of (4.5 − 7.5) Å, and VSA of (80 − 640) o

m2/cm3. In term of the univariate analysis, Q

st

is revealed to be an important descriptor

governing thiol adsorption. Compared to zeolites, MOFs have significantly better potential for thiol capture. Although hMOFs and CoRE-MOFs display a similar pattern, all the top 0.1% are hMOFs under the conditions of the current study. This reveals that the diversity of topology in o st

MOFs is not crucial. Through PCA and MLR, φ, Q

and VSA are found to play a dominant role

o

in MOF performance, particularly, Q st. On this basis, three-level decision rules are employed in a DT model to define a clear effective path for screening high-performance MOFs. Moreover, two different charge estimation methods are found to give similar results. This study reveals the key descriptors governing thiol adsorption, develops quantitative structure-property relationships, and identifies the best MOFs for thiol capture from air from a large collection of MOFs.

ASSOCIATED CONTENT Supporting Information 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 20

The Supporting Information is available free of charge on the ACS Publication website. Lennard-Jones parameters of MOFs; united-atom models of MeSH and EtSH; bond bending and torsion potentials of MeSH and EtSH; Lennard-Jones parameters and charges of N2, O2 and water; water isotherms in ZIF-8; SSH/Air ~ NSH relationship, performance of two hMOFs, principal component analysis and multiple linear regression.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]. Phone: +65-65165083. ORCID Jianwen Jiang: 0000-0003-1310-9024 Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS We gratefully thank the National University of Singapore and the Ministry of Education of Singapore (R-279-000-474-112 and R-261-508-001-646/733), and the National Natural Science Foundation of China (No. 21676094) for financial support.

REFERENCES (1) Roy, K. M. Thiols and Organic Sulphides. Wiley-VCH Verlag: Weinheim, 2000. (2) Carson, J. F. Chemistry and Biological Properties of Onions and Garlic. Food. Rev. Int. 1987, 13, 71103. (3) http://www.guinnessworldrecords.com/ (access on 06 Sep. 2017). (4) Weber, G.; Benoit, F.; Bellat, J. P.; Paulin, C.; Mougin, P.; Thomas, M. Selective Adsorption of Ethyl Mercaptan on NaX Zeolite. Microporous Mesoporous Mater. 2008, 109, 184-192. (5) Ding, L.; Liu, T.-X.; Li, X.-Z. Removal of CH3SH with in-situ Generated Ferrate(Vi) in a WetScrubbing Reactor. J. Chem. Technol. Biot. 2014, 89, 455-461. (6) Keshavarzi, N.; Mashayekhy Rad, F.; Mace, A.; Ansari, F.; Akhtar, F.; Nilsson, U.; Berglund, L.; Bergström, L. Nanocellulose-Zeolite Composite Films for Odor Elimination. ACS Appl. Mater. Interfaces 2015, 7, 14254-14262. 16


Page 17 of 20

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


(7) Yaghi, O. M.; O'Keeffe, M.; Ockwig, N. W.; Chae, H. K.; Eddaoudi, M.; Kim, J. Reticular Synthesis and the Design of New Materials. Nature 2003, 423, 705-714. (8) Furukawa, H.; Cordova, K. E.; O'Keeffe, M.; Yaghi, O. M. The Chemistry and Applications of Metal– Organic Frameworks. Science 2013, 341, 974-986. (9) Suh, M. P.; Park, H. J.; Prasad, T. K.; Lim, D. W. Hydrogen Storage in Metal–Organic Frameworks. Chem. Rev. 2012, 112, 782-835. (10) Sumida, K.; Rogow, D. L.; Mason, J. A.; McDonald, T. M.; Block, E. D.; Herm, Z. R.; Bae, T. H.; Long, J. R. CO2 Capture in Metal–Organic Frameworks. Chem. Rev. 2012, 112, 724-781. (11) Wilmer, C. E.; Farha, O. K.; Bae, Y. S.; Hupp, J. T.; Snurr, R. Q. Structure–Property Relationships of Porous Materials for CO2 Separation and Capture. Energy Environ. Sci. 2012, 5, 9849–9856. (12) Haldoupis, E.; Nair, S.; Sholl, D. S. Efficient Calculation of Diffusion Limitations in Metal–Organic Framework Materials: A Tool for Identifying Materials for Kinetic Separations. J. Am. Chem. Soc. 2010, 132, 7528-7539. (13) Watanabe, T.; Sholl, D. S. Accelerating Applications of Metal–Organic Frameworks for Gas Adsorption and Separation by Computational Screening of Materials. Langmuir 2012, 28, 14114-14128. (14) Fernanadez, M.; Boyd, P. G.; Daff, T. D.; Aghaji, M. Z.; Woo, T. K. Rapid and Accurate Machine Learning Recognition of High Performing Metal–Organic Frameworks for CO2 Capture. J. Phys. Chem. Lett. 2014, 5, 3056-3060. (15) Lin, L.-C.; Berger, A. H.; Martin, R. L.; Kim, J.; Swisher, J. A.; Jariwala, K.; Rycroft, C. H.; Bhown, A. S.; Deem, M. W.; Haranczyk, M.; Smit, B. In Silico Screening of Carbon-Capture Materials. Nat. Mater. 2012, 11, 633-641. (16) Huck, J. M.; Lin, L.-C.; Berger, A. H.; Shahrak, M. N.; Martin, R. L.; Bhown, A. S.; Haranczyk, M.; Reuter, K.; Smit, B. Evaluating Different Classes of Porous Materials for Carbon Capture. Environ. Energy Sci. 2014, 7, 4132-4146. (17) Simon, C. M.; Kim, J.; Gomez-Gualdron, D. A.; Camp, J. S.; Chung, Y. C.; Martin, R. L.; Mercado, R.; Deem, M. W.; Gunter, D.; Haranczyk, M.; Sholl, D. S.; Snurr, R. Q.; Smit, B. The Materials Genome in Action: Identifying the Performance Limits for Methane Storage. Environ. Energy Sci. 2015, 8, 11901199 (18) Qiao, Z.; Zhang, K.; Jiang, J. W. In Silico Screening of 4764 Computation-Ready, Experimental Metal–Organic Frameworks for CO2 Separation. J. Mater. Chem. A 2016, 4, 2105-2114 (19) Qiao, Z. W.; Peng, C. W.; Zhou, J.; Jiang, J. W. High-Throughput Computational Screening of 137953 Metal–Organic Frameworks for Membrane Separation of a CO2/N2/CH4 Mixture. J. Mater. Chem. A 2016, 4, 15904-15912. (20) Wang, X. L.; Fan, H. L.; Tian, Z.; He, E. Y.; Li, Y.; Ju, S. Adsorptive Removal of Sulfur Compounds Using IRMOF-3 at Ambient Temperature. Applied Surf. Sci. 2014, 289, 107-113. (21) Li, Y.; Wang, L. J.; Fan, H. L.; Ju, S.; Wang, H.; Mi, J. Removal of Sulfur Compounds by a CopperBased Metal Organic Framework under Ambient Conditions. Energy Fuels 2014, 29, 298-304. 17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 20

(22) Chen, G.; Tan, S.; Koros, W. J.; Jones, C. W. Metal−Organic Frameworks for Selective Adsorption of T-butyl Mercaptan from Natural Gas. Energy Fuels 2015, 29, 3312−3321. (23) Nazarian, D.; Camp, J. S.; Sholl, D. S. A Comprehensive Set of High-Quality Point Charges for Simulations of Metal−Organic Frameworks. Chem. Mater. 2016, 28, 785-793. (24) Wilmer, C. E.; Leaf, M.; Lee, C. Y.; Farha, O. K.; Hauser, B. G.; Hupp, J. T.; Snurr, R. Q. LargeScale Screening of Hypothetical Metal–Organic Frameworks. Nat. Chem. 2011, 4, 83-89. (25) Chung, Y. G.; Camp, J.; Haranczyk, M.; Sikora, B. J.; Bury, W.; Krungleviciute, V.; Yildirim, T.; Farha, O. K.; Sholl, D. S.; Snurr, R. Q. Computation-Ready, Experimental Metal–Organic Frameworks: A Tool to Enable High-Throughput Screening of Nanoporous Crystals. Chem. Mater. 2014, 26, 61856192. (26) Rappe, a. K.; Casewit, C. J.; Colwell, K. S.; 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. (27) Jiang, J. W., Metal–Organic Frameworks: Materials Modeling Towards Potential Engineering Applications. Pan Stanford Publishing Pte. Ltd.: Singapore, 2015. (28) Kadantsev, E. S.; Boyd, P. G.; Daff, T. D.; Woo, T. K. Fast and Accurate Electrostatics in Metal− Organic Frameworks with a Robust Charge Equilibration Parameterization for High-Throughput Virtual Screening of Gas Adsorption. J. Phys. Chem. Lett. 2013, 4, 3056-3061. (29) Willems, T. F.; Rycroft, C. H.; Kazi, M.; Meza, J. C.; Haranczyk, M. Algorithms and Tools for High-Throughput Geometry-Based Analysis of Crystalline Porous Materials. Microporous Mesoporous Mater. 2012, 149, 134-141. (30) Dubbeldam, D.; Calero, S.; Ellis, D. E.; Snurr, R. Q. RASPA: Molecular Simulation Software for Adsorption and Diffusion in Flexible Nanoporous Materials. Mol. Simul. 2016, 42, 81-101. (31) 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. (32) Horn, H. W.; Swope, W. C.; Pitera, J. W.; Madura, J. D.; Dick, T. J.; Hura, G. L.; Head-Gordo, T. Development of an Improved Four-Site Water Model for Biomolecular Simulations: TIP4P-Ew. J. Chem. Phys. 2014, 120, 9665-9678. (33) Moghadam, P. Z.; Fairen-Jimenez, D.; Snurr, R. Q. Efficient Identification of Hydrophobic Mofs: Application in the Capture of Toxic Industrial Chemicals. J. Mater. Chem. A 2016, 4, 529-536. (34) Fernandez, M.; Woo, T. K.; Wilmer, C. E.; Snurr, R. Q. Large-Scale Quantitative Structure−Property Relationship ( Qspr ) Analysis of Methane Storage in Metal−Organic Frameworks. J. Phys. Chem. C 2013, 117, 7681. (35) Jolliffe, I. T. Principal Component Analysis. Springer-Verlag: New York, 2002. (36) Freedman, D. Statistical Models: Theory and Practice. 2nd ed.; Cambridge University Press, 2009. (37) Rokach, L.; Maimon, O. Data Mining with Decision Trees. World Scientific: Singapore, 2007. 18


Page 19 of 20

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


(38) Nazarian, D.; Camp, J. S.; Sholl, D. S. A Comprehensive Set of High-Quality Point Charges for Simulations of Metal–Organic Frameworks. Chem. Mater. 2016, 28, 785-793.

19



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

Abstract Graphic

A computational study is reported to screen metal−organic frameworks for thiol capture from air.

20


Page 20 of 20

High-Throughput Computational Screening of ... - ACS Publications

Recommend Documents