Toward a New Era of Designed Synthesis of Nanoporous Zeolitic

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Toward a New Era of Designed Synthesis of Nanoporous Zeolitic Materials Yi Li,†,‡ Hongxiao Cao,† and Jihong Yu*,†,‡ †

State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, and ‡International Center of Future Science, Jilin University, Changchun 130012, China ABSTRACT: Due to their nanoporous framework structures, zeolites have been widely used as catalysts, adsorbents, and ion exchangers in many industrial fields. Discovering zeolitic materials with new structures and desired functions is one of the most important tasks in the zeolite community. Traditional zeolite discovery relies primarily on low-efficiency trial-and-error processes. So far, many computational and experimental efforts have been devoted to the designed synthesis of zeolitic materials, representing a promising highway toward function-led discovery of nanoporous materials. In particular, the design of structure-directing agents, the design of target zeolites via structure enumeration, and the reorganization of disassembled building layers have led to the discovery of dozens of unprecedented zeolitic structures in the past 5 years. In this Perspective, we briefly discuss these advances and describe the research efforts that are needed in the coming era of function-led zeolite discovery.

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Materials Genome Initiative launched by the U.S. government in 2011.14 To date, many approaches combining computational and experimental efforts in the synthesis of nanoporous zeolites have been developed.15−18 In particular, the design of SDAs, the design of target zeolites via structure enumeration, and the reorganization of disassembled building layers have led to the discovery of dozens of unprecedented zeolite structures in the past 5 years. These advances represent a significant step forward toward a new era of designed synthesis of zeolites. In this Perspective, we briefly discuss key advances in zeolite synthesis and describe what research efforts will be needed in the future. Designing Structure-Directing Agents. It is widely accepted that SDAs, which are typically positively charged organic amines, are highly correlated with the resultant zeolite structures. Therefore, the rational selection of SDAs is critical to the synthesis of target zeolite structures.19 In particular, zeolites with multidirectionally interconnected nanopores are highly desired for industrial catalysis because they can provide multiple paths for mass transfer and suitable space confinement toward the formation of specific intermediates/transition states. To synthesize such zeolites, branched tertiary and quaternary amines fitting the dimensions and the shapes of the nanopores are often used.20−22 This “hand-in-glove” fit can be further enhanced by using complicated, bulky molecules as SDAs.1,23−28 In addition to organic amines, other organic molecules, including organic phosphonium cations,29 phosphazenes,30 and organic

eolites are a family of crystalline aluminosilicates consisting of orderly distributed nanopores with molecular dimensions. Because of this key structural feature, zeolites can be used to adsorb guest molecules of specific sizes and shapes or to separate them from liquid or gas mixtures as molecular sieves.1,2 Moreover, the spatial confinement of zeolites for guest species, coupled with their internal acid or metal sites, endows zeolites with shape-selective catalysis toward the formation of specific products.3−5 Currently, zeolites are the most important solid catalysts used in traditional petrochemical industries.6−9 Zeolitic materials are also finding promising applications in many sustainable processes, such as renewable energy and environmental improvement.10 The physical and chemical properties of zeolites are determined by their intrinsic nanoporous framework structures, which are built from corner-sharing TO4 tetrahedra (“T” denotes tetrahedrally coordinated Si, Al, P, etc.). Different types of tetrahedra connection lead to a diversity of zeolite framework types.11 To date, 235 types of zeolite frameworks have been discovered,12 but they still cannot meet the increasing demands from various industries for zeolitic materials with new structures and superior functions. Due to our limited understanding of the relationships among zeolite structures, functions, and synthesis, traditional zeolite discovery has long been a slow, wasteful trial-and-error process. In 2010, Yu et al. outlined a blueprint for function-led zeolite discovery via the design of target zeolite structures and structure-directing agents (SDAs) with the aid of computational chemistry, data mining, and combinatorial synthesis.13 This idea is analogous to the © XXXX American Chemical Society

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DOI: 10.1021/acsnano.8b02625 ACS Nano XXXX, XXX, XXX−XXX

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sulfonium cations,31 can also be used as SDAs for the synthesis of zeolites with interconnected nanopores.

approach to predict a variety of inexpensive SDAs to synthesize zeolite SSZ-52 (SFW type), a potential catalyst for the selective catalytic reduction of NOx species.38 Recently, Brand et al. used this de novo approach to predict several chiral diquaternary imidazolium SDAs for chiral zeolite STW and successfully synthesized enantiomerically enriched polycrystalline STW using one of the predicted SDAs.27 This approach represents a new trend for high-throughput automated SDA design and significantly reduces the need for synthetic chemists to rely on their own empirical intuition when selecting SDAs. Despite these successes, however, function-led discovery of new zeolite structures remains challenging. For instance, if researchers require a new zeolite to catalyze a specific reaction, they first need to identify a candidate zeolite framework that possesses suitable nanopores for the target reaction. Next, they select the SDAs that may induce the formation of the candidate zeolite, either from scientific intuition or from computer modeling. After the candidate zeolite is synthesized, it must be tested for the target reaction. If the catalytic performance of the candidate zeolite is not satisfactory, the researchers then need to identify another candidate zeolite structure and start the process again. Recently, Gallego et al. proposed a transitionstate mimic (TSM) approach, which is a promising method to avoid this trial-and-error procedure.39 In comparison with conventional methods based on predefined candidate zeolites, the TSM approach focuses on the target reaction to be catalyzed. A good catalyst should interact strongly with the transition state of the target reaction and minimize the free energy of the transition state. Given the strong interactions between zeolite nanopores and their SDAs, a zeolite formed in the presence of an SDA mimicking the transition state of the target reaction should lower the free energy of the transition state, therefore lowering the activation energy of the target reaction. For instance, the production of xylenes by toluene disproportionation is carried out commercially primarily with zeolites ZSM-5 (MFI type) and mordenite (MOR type), where two toluene molecules produce one benzene molecule and one dimethylbenzene molecule via a positively charged transition state (Figure 1A). Without any predefined synthetic target, Gallego

The rational selection of structuredirecting agents is critical to the synthesis of target zeolite structures. In addition to complicated SDAs, we can also utilize the cooperation of multiple simple SDAs to induce the formation of target zeolites. A well-known example is the charge density mismatch approach, which introduces low-charge-density SDAs, such as tetraethylammonium cations, into an aluminosilicate solution with a low Si/Al ratio.32 Such a reaction system cannot crystallize because of the charge density mismatch between tetraethylammonium cations and the potential zeolite frameworks with high negative charges. By adding small amounts of high-charge-density SDAs, such as Na+ and tetramethylammonium cations, the charge density mismatch in the reaction system will be perturbed and the target zeolites will crystallize. In comparison with the usage of complicated SDAs, utilizing the cooperation of multiple, simple SDAs could be a promising way to discover new zeolite structures at a relatively low cost. Traditionally, the selection of SDAs depends mainly on the synthetic chemist’s scientific intuition. Computer modeling has provided a quantitative way to evaluate the structure-directing effect of SDAs. In general, a number of candidate SDAs are first selected for a specific zeolite framework. Then, the candidate SDAs are put inside zeolite pores and relaxed by molecular mechanics, molecular dynamics, Monte Carlo, or density functional theory (DFT) calculations. After relaxation, the SDAs exhibiting the strongest nonbonding interactions with the target zeolite frameworks are predicted to be the optimum SDAs for the corresponding zeolite frameworks. Indeed, computer modeling has successfully been used to search for cheap and effective SDAs for the synthesis of many zeolite frameworks.30,33,34 For instance, Simancas et al. identified tert-butyliminotris(dimethylamino)phosphorene as a good SDA for the elusive boggsite zeolite (BOG type) via molecular mechanics and DFT calculations and realized its synthesis using the predicted SDA;30 Turrina et al. investigated the candidate SDAs for zeolites SAPO-56 (AFX type), STA-18 (SFW type), and STA-19 (GME type) via molecular dynamics calculations and identified trimethylammonium cation and bisdiazabicyclooctane-based cations as the best SDAs for the synthesis of these zeolites.35 Thus, with the aid of computer modeling, zeolite synthesis can now be conducted in a more calculated way, and unfeasible SDAs can be avoided. To improve the efficiency of SDA design, Pophale et al. developed a de novo computational procedure for high-throughput prediction of SDAs in an automated way.36 In contrast to previous approaches, this de novo algorithm does not require a number of candidate SDAs as input. Starting from a library of all commercially available reagents, this de novo algorithm automatically creates a large number of theoretically available organic compounds by making dozens of types of known organic chemistry transformations. The theoretically created compounds are scored based on stability, rigidity, volume, geometric compatibility, and interaction energy with the target zeolites, and those with the highest scores are expected to be the best SDAs for the target zeolites. Using the SDAs predicted via this de novo approach, Pophale et al. successfully synthesized zeolites AEI, ITE, and STF,36 and Schmidt et al. realized the synthesis of STW-type zeolites.37 In a later study, Davis et al. employed this

Figure 1. Toluene disproportionation. (A) Reaction mechanism for the toluene disproportionation reaction. (B) Proposed transitionstate mimics (TSMs) used as structure-directing agents.

et al. employed several TSMs as the SDAs for zeolite synthesis (Figure 1B), among which TSM_2 led to the formation of zeolite ITQ-27 (IWV type). In comparison with commercially used ZSM-5 and mordenite, ITQ-27 exhibited higher activity and selectivity to xylenes. In addition to ITQ-27, Gallego et al. also synthesized several other high-performance zeolite catalysts, such as ITQ-64 and MIT-1, by using TSMs as SDAs.39 Although the reported zeolites were not new, the TSM approach B


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Figure 2. Enumeration of ABC-6 zeolite structures. Left: zeolites cancrinite, sodalite and chabazite. Middle: schematic drawing of the three types of 6-rings and their stacking sequences. Right: example cages extracted from the stacking sequences and selected ABC-6 structures enumerated from the stacking of five layers of 6-rings. Reprinted with permission from ref 55. Copyright 2015 Springer Nature.

cost. Meanwhile, only combinations leading to feasible zeolite structures are allowed during enumeration, so the structure evaluation step becomes unnecessary. More importantly, the hypothetical structures designed in this way are composed of known cages. By tuning the synthetic conditions for these cages, the designed zeolite structures can be realized experimentally. Following this method, two zeolite families have successively been designed and synthetically realized recently: the ABC-6 family and the EIZ family. The ABC-6 zeolite family currently consists of over 30 endmembers, including CAN, SOD, CHA, etc. All ABC-6 structures, as well as their featured nanosized constituent cages, are constructed from stacking 6-rings along the (0,0,z), (1/3,2/ 3,z), and (2/3,1/3,z) axes in the hexagonal unit cell (denoted A, B, and C, respectively; Figure 2). The ternary stacking sequences of 6-rings determine the framework structures as well as the physical and chemical properties of ABC-6 zeolites. Therefore, by enumerating every possible ternary stacking sequence, all chemically feasible ABC-6 zeolites can be enumerated. In 2015, Li et al. realized this idea and enumerated 84,292 distinct ABC-6 zeolite structures constructed from ≤16 layers of 6-rings.55 Subsequent structure evaluation using the local interatomic distance criteria47,49 validated the chemical feasibility of all of the enumerated structures. Meanwhile, through a custom high-throughput decoding program, Li et al. extracted the key structural information on these ABC-6 structures directly from their ternary stacking sequences, including their constituent cages (Figure 2). Noting that all existing ABC-6 zeolites were composed of no more than four types of constituent cages, the authors narrowed down the list of the most realizable synthetic targets to 1127 ABC-6 structures and successfully realized two of them.55 An important advantage of this approach is its capability to decompose an ABC-6 framework topology into constituent cages. In comparison with other zeolite structures, ABC-6 structures are relatively easy to create by targeted synthesis because the host−guest interactions between their nanosized constituent cages and SDAs are relatively specific and predictable. The most promising SDAs for each type of cages can be identified via computer modeling. Using multiple SDAs that can direct

represents an important step forward toward function-led discovery of zeolitic materials by identifying highly active and selective zeolites according to target reactions. Designing Target Zeolites via Structure Enumeration. From primitive model building to today’s sophisticated highthroughput computational techniques, predicting not-yetdiscovered hypothetical zeolite structures has attracted attention since the last century.11 Zeolite structure prediction generates a large number of theoretical zeolite structures, from which synthetic targets can be selected according to functional need.40−42 Traditional zeolite prediction involves two major steps. The first step is to generate hypothetical zeolite structure models based on thermodynamic, topologic, or geometric regulations;43−46 the second step is to evaluate the predicted structures and screen out unfeasible ones that would be difficult to realize experimentally.47−51 Such two-step procedures lack efficiency. The model-generating step is computationally expensive, and the majority of the generated models are synthetically inaccessible and need to be screened out in the structure evaluation step. Although the number of feasible structures predicted in this way is immense, few of them have been realized so far.52,53 This conundrum might be due to the limitations in our understanding of the structure-directing mechanism during the formation of zeolites.

Zeolite structure prediction generates a large number of theoretical zeolite structures, from which synthetic targets can be selected according to functional need. An alternative method for designing realizable zeolite structures is to enumerate new combinations of the structural building units found in existing zeolites, such as their nanosized constituent cages.54 Because each combination of the constituent cages corresponds to a specific zeolite structure, enumerating all possible cage combinations will generate a large number of hypothetical zeolite structures at a relatively low computational C


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Figure 3. RHO EIZ family. The lattice length and the name of each member are shown. Note that RHO-G2 has not yet been discovered experimentally. Reprinted with permission from ref 59. Copyright 2016 John Wiley and Sons.

Reorganizing Disassembled Building Layers. In comparison with silicate and aluminosilicate zeolites, germanosilicate zeolites are much less stable because germanium atoms tend to occupy the T sites in d4r cages, which can easily be hydrolyzed even in atmospheric moisture. The chemical weakness of germanosilicate zeolites was viewed as a negative feature for a long time, until recent reports showed that such weakness could be utilized for the chemically selective top-down synthesis of new nanoporous materials.60 In an early study, Verheyen et al. reported the synthesis of an unprecedented all-silica zeolite from the transformation of a UTL-type germanosilicate zeolite.61 The germanium atoms in the UTL framework were selectively removed under acidic conditions and the leached UTL framework was calcined to transform into a new all-silica zeolite COK-14 (OKO type). Recently, Morris, Č ejka, and co-workers expanded this idea and proposed the assembly−disassembly−organization−reassembly (ADOR) synthetic approach.60,62 This approach starts from the synthesis of a parent zeolite (“assembly”), which contains a hydrolytically sensitive exotic element incorporated in a silica-rich framework at a specific site (such as germanium at d4r cages). Then, the exotic element is selectively removed under acidic conditions because of its chemical weakness, and the parent zeolite framework is decomposed into a layered precursor (“disassembly”). If the layers in the precursor are shifted relative to one another, a new layered precursor will form (“organization”). After calcination, the new layered precursor may transform into a new zeolite framework by linking the shifted layers (“reassembly”). The key to the success of the ADOR approach is breaking the strong hydrogen bonding between layers and shifting the layers to form a new precursor different from the parent. This shift in layers can be accomplished either by the stepwise usage of large surfactant molecules to separate the layers and suitable SDAs to direct the arrangement of layers or by deprotonating the silanol groups covering the surface of the layers under basic conditions and simultaneously intercalating SDAs between the layers.63 Using UTL-type zeolites as the parent, Morris, Č ejka, and co-workers successfully synthesized six unprecedented zeolite structures, namely, IPC-2 (OKO type), -4 (PCR type),64 -6, -7,65 -9, and -1063 (Figure 4). The ADOR approach is not limited to UTLparented zeolites, however. In principle, any zeolite consisting of silica-rich layers linking by a chemically weak building unit could be used as the parent framework. For instance, IPC-12 was synthesized through the ADOR method from a UOV-type parent zeolite;66 IPC-15 and -16 were synthesized from the parent zeolite SAZ-1 and were constructed by the linkage of cf i-type layers.67 The advantage of the ADOR approach is two-fold. First, the size of the nanopores in zeolite frameworks can be tuned

different individual constituent cages as co-templates, the target ABC-6 structure might be synthetically accessible. Several predicted ABC-6 candidate structures have been realized via this “retrosynthetic” approach.35,56 The concept of the embedded isoreticular zeolite (EIZ) family was recently proposed by Hong, Zou, and their co-workers.57 All members of an EIZ family are isoreticular and are built by two groups of constituent cages: the scaffold cages that determine the scaffold structures of the family and the embedded cages that fill the nanopores between the scaffolds. Starting from zeolite RHO, a number of isoreticular structure models could be built in body-centered cubic space group Im3̅m, where the lta, pau, and d8r cages form the scaffold structures, and the t-plg, t-oto, t-gsm, and t-phi cages are the embedded cages (Figure 3). As with the ABC-6 family, the structures of the EIZ family can be enumerated by calculating all possible stacking sequences of the scaffold cages. Starting from zeolite RHO, Hong, Zou, and their co-workers designed several EIZ structures and named each of them “RHO-Gn”, where n was the generation number in this family (Figure 3).58,59 Like the ABC-6 family, the EIZ family is also experimentally accessible via the “retrosynthetic” approach because of their featured cage structures. In the RHO family, RHO-G1 (zeolite Rho) and -G3 (paulingite, PAU type) are natural zeolites. RHO-G4 (ZSM-25, MWF type) was synthesized in the 1980s by using Na+ and tetraethylammonium cations as the SDAs, but its structure was not determined until 2015.58 In comparison with RHO-G1, -G3, and -G4, the higher generations in the RHO family comprise much more embedded cages, such as the t-gsm, t-oto, and t-phi cages. To encourage the formation of these embedded cages, Hong, Zou, and co-workers employed Sr2+ or Ca2+ as inorganic SDAs, which could be found in existing zeolites consisting of these embedded cages, in addition to using Na+ and tetraethylammonium cations for the formation of RHO-G4. By varying the SiO2/Al2O3 and H2O/SiO2 ratios of the reaction gels, the authors successfully synthesized the designed higher generations of the RHO family, including RHO-G5 (PST-20), RHO-G6 (PST-25), RHO-G7 (PST-26), and RHO-G8 (PST-28), each of which represented an unprecedented zeolite framework type (Figure 3).58,59 In particular, RHO-G8 possesses 104 topologically distinct T atoms and a unit cell larger than 600 nm3, which are both record highs among all existing zeolite structures. The ABC-6 and RHO families are two examples showing how target zeolites can be designed a priori by structure enumeration and then realized in synthesis. In theory, this approach is applicable to all zeolite structures that are constructed from systematic stacking of well-defined constituent cages. D


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Figure 4. Six unprecedented zeolite structures realized from the same parent zeolite UTL via the assembly−disassembly−organization− reassembly approach.

continuously.65 In traditional zeolite synthesis, SDAs play a key role in directing the formation of specific nanopores. However, the influence of SDAs is realized via nonbonding host−guest interactions, which are too weak to control the arrangement of framework atoms as precisely as required. In comparison, the ADOR approach selectively hydrolyzes the weak points at specific framework sites, enabling the atomic structures of the disassembled building layers to be determined precisely. The arrangement of the building layers can be controlled at the “organization” step by varying the acidity of the reaction system. In situ magic-angle spinning nuclear magnetic resonance (NMR), and X-ray diffraction (XRD) studies indicate that, under highly acidic conditions, silica rearrangement takes place extensively throughout the building layers and evolves with time, leading to different zeolite structures, whereas low acidity does not promote silica rearrangement.68,69 As a result, each step of the ADOR approach, as well as the resultant zeolite frameworks, can be well tuned. In fact, many hypothetical zeolite structures can be predicted in silico by modeling the assembly of building layers, which provides a large number of synthetic candidates accessible by the ADOR approach.70,71 As shown in Figure 4, by varying the organization and reassembly conditions from the same parent zeolite, the porosity of the resultant zeolite frameworks can be tuned continuously across small (7- and 8-rings), medium (9- and 10-rings), large (12-ring), and extra-large pores (14-ring).72 The second advantage of the ADOR approach is its capability to realize conventionally inaccessible zeolites. Although millions of hypothetical zeolite structures have been predicted, most of them are deemed unfeasible synthetic targets from thermodynamic and topological points of view. However, the structures inaccessible by conventional synthetic methods might be accessible via the indirect multistep ADOR approach. For instance, Mazur et al. synthesized two new high-silica zeolites, IPC-9 and IPC-10, via the ADOR approach.63 These two structures have unusual characteristics, such as odd-membered-ring pore openings. More importantly, both of the structures violate the local interatomic distance and the energy−density correlation criteria that previously discovered zeolite structures all follow,47,48 so IPC-9 and IPC-10 should be “unfeasible” under conventional

synthetic conditions. Nonetheless, the ADOR approach bypasses the direct formation of thermodynamically unfeasible structures, providing a new pathway for the synthesis of unprecedented zeolite frameworks. This approach opens up the possibility that the majority of the hypothetical zeolites, which were once thought to be unfeasible synthetic targets, are potentially accessible. In contrast to conventional zeolitic materials with threedimensionally extended frameworks, the layered building units used in ADOR approaches, together with other zeolitic materials propagating in only two dimensions, can be deemed to be two-dimensional zeolites.73−75 In addition to being the precursors for conventional zeolites, two-dimensional zeolites with pillared, delaminated, and interlamellar expanded structures have found many applications in catalysis because of the improved exposure of active sites and enhanced diffusion of large reactants.76 Moreover, two-dimensional zeolites can be fabricated into nanosheets and membranes, which have exhibited superior capability for catalysis and separation.2,77,78 Two-dimensional zeolites have changed the fundamental understanding of what zeolitic materials are, and readers who are interested in this topic can refer to several recently published review articles.72,76,79−81

This approach opens up the possibility that the majority of the hypothetical zeolites, which were once thought to be unfeasible synthetic targets, are potentially accessible. CONCLUSIONS AND OUTLOOK Although the era for function-led ab initio zeolite synthesis has not yet arrived, recent progress has inspired us to keep working toward this goal: SDAs can be designed to synthesize unprecedented zeolite frameworks according to host−guest specificity and functional need; structure enumeration provides a large number of feasible synthetic candidates with intriguing structures and properties; and unconventional synthetic methods, such as the ADOR approach, offer new possibilities to realize synthetic targets, even those deemed unfeasible before. Despite E


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calculations, we will get a comprehensive picture of zeolite structures and functions. Another missing link in the chain of function-led discovery of zeolitic materials is targeted synthesis of desired structures. Efficient synthesis of target structures without the usage of expensive or environmentally unfriendly reagents and the generation of unwanted phases is still challenging. Due to the limitations of current characterization techniques, we still do not understand the exact mechanism of zeolite formation from the reaction gels under hydrothermal conditions.91 Again, machine learning might be a promising tool to establish a quantitative model linking zeolite structures and their synthetic attributes. Learning from known experimental data, this method can be used to predict the synthetic conditions for target zeolites directly from their structural attributes, bypassing the mysterious synthetic chemistry that we still do not understand. Early studies have demonstrated the promise of machine learning in the prediction of the resultant zeolites by using typical synthetic attributes as the input, such as the initial gel contents and the types of SDAs.92,93 Meanwhile, our group has built a database of the synthetic data of zeolites, termed ZEOBANK.94 We anticipate that as more synthetic data are gathered and zeolite structure description methods develop further,11,86 machine learning will play increasingly important roles in the synthesis of target zeolitic materials. When the function−structure−synthesis relationships are understood, function-led discovery of zeolitic materials by a priori design will ultimately be achieved.

all the success, it is notable that none of the recently discovered zeolite framework types have yet been commercialized. In fact, fewer than 20 zeolite framework types have been developed commercially during the past century. Bottlenecks in the commercialization process include the function and performance of new zeolites as well as the technology and cost of large-scale production, processing, and application. Much effort is still needed to accelerate the function-led innovation of zeolitic materials. First, a deep understanding of the relationship between zeolite structures and functions is highly desirable. Beginning with the functional needs, such as needing to adsorb a specific guest molecule or to catalyze a specific reaction, researchers should first determine what kind of zeolite structures would have such functions. Atomic-scale structure details that affect the functions of zeolites (e.g., the active sites) are difficult to elucidate via conventional characterization techniques. Computational chemistry provides an alternative way to probe the atomic-level details of zeolite structures, especially to reveal structure−function relationships during chemical processes.82,83 For instance, Gao et al. identified the nanostructures of extraframework molybdenum oxides during the direct conversion of methane into aromatic hydrocarbons over zeolite MFI via DFT calculations;84 Bereciartua et al. investigated the distribution of the window size in zeolite ITQ-55 via ab initio molecular dynamics simulations and discovered the critical role of framework flexibility in the separation of ethane and ethylene;1 Sushkevich et al. investigated the anaerobic oxidation of methane into methanol over Cu-MOR using DFT calculations and discovered the valence change in extra-framework Cu, the active site of this reaction.85 Another promising way to establish the correlation between zeolite structures and functions is machine learning, which is an informatics approach based on a large amount of experimental or computational data. Bypassing the unclear chemistry behind zeolite structures, machine learning can be used to predict the functions of zeolites from their structural attributes or vice versa.86,87 We anticipate that with the development of computational chemistry, machine learning, and the rapid growth of computer power, theoretical calculations will provide increasingly important clues for revealing the complex relationships between zeolite structures and their functions. In addition to theoretical calculations, advanced characterization techniques are also critical for the investigation of zeolite structures and functions. The nanoscale and macroscale structures of zeolites, such as the crystallinity, size, and shape of zeolite crystals, the distribution of intracrystalline nanopores and macropores, and the encapsulation of exotic nanoparticles, also significantly affect the adsorption, diffusion, and catalytic processes in zeolites. The structures of zeolites may also change during these processes. Thus, we need state-of-the-art characterization techniques not only to reveal the structures of zeolites at different length scales but also to monitor the variation of zeolite structures with time. For instance, time-resolved atomic force microscopy can be used to reveal the mechanism of zeolite crystallization;88 interference and infrared microscopies provide a wealth of information on mass transfer in nanoporous materials during catalysis;89 in situ solid-state NMR and XRD can be used to monitor the local and global structural changes in zeolites during their synthesis and application;68 and atom probe tomography can be used to determine the nanoscale three-dimensional distribution of specific elements within zeolites.90 With all these advanced characterization techniques, coupled with theoretical

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

Yi Li: 0000-0002-5222-3674 Jihong Yu: 0000-0003-1615-5034 Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Nos. 21622102, 21621001, and 21320102001), the National Key Research and Development Program of China (No. 2016YFB0701100), the National 111 Project (B17020), and Program for JLUSTIRT. REFERENCES (1) Bereciartua, P. J.; Cantín, Á .; Corma, A.; Jordá, J. L.; Palomino, M.; Rey, F.; Valencia, S.; Corcoran, E. W.; Kortunov, P.; Ravikovitch, P. I.; Burton, A.; Yoon, C.; Wang, Y.; Paur, C.; Guzman, J.; Bishop, A. R.; Casty, G. L. Control of Zeolite Framework Flexibility and Pore Topology for Separation of Ethane and Ethylene. Science 2017, 358, 1068−1071. (2) Jeon, M. Y.; Kim, D.; Kumar, P.; Lee, P. S.; Rangnekar, N.; Bai, P.; Shete, M.; Elyassi, B.; Lee, H. S.; Narasimharao, K.; Basahel, S. N.; Al-Thabaiti, S.; Xu, W.; Cho, H. J.; Fetisov, E. O.; Thyagarajan, R.; DeJaco, R. F.; Fan, W.; Mkhoyan, K. A.; Siepmann, J. I.; et al. UltraSelective High-Flux Membranes from Directly Synthesized Zeolite Nanosheets. Nature 2017, 543, 690−694. (3) Jiao, F.; Li, J.; Pan, X.; Xiao, J.; Li, H.; Ma, H.; Wei, M.; Pan, Y.; Zhou, Z.; Li, M.; Miao, S.; Li, J.; Zhu, Y.; Xiao, D.; He, T.; Yang, J.; Qi, F.; Fu, Q.; Bao, X. Selective Conversion of Syngas to Light Olefins. Science 2016, 351, 1065−1068. (4) Snyder, B. E. R.; Vanelderen, P.; Bols, M. L.; Hallaert, S. D.; Böttger, L. H.; Ungur, L.; Pierloot, K.; Schoonheydt, R. A.; Sels, B. F.; Solomon, E. I. The Active Site of Low-Temperature Methane F


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