Aromatic compounds‐mediated synthesis of anatase‐free hierarchical TS‐1 zeolite: Exploring design strategies via machine learning and enhanced catalytic performance

Simultaneous achievement of constructing mesopores and eliminating anatase is a long‐term pursuit for enhancing the catalytic performance of TS‐1. Here, we developed an aromatic compounds‐mediated synthesis method to prepare anatase‐free and hierarchical TS‐1 for olefin epoxidation. A series of hierarchical TS‐1 zeolites were prepared by introducing aromatic compounds containing different functional groups via the crystallization process. The formation of intercrystalline mesopores and insertion of titanium into framework were facilitated at different extent. The synergistic coordination of carboxyl and hydroxyl in aromatic compounds with Ti(OH)4 realizes the uniform distribution of titanium species and eliminates the generation of anatase. Noteworthily, eight machine learning models were trained to reveal the mechanism of additive functional groups and preparation conditions on anatase formation and microstructure optimization. The prediction accuracy of most models can reach more than 80%. Benefiting from the larger mesopore volumes (0.37 cm3·g−1) and higher content of framework Ti species, TS‐DHBDC‐48h samples exhibit a higher catalytic performance than other zeolites, giving 1‐hexene conversion of 49.3% and 1,2‐epoxyhenane selectivity of 99.9%. The paper provides a facile aromatic compounds‐mediated synthesis strategy and promotes the application of machine learning toward the design and optimization of new zeolites.


INTRODUCTION
Epoxides are among the most useful synthetic intermediates for producing high added-value chemicals in the chemical industry, [1][2][3] so great efforts have been made to improve the catalytic efficiency for the epoxidation of olefins in the last century. As an important branch of MFI topological zeolites, titanium silicalite-1 (TS-1) has been widely applied in catalytic oxidation of various olefins since its discovery owing to the active titanium species, [4,5] unique micropore structure, [6,7] and high hydrothermal stability. [8][9][10][11][12][13] Olefin oxidation catalyzed by TS-1 using H 2 O 2 as oxidant can achieve high efficiency under mild conditions, and water is the only by-product, which attract increasing attention for its environment friendly and pollution-free process. [14][15][16][17][18] The coordinated state of the titanium species in TS-1 is significant for the catalytic performance, [19] which exist in various coordination forms. The highly dispersed tetrahedrally coordinated Ti species (TiO 4 ) in the framework, [20] and sometimes high coordinated Ti species (TiO 5 and TiO 6 species) are considered as the catalytic active sites. [4] But anatase TiO 2 in the form of octahedral coordination is harmful because it could decompose H 2 O 2 and then reduce the effective utilization of oxidants. [21][22][23] However, Ti species are likely to aggregate and generate anatase owing to the mismatched hydrolysis rate of Ti source and Si source. [24] Furthermore, the crossed micropore channel of TS-1 less than 2 nm limits the accessibility of bulky molecules to active sites, [2,10,25] resulting in the low conversion of substrates. Enhancing the content of framework Ti, eliminating the formation of anatase and constructing hierarchical porous structure have always been hot research issue for TS-1 zeolites.
Plenty of approaches have been made to prepare anatase-free and hierarchical TS-1 zeolites. Hard templates, such as chitosan and precursor nanoparticles, were used to synthesize hierarchical TS-1 by hydrothermal or steam-assisted crystallization method. [22,26,27] However, the interaction between most hard templates and Ti species is weak, so it is difficult to regulate the coordination state of Ti species. [28] Macromolecular compounds, such as polyacrylamide (PAM), 2-(2-[4-(1,1,3,3-Tetramethylbutyl)phenoxy]ethoxy)ethanol (Triton X-100), polyvinyl butyral (PVB), polydiallyldimethylammonium chloride (PDADMAC) and polyvinyl alcohol (PVA), were used as soft templates to synthesize mesoporous or anatasefree TS-1 with different morphologies. [2,[29][30][31][32] Recently, introducing crystallization modifiers has been proven to be a feasible strategy to modulate the crystallization process of TS-1. L-Lysine acts as a crystallization inhibitor to promote the oriented aggregation of nanoparticles in a noncompact manner, and the interconnected mesopores are obtained by a two-step crystallization strategy. [33] Amino acid L-carnitine can interact with Ti precursors to realize the uniform distribution of framework Ti species and control the morphology of TS-1 zeolites. [28] However, constructing abundant mesopores and eliminating the formation of anatase simultaneously during the crystallization process of TS-1 are still very challenging. So, it is highly desirable to develop a facile and effective method for fabricating hierarchical and anatase-free TS-1. In recent years, machine learning methods have shown broad prospects in materials structural design and performance prediction, [34][35][36] proving a new way to accelerate the structural design optimization and performance prediction of zeolites and other materials. [37][38][39][40] In this work, a novel one-step aromatic compoundsmediated synthesis method was developed for fabricating anatase-free hierarchical TS-1 zeolites. Aromatic compounds with good structure stability containing various functional groups were chosen as multifunctional mediators to guide the formation of intercrystalline mesopores, regulate the distribution of Ti species and eliminate the generation of anatase during the crystallization process. Machine-learning strategy was introduced to investigate and discuss the key influencing factors of the anatase-free hierarchical TS-1, and 8 machine models were used to study the effects of 12 features and show accuracy of more than 80% in predicting the formation of anatase. The catalytic performance of obtained TS-DHBDC-48h samples is improved by 29.4% in the epoxidation of 1-hexene and 53.3% of cyclohexene than conventional TS-1 samples. This paper provides a feasible strategy to regulate the coordination state of Ti and induce the formation of mesopores in TS-1 simultaneously and promotes the development of machine-learning-assisted fabricating new zeolites.

Synthesis of TS-1
Nano-sized TS-1 was synthesized by a hydrothermal method with the molar composition of SiO 2 :  The samples prepared using BDC/DABDC/DHBDC crystallized for 48h were  named as TS-BDC-48h, TS-DABDC-48h and TS-DHBDC-48h, respectively. The sample without aromatic compounds  was named as TS-WAC-48h for reference. Furthermore, the DHBDC-mediated TS-1 zeolites under the crystallization time of 0.5 h / 1 h / 1.5 h / 2 h / 4 h / 24 h were also synthesized and named as TS-DHBDC-xh, which xh represents the crystallization time.

Characterization
The crystallinity and phase purity were measured by powder X-ray diffraction (XRD) via a Bruker D8 ADVANCE X-ray diffractometer with Cu Kα1 radiation (λ = 1.5406 Å) operated at 40 kV and 40 mA. The relative crystallinity (RC) is the ratio of the total characteristic peak (7.9 • , 8.9 • , 23.1 • , 23.9 • and 24.4 • ) areas of the aromatic compoundsmediated zeolites to the reference sample. SHIMADZU UV-2550 absorption spectrometer was used to record the ultraviolet-visible diffuse reflectance spectra (UV-Vis) in the range of 200-450 nm, and nanoscale BaSO 4 plate was used as a reflectance reference. UV-Raman spectra with an excitation wavelength of 325 nm were explored by a UV-Raman spectrograph using Horiba Scientific LabRAM HR Evolution. Fourier transform infrared (FT-IR) spectra were conducted on a Nicolet 6700 spectrometer in the range from 4000 cm −1 to 400 cm −1 . X-ray photoelectron spectroscopy (XPS) was examined to test the state of Ti elements in composites with Thermo escalab 250XI (America). Micromorphology and grain size were collected by scanning electron microscopy (SEM) using the SU8000 electron microscope. Transmission electron microscopy (TEM) was obtained by Hitachi 7700 to detect the microstructure of all samples. N 2 adsorption-desorption isotherms were performed by Micromeritics ASAP 2420 instrument at 77 K. The specific surface area and pore size distribution were analyzed by density functional theory method. Inductively coupled plasma optical emission spectrometer (ICP-OES) was employed to calculate the contents of Ti and Si elements in samples on an Agilent ICP-OES 730 (America) instrument. The MAS NMR of 13 C and 29 Si was tested with a 500 MHz Avance III 600WB spectrometer (Bruker, Germany) to characterize the chemical environment of C and Si atoms. The chemical drifts of 13 C and 29 Si were determined using tetramethylsilane as a reference with a magic angle spinning rate of 5 kHz. Thermogravimetric (TGA) curves were obtained using Thermo Gravimetric Analyzer (449F3, Germany), and the temperature ranges from room temperature to 873 K with a heating rate of 10 K⋅min −1 .

Machine learning models
Random Forest (RF), Support Vector Machine (SVC), Decision Tree (DT), Stochastic Gradient Descent (SGD), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbors (KNN) and Natural Bayes (NB) were trained to predict the existence of anatase using 12 features, including the type and number of functional groups in additives and preparation parameters. The additives include those reported in the literature and used during our experiment (see attachment). "1" represents that anatase exists in the prepared TS-1, and "0" is the opposite. We firstly processed the data with Synthetic Minority Over-sampling Technique (SMOTE) algorithm to eliminate the scale imbalance caused by the inconsistent number of 0 and 1. Then the collected data were divided into two sets, 70% of which were training sets and 30% were test sets. We implemented the 10-fold Cross-Validation method to verify whether the above models have an overfitting phenomenon and reported the prediction accuracy in the evaluation metrics. All the algorithms were obtained from the Scikit-Learn package and programmed using Python.

Catalytic tests
The epoxidation of 1-hexene with H 2 O 2 was completed in a 25 mL three-necked round-bottom-flask with a reflux condenser. Ten millimeters methanol, 10 mmol 1-hexene, 10 mmol H 2 O 2 (30 wt%) and 50 mg catalyst were added to the flask in order. Then the reaction started at 333 K with vigorously magnetic stirring for 2 h. Afterwards 0.5 mL reaction solution was mixed with 2 mL methanol, and the mixture was centrifuged under 10,000 r/min for 3 min. The supernate was analyzed by a gas chromatograph-mass spectrometer (Agilent 7890A). The conversion of 1-hexene and the selectivity of products were calculated via the area normalization method. The recycled TS-1 samples were washed with methanol for three times and dried at 353 K under vacuum atmosphere overnight. Then the recycled zeolites were calcinated at 823 K in air for 3 h.

Characterization of TS-1 zeolites
The preparation process of aromatic compounds-mediated synthesis method is shown in Scheme 1. Firstly, the mixture of TPAOH/H 2 O and TEOS/TBOT was hydrolyzed under stirring at room temperature, and the produced alcohols were removed at 353 K. Then aromatic compounds and hydrolysate were added into a Teflon-lined stainless autoclave, and the crystallization process was carried out. Finally, the products were obtained after washing, drying and calcination. These samples were named by the kind of aromatic compounds and crystallization time. The DHBDC-mediated zeolites with different Ti/Si molar ratios (0.045, 0.050 and 0.055) were also synthesized following the same procedure. TS-DHBDC-48h (TTIP) and TS-DHBDC-48h (TEOT) were prepared following the synthesis process as TS-DHBDC-48h, while TBOT was replaced by TTIP and TEOT. The XRD patterns of the prepared TS-1 samples are shown in Figure 1A,B. The characteristic peaks shown at 7.9 • , 8.9 • , 23.1 • , 23.9 • and 24.4 • match the standard diffraction peaks (JCPDS No. 43-0055) and prove that high-purity TS-1 zeolites were successfully synthesized. [10,20] Because the ion radius of Ti 4+ is larger than that of Si 4+ , the increased cell volume is a strong evidence for the insertion of Ti atoms into the frameworks and the formation of tetrahedrally coordinated Ti species. [6,7] Lattice constant and cell volume are shown in Table S1. The cell volume of aromatic compounds-mediated zeolites is larger than reference samples, indicating that aromatic compounds are helpful for the coordination of Ti species with Si species to form TiO 4 species. The relative crystallinity of aromatic compounds-mediated zeolites is higher than that of TS-WAC-48h, which proves that aromatic compounds contribute to enhancing the crystallinity of TS-1. The XRD patterns of the TS-DHBDC samples prepared at different crystallization time are shown in Figure 1B. DHDBC-mediated zeolites show a growing trend in cell volume and RC with the increasing crystallization time. [41,42] The characteristic peaks of TS-DHBDC-1h at 23.9 • and 24.4 • have not been formed, and the crystallinity is much lower. The characteristic peaks of MFI topology have been formed, and the crystallinity is significantly improved when the crystallization time is extended to 2 h. All these results suggest that introducing aromatic compounds can effectively accelerate the crystallization process and enhance the crystallinity of TS-1 zeolites.
The Ti coordination states of the as-synthesized TS-1 zeolites were investigated by UV-Vis spectra in Figure 1C. All the samples show a strong absorption band at λ = 220 nm, which is assigned to charge transfer between tetrahedrally coordinated Ti and O in the framework. [43,44] The phenomenon suggests that Ti species have been successfully inserted into the framework. Compared with TS-DHBDC-48h, the curves of TS-BDC-48h, TS-DABDC-48h and TS-WAC-48h exhibit an absorption band at λ = 300-330 nm, proving the existence of anatase TiO 2 . [41,45] 2,5-dihydroxyterephthalic acid has extra dihydroxyl groups compared to terephthalic acid, but anatase TiO 2 can be notably found in TS-BDC-48h. To obtain more detailed information about the crystallization process, the UV-Vis spectra of DHBDC-mediated TS-1 zeolites with different crystallization time from 1 to 24 h were analyzed in Figure S1A. During the entire crystallization, no peaks attributed to anatase TiO 2 can be found. To further study the inhibition effect of DHBDC on the formation of anatase TiO 2 , TS-1 samples with different Ti/Si molar ratio (0.045, 0.050 and 0.055) were synthesized using the same synthesis method as TS-DHBDC-48h. It can be seen in Figure S1B that even if Ti/Si molar ratio is significantly increased, no anatase can be detected. Therefore, it can be concluded that DHBDC plays a vital role in avoiding the formation of anatase. Furthermore, the hydrolysis rate of TEOT and TTIP is faster than TBOT, easily leading to the aggregation of Ti species and the formation of anatase TiO 2 .
In order to qualitatively analyze the coordination state of Ti species, UV-Raman spectra of synthesized zeolites were detected and shown in Figure 1D. TS-WAC-48h, TS-BDC-48h and TS-DABDC-48h show similar peaks at 390, 516 and 637 cm −1 , indicating the existence of anatase TiO 2 in these samples. [47,48] Conforming to the UV-Vis results, TS-DHBDC-48h exhibits no characteristic peaks of anatase TiO 2 . The peak at 380 cm −1 is the characteristic of silicate-1 zeolites, and the peaks at 960 and 1125 cm −1 are ascribed to the Ti-O-Si groups in the framework [47] The intensity of the characteristic peak at 960 cm −1 in TS-WAC-48h was significantly weaker than that of aromatic compounds-mediated zeolites. Therefore, it can be qualitatively determined that more extra-framework Ti species may exist in TS-WAC-48h. And, the aromatic compounds-mediated synthesis method is considered to be an effective strategy to enhance the Ti content in the framework and prevent the generation of anatase TiO 2 .
The FT-IR spectroscopy was collected to discuss the coordination environment of Ti species in prepared zeolites. The absorption bands at 1230, 1100, 960, 550 and 455 cm −1 belong to the structure of MFI topology, [49] indicating the successful synthesis of TS-1 samples, which is associated with the XRD analysis. The peak at 960 cm −1 is ascribed to the vibration of the Ti-O-Si group in the framework, proving Ti species incorporate into the framework successfully. [7] The intensity ratio of the absorption band at λ = 960-800 cm −1 (I 960 /I 800 ) was calculated to evaluate Ti content in the framework of different samples. [24] The I 960 /I 800 of aromatic compounds-mediated zeolites is 2.20, 2.14 and 2.15, which is higher than that of TS-WAC-48h. This result demonstrates that aromatic compounds are beneficial to facilitating Ti species to coordinate with Si species and increasing the Ti content in the framework. Furthermore, the characteristic peak at 960 cm −1 formed at 1.5 h of crystallization, and the I 960 /I 800 of TS-DHBDC-4h has exceeded that of TS-WAC-48h in Figure S2A.
In order to determine the chemical interaction between DHBDC and Ti species, the FT-IR spectra of the supernatant at the early crystallization stage of DHBDC-mediated zeolites is shown in Figure S2B. The Ti-O-Si at 960 cm −1 and Ti-O-C bonds at 1125 cm −1 formed gradually as the increased crystallization time. [50] The Ti-O-C bonds prove that Ti(OH) 4 species have a suitable chemical interaction with the C-OH bonds in DHBDC and provides evidence for coordination interactions between DHBDC and Ti species. Yu et al. believed that the amino/carboxyl groups of amino acids strongly interact with specific crystal surface of zeolites to regulate the Ti coordination states. [42] Wang et al. thought that the Ti nodes coordinated with MOFs ligands (2-aminoterephthalic acid) to avoid the generation of anatase when using Ti MOFs as Ti-containing precursors. [51] Similarly, the chemical interaction between DHBDC and Ti species can be speculated as coordination interaction of functional groups.
XPS analysis in Figure 2B shows the chemical composition and electronic structure of Ti species. All TS-1 samples exhibit signals at the binding energy of 460.1 and 465.4 eV, while the peak at the binding energy of 458.4 eV exists in TS-WAC-48h, TS-BDC-48h and TS-DABDC-48h. The signals of electronic binding energies at 460.1 and 458.4 eV are assigned to Ti 2p 3/2 , corresponding to tetrahedrally coordinated Ti(IV) species in framework and octahedrally coordinated Ti(VI) in extra-framework, respectively. [41] Through the element analysis of XPS, the Ti species in TS-DHBDC-48h is in the form of tetrahedrally coordinated Ti 4+ without extra-framework Ti species. These phenomena indicate that DHBDC promotes Ti species to insert into the framework and restricts the formation of anatase, which is in accord with the UV-Vis and UV-Raman results.
The SEM and TEM images of conventional sample and hierarchical samples are shown in Figure 3. All samples exhibit an irregular and ellipsoid-like morphology with a smooth surface. [42] The grain size of these samples ranges from 130 to 150 nm. Comparing Figure S3A and S4A, it can be seen that TS-DHBDC-1h has a larger grain size than TS-WAC-1h. The pH of hydrolysate before and after adding aromatic compounds is shown in Table S2. The hydrolysate adding DHBDC has a relatively low pH, which significantly reduces the nucleation rate and increases the growth rate. Therefore, the particle size of TS-DHBDC-1h is larger at the initial crystallization stage. [33] Mesopores can be found in the DHBDC-mediated zeolites. According to Figure  S4F and G, the TEM images of TS-DHBDC-1h and TS-DHBDC-1.5h prove that the intergranular mesopores were formed during the aggregation of nanozeolite particles. [33]   Compared with the TEM images of Figure 3E-H, TS-1 zeolites synthesized via aromatic compounds-mediated method exhibit noticeable intercrystalline mesopores. And, no obvious intergranular mesopores can be found in TS-WAC-1h and TS-WAC-48h. It can be inferred that aromatic compounds have significant effects on guiding the formation of intergranular mesopores. ICP-OES was analyzed to determine the mass fraction of total Ti species and Si/Ti molar ratio. Therefore, the mass fraction of Ti given in Table 1 includes both the mass of tetrahedrally coordinated Ti species and highly coordinated Ti species. Compared with other zeolites, TS-DHBDC-48h possesses much lower Ti content (1.53 wt%) and higher Si/Ti molar ratio (48.6). Based on the analysis of UV-Vis, UV-Raman and XPS analysis, it is clear that the Titanium element in TS-DHBDC-48h exists in the form of tetrahedrally coordination. So, 1.53 wt% of the Ti species can be determined as the mass fraction of framework Ti species. For TS-WAC-48h, TS-BDC-48h and TS-DABDC-48h prepared without using DHBDC as mediator, the existence of anatase may be responsible for the high mass fraction of Ti elements.
The N 2 adsorption-desorption isotherms and pore width distribution curves of the prepared samples are shown in Figure 4. All samples except TS-WAC-48h exhibit the typical type-IV N 2 adsorption-desorption isotherms in Figure 4A. The N 2 absorbed amounts of TS-1 samples synthesized via aromatic compounds-mediated method increase rapidly at P/P 0 = 0.9-1.0, indicating mesopores were successfully introduced. The emergence of the H3 hysteresis loop may be ascribed to the slit mesopores during the aggregation and accumulation process of zeolite nanoparticles. [10] DHBDC-mediated zeolites prepared at different crystallization time display the same isotherms and hysteresis loop in Figure S5A, which proves the existence of intercrystalline mesopores during the entire crystallization process. On the contrary, the N 2 adsorption-desorption isotherm of TS-WAC-48h is type-I and shows the feature of microporous materials. [52] As shown in Figure 4B, the micropores distributed at less than 1 nm in all samples are the characteristic of MFI topological channels. [52] In Figure S5B, the main mesoporous size gradually increases as prolonging the crystallization time. The mesopore width of TS-DHBDC-1h ranges from 20 to 30 nm, while the pore size increases to 30-40 nm for TS-DHBDC-4h. Finally, the mesopore width of TS-DHBDC-48h is centrally distributed at 50 nm, which is larger than that of other samples. It can be speculated that small intergranular mesopores are interconnected and become larger ones during the aromatic compounds-mediated crystallization process. [33] The textural structure of synthesized TS-1 zeolites is illustrated in Table 1  Solid state nuclear magnetic resonance was carried out to identify the effect of 2,5-dihydroxyterephthalic and the chemical environment of Si/Ti species. The TS-DHBDC-48h MAS NMR spectra of 13 C before calcination and 29 Si after calcination are shown in Figure 5. The TS-DHBDC-48h before calcination displays strong characteristic signals at δ = 9.7, 15.4 and 62.8 ppm in Figure 5A, which are assigned to the three carbon species of template TPA + trapped in the zeolite channels. [53] In addition, the signals of carboxyl group and the C atoms in benzene of 2,5-dihydroxyterephthalic acid are not detected, indicating that aromatic compounds are detached from TS-1 during centrifugation following the supernate. This phenomenon proves that the inhibitory effect of DHBDC on the formation of anatase TiO 2 during crystallization and DHBDC is not incorporated into the framework of TS-1. In the 29 Si MAS NMR spectra, the resonance signal at δ = −113.0 ppm belongs to Si(OSi) 4 (Q 4 ) species, and the acromion appears at −115.9 ppm. [54,55] The two resonance peaks of −113.0 and −115.9 ppm appears simultaneously, indicating that the crystal system of zeolite changes from monoclinic to orthorhombic, which is the compelling evidence of Ti atoms entering the lattice of zeolite. [42] The resonance signal at −102.4 ppm is ascribed to Si(OSi) 3 OH (Q 3 ) species, and its peak intensity can directly reflect the quantity of defect sites in zeolites. [56] These results prove that DHBDC-mediated TS-DHBDC-48h possesses excellent crystallinity and few defects.

The formation and evolution mechanism of mesopore in TS-DHBDC-48h
In order to better investigate the effects of the aromatic compounds on the formation of intergranular mesopores, TGA curves of TS-DHBDC-48h and TS-WAC-48h before calcination were obtained ( Figure S6). The weight loss of TS-DHBDC-48h and TS-WAC-48h is 12.17% and 12.57%. The little difference in weight loss between TS-DHBDC-48h and TS-WAC-48h indicates that the aromatic compound is almost removed after the following centrifugation. So, the aromatic compound just guided the formation of intercrystalline mesoporous during crystallization and was not incorporated into the zeolite frameworks. It is certain that charge repulsion exists between the negatively charged aromatic carboxylates and silica nanoparticles in the basic environment during the crystallization, but there is also a certain binding force between them. Yu et al. demonstrated the complexation between L-lysine and silicon species leads to the aggregation of protozeolite nanoparticles in a noncompact manner. [33] It can be determined that aromatic compounds play an active role in the aggregation and maturation process of zeolite nanoparticles. Hydroxyl groups are strong polar groups, so the association phenomenon is very significant. As shown in Figure S2B, the association peaks of intermolecular hydrogen bonds formed between hydroxyl groups appears in the FT-IR figure at 3550-3200 cm −1 . [57] The intermolecular hydrogen bond may include not only the chemical interaction between silicon hydroxyls and titanium hydroxyls, but also nano silicon particles and aromatic compounds. Hydrogen bonded complex is an important embodiment of complexation effect. Therefore, the interaction that make carboxyl in aromatic compounds attach on the surface of silicon nanoparticles can be speculated as hydrogen bonding.
The crystallization pathway of aromatic compoundsmediated TS-1 is a kind of nonclassical crystallization manner. [58] Firstly, the aggregation of nanoparticles is dominant within the first 0.5 h of crystallization. Zeolite nanoparticles begin to be oriented attachment under the guidance of aromatic compounds, as shown in the selected area of Figure S7A. The size of formed TS-DHBDC-0.5h nanoparticles is about 10 nm in Figure S7B. The stacking process of protozeolite particles follows an oriented-attachment manner. [33] Secondly, intraparticle ripening occurs preferentially over nanoparticle orientation after 1-h crystallization. The size of TS-DHBDC samples prepared from 1 to 48 h almost remains the same ( Figure S4 and Figure 3D). Intraparticle ripening process involves larger zeolite particles swallowing up smaller ones to minimize the Gibbs free energy of the whole system. [59,60] Intergranular mesopores inside the particles are derived from the ripening process and then grow to larger ones. This ripening phenomenon can be observed in Figure S4F-J. Finally, the continuous phagocytosis between zeolite nanoparticles eventually leads to a relatively smooth surface and interconnected intercrystalline mesopores. Scheme 2 illustrates the grain growth process under the mediation of aromatic compounds and the intergranular mesopores formation mechanism of hier-archical zeolites. The TEM images of TS-DHBDC-0.5h to TS-DHBDC-48h in Scheme 2 also proved the evolution process of intercrystalline mesopores, which is in accordance with the evolution of above-mentioned mesopores width from BET analysis. In addition, the mechanism is applicable to the preparation of mesoporous TS-1 mediated by other aromatic compounds containing carboxyl groups.

The elimination mechanism of anatase
In order to identify whether the hydroxyl and carboxyl group in 2,5-dihydroxy terephthalic acid acts alone or synergistically to regulate the crystallization process, the UV-Vis spectrum of hydroquinone-mediated zeolite (TS-HQ-48h) was also tested. The spectrum shows obvious absorption peak at 300-330 nm ( Figure S8), indicating the synergistic effect of hydroxyl group and carboxyl group to inhibit the formation of anatase. [22] Since titanium hydroxyl and silicon hydroxyl partially formed polymers during hydrolysis, the hydroxyl group and carboxyl group coordinate with titanium hydroxyl to achieve uniform distribution of Ti species during depolymerization process. The detailed depolymerization, coordination and reorganization processes are shown in Scheme 3. However, due to the lack of coordination of hydroxyl groups of terephthalic acid, uncoordinated Ti-OH may combine with other Ti species, resulting in the aggregation of Ti species and the generation of extra-framework anatase. In addition, the binding effect of amino group in DABDC and Ti-OH is weaker than that of hydroxyl group, [61] which also leads to the uneven distribution of Ti species and the formation of anatase. The synergistic coordination of hydroxyl group and carboxyl group plays a key role in eliminating anatase. Thus, adding appropriate additives in the crystallization process of zeolites is of great significance to improve the coordination of Ti species.

MACHINE LEARNING-ASSISTED INVESTIGATION OF AROMATIC COMPOUNDS-MEDIATED SYNTHESIS PROCESS
In recent years, machine learning has attracted great attention in materials screening and performance prediction owing

S C H E M E 3
The elimination mechanism of anatase in TS-DHBDC-48h during the crystallization process. The hydrogen atoms have been omitted for viewing purposes to the advantages of recognizing the hidden laws in material data. [35] In the literature related to zeolites, the principle of machine learning has been successfully applied to synthesize zeolites with different topological structures. [62,63] However, few reports are related to the structural optimization of zeolites by machine learning models.
This part reasonably discusses the effect of synthesis conditions on anatase and broadens the application of crystallization modifiers in preparing high-performance TS-1. Based on 68 groups of experimental data from our previous experiment and reported articles, machine learning models were trained to study the effects of preparation conditions and different functional groups in additives on the formation of anatase. The additives include BDC, DABDC, DHBDC, pphenylenediamine, 2-methylimidazole, p-aminobenzoic acid, 2-aminoterephthalic acid, 2,5-dimethylterephthalic acid, hydroquinone, Tween20, L-carnitine, Triton X-100, PVB, PDADMAC, 1,3,5-benzenetricarboxylic acid and PEG-1000. [22,[28][29][30][31]64,65] We visualized the types and quantities of functional groups in additives, as well as the preparation parameters (Ti/Si molar ratio, TPAOH/Si molar ratio, H 2 O/Si molar ratio, crystallization temperature and time). The parallel coordinate plot is shown in Figure S9. The ordinate of functional groups is calculated by multiplying the molar number of additives with the number of functional groups contained. Blue lines (1) represent that anatase exists in TS-1 under this condition, and red lines (0) refer to the opposite results. When the additives contain amino, imino and carbon-carbon double bond functional groups, the probability of anatase in TS-1 samples is high. The phenomenon indicates that the additives containing the above functional groups may play a positive effect on the formation of anatase. However, the influence of preparation parameters is complex, so machine learning models were used to analyze the data more deeply.
We processed the data with SMOTE algorithm to solve the problem of unbalance date categories. Eight machine learning models, including RF, SVC, DT, SGD, LR, XGBoost, KNN and NB, were trained to predict the existence of anatase. In the standard 10-fold cross-validation process, the collected data were divided into 10 subsets. Then, the eight machine learning models were trained on nine subsets, and the remaining subset was used as test set. [35] After adjusting the parameters by the Bayesian Optimization algorithm, the cross-validation score of each model is shown in Figure 6A. XGBoost, RF, NB, LR and DT models perform well, and the 10-fold cross-validation accuracy is above 0.8. In addition, we took 70% of the collected data set as a training F I G U R E 6 Comparison of machine learning models scores: (A) accuracy of 10-fold Cross-Validation, (B) accuracy of the training set and test set set and 30% as a test set to train the above eight machine learning models, and realized the high-speed and low-cost performance prediction of TS-1. The results are shown in Figure 6B. It can be seen that SVC and SGD models show good accuracy in training set but poor performance in test set. There is an evident overfitting phenomenon for SVC and SGD models, indicating that these two methods have large generalization error and poor generalization ability for data set. RF, DT, LR, XGBoost and NB models show excellent prediction accuracy in both the training and test set, which corresponds to the results of 10-fold cross-validation.
Eight machine learning models were further compared by receiver operating characteristic curve (ROC) in Figure S10. ROC shows the relationship between recall and false positive curve. The evaluation metrics are used to evaluate the results of TS-1 classification and anatase detection. The dotted line in Figure S10 represents the ROC curve of the pure random classifier. The classifier with excellent performance should deviate far from the dotted line and locate in the upper left corner, indicating that the machine learning model has higher prediction accuracy. [35] It is unrealistic to distinguish several classifiers by directly comparing ROC curves. So, area under curves (AUC) were measured to make a more intuitive judgment in Figure S10. The AUC of a perfect classifier should be 1, while the AUC of a pure random classifier should be 0.5. [35] Compared with the other models, NB and LR models show well prediction accuracy with AUC values of 0.90 and 0.88, respectively.
Most machine learning algorithms are the "black box", which has been proved difficult to interpret the prediction results. [35] For the above four models (NB, LR, RF, DT) with high prediction accuracy, the feature importance can rank these features, [36] which is helpful to study the reaction mechanism and accelerate the development of anatase-free TS-1. Figure S11 shows the feature importance of 12 features, and the four models all reveal that hydroxyl group has a critical impact on the formation of anatase. [36,66] Combined with the experimental part of this paper, it can be inferred that hydroxyl group can effectively inhibit the aggregation of Ti species and the formation of anatase. In addition, it can also be found in the DT model that carboxyl group and crystallization temperature have a significant influence on the results. So, we speculate that the synergistic effect of hydroxyl and carboxyl groups inhibits the formation of anatase. Higher crystallization temperature may accelerate the aggregation of Ti species and promote the formation of anatase; Ti/Si molar ratio and H 2 O/Si molar ratio also directly or indirectly affect the existence state of Ti species in TS-1.
LR model was selected for further data analysis owing to its high prediction accuracy compared with other models. The Partial dependency plot shows the marginal effect of features on the model prediction results. In this part, we evaluated the relevance of functional groups and preparation conditions on the generation of anatase. Figure 7 displays the change of predicted value with feature value. The partial dependence shows a gradual downward trend with increasing the usage of hydroxyl, carboxyl, ether bond and ester groups, indicating that these four functional groups play a positive role in inhibiting the formation of anatase. The speculation is consistent with the result of TS-DHBDC-48h and some TS-1 samples prepared using Triton X-100 and Tween20 as soft templates. However, imino group, amino group, carbon-carbon double bond and Ti/Si molar ratio can promote the formation of anatase, which is consistent with the result of prepared samples adding 2-methylimidazole and p-phenylenediamine.
It is worth noting that only the DT model (AUC = 0.79) that performs well is a white box model with excellent interpretability among the above models. [35] Therefore, the effect of functional group types and preparation parameters on the formation of anatase in TS-1 can be revealed through the decision process. The obtained flowchart is shown in Figure 8. It shows the decision process of how to classify the reaction results according to the experimental input conditions. The samples attribute of each node indicates the number of instances. And the value attribute in every node describes the number of training instances of each category, representing the number of samples without anatase (left) and with anatase (right). The Gini attribute of the node measures its impurity, and class represents the category of the leaf node. When the number of samples with anatase is more than the samples without anatase, it is classified as 1, and the color of the node is blue, otherwise it is orange. [67] In this case, the most likely path to synthesize TS-1 without anatase was obtained, which is indicated by the red arrow. Starting from the root node, the flowchart was divided into two categories according to the number of hydroxyl groups in additives. It is judged that the addition amount is >0.014, and then it moves to the right child node. Further, the node will evaluate whether the addition amount of carboxyl is ≤0.02 and enter the next depth. The judgment condition is changed to whether the crystallization time is ≤42 h. Because the node is impure and does not reach the maximum depth, it is split here. The judgment condition is the number of hydroxyl groups, but its threshold value becomes 0.108. It is judged to enter the left leaf node, which is pure and the color is the deepest orange. All instances are classified as the absence of anatase, and the decision tree stops here. In addition, when the added amount of hydroxyl group is >0.014, and carboxyl group is >0.02, all examples are also classified as the absence of anatase, which corresponds to the above experimental results. Similarly, the instances of the route marked by blue arrow are classified as the presence of anatase, and the node color is the deepest blue. Based on more experimental data, these machine learning models are expected to achieve higher accuracy. In the feature, the developed machine learning models can be combined with high-throughput experiments, which will greatly accelerate the development of high-performance zeolites. [36]

CATALYTIC PERFORMANCE
The catalytic performance of the as-synthesized TS-1 was investigated for the epoxidation of 1-hexene using H 2 O 2 as oxidant at 333 K for 2 h (Figure 9). TS-WAC-48h shows a poor conversion (38.1%) of 1-hexane and selectivity (99.2%) of 1,2-epoxyhenane, which can be attributed to the negative effect of anatase and the limitation of lower mesopore volume. [22,68] The main product is 1,2-epoxyhenane, and by-products include pentanal and ring-opening products 1,2-hexanenol. 1-hexene conversion was boosted when using aromatic compounds-mediated zeolites as catalysts. TS-BDC-48h and TS-DABDC-48h display improved 1-hexene conversion (40.9%, 48.1%) owing to the larger mesopore volume and higher framework Ti content than TS-WAC-48h, but the selectivity decreases to 98.8% and 96.1%, respectively. TS-DHBDC-48h exhibits a much higher catalytic activity, giving 1-hexene conversion of 49.3% and 1,2-epoxyhenane selectivity of 99.9%. The conversion of 1-hexene using DHBDC-mediated TS-DHBDC-48h as catalyst improved by 29.4% than conventional TS-WAC-48h. Furthermore, the conversion of 1-hexene catalyzed by DHBDC-mediated zeolites with different crystallization time is shown in Figure 9B, which is 34.7%, 34.4%, 38.7% and 41.0%, respectively. And the selectivity of 1,2-epoxyhexane is 99.9%. The increased conversion may be related to the crystallinity of these samples. When crystallization time is above 4h, the zeolites show higher catalytic activity than TS-WAC-48h. Although TS-DHBDC-1h exhibits the largest mesoporous volume and specific surface area, the lowest conversion of 1-hexene may be caused by the relatively low framework titanium content in the early crystallization stage. The reaction pathway of olefin epoxidation in TS-1/H 2 O 2 /CH 3 OH system is shown in Figure S12. It is generally believed that the active sites of TS-1 are the tetrahedrally coordinated Ti species. [4,5] The path can be summarized as follows: formation of the active intermediate Ti-OOH, stabilization of the intermediate and transfer of the active O atoms. [54] Due to the coordination-unsaturated and electron-accepting ability, the tetrahedrally coordinated Ti species preferentially adsorb and activate H 2 O 2 , and subsequently form five-membered ring Ti-O α -O β -H active intermediates coordinated with methanol. The active O atoms on the intermediate are then transferred to the adsorbed olefin molecules to complete the epoxidation reaction, while the active sites return to the initial state.
We compared the catalytic performance of aromatic compounds-mediated zeolites with those reported TS-1 samples ( Figure 9C) in terms of 1-hexene conversion and 1,2-epoxyhexane selectivity. [69] The property of DHBDCmediated TS-DHBDC-48h was much better than those of most reported TS-1 zeolite. The 1-hexene conversion is at a fairly high level, and the 1,2-epoxyhexane selectivity reaches the highest value reported, which are even higher than those reported properties using more oxidant or reacting for longer time. The excellent catalytic performance of TS-DHBDC-48h is mainly attributed to the following points. (i) Larger mesopore size and volume: interconnected intercrystalline mesopores (about 50 nm) contribute to the formation of five-membered Ti-O α -O β -H intermediates and promote the diffusion of olefins to intermediates and epoxy products. (ii) Higher framework Ti content: more tetrahedrally coordinated Ti species provide more active adsorption sites for H 2 O 2 and substrates. (iii) Efficient utilization of oxidants: the absence of anatase avoids the decomposition of H 2 O 2 and improves its utilization ability.
The recycling experiment was carried out using TS-DHBDC-48h for the epoxidation of 1-hexene ( Figure 9D). The conversion of 1-hexene maintains at about 50%, and the selectivity of 1,2-epoxyhexane all retains 99.9% during the recycling process. Moreover, the MFI topology is well kept, but the relative crystallization decreases to 102.8% ( Figure  S13). The decreased crystallinity may be caused by the loss of active Ti sites and the formation of defects. The UV-Vis spectrum of recycled TS-DHBDC-48h suggests no anatase exists in the catalyst after reaction and calcination ( Figure S14). The I 960 /I 800 decreases from 2.15 to 1.94 ( Figure S15). The morphology of reused TS-DHBDC-48h remains ellipsoid-like in Figure S16. All these results indicate that TS-DHBDC-48h has excellent stability and recycling performance for the epoxidation of 1-hexene.
The conversion of 1-hexene when using TS-DHBDC-48h (TTIP) and TS-DHBDC-48h (TEOT) as catalysts is 47.9% and 46.3% ( Figure S17), respectively. Except for 1,2-epoxyhexane, there are no other by-products in the epoxidation reaction. This experiment confirms the general applicability of DHBDC as a multifunctional crystallization mediator in the field of Ti-containing zeolites.
To confirm the catalytic performance for other olefins of the aromatic-compounds mediated TS-1, cyclohexene epoxidation was also tested. The cyclohexene conversion of all samples in Figure S18A is 22.55%, 26.12%, 28.22% and 34.57%, respectively. Compared with TS-WAC-48h, the conversion of cyclohexene using TS-DHBDC-48h as a catalyst increased by 53.3%. But the cyclohexane oxide selectivity of TS-DHBDC-48h is only 22.0%. Furthermore, the mesopores and large surface area of TS-DHBDC-1h make it an excellent catalytst. The cyclohexene conversion (26.43%) and cyclohexane oxide selectivity (34.8%) are superior to TS-WAC-48h. The conversion of cyclohexene catalyzed by TS-DHBDC-2h, TS-DHBDC-4h and TS-DHBDC-24h is 23.20%, 24.97%, 28.49%, respectively. In conclusion, the state of Ti species and pore width are the main factors affecting the catalytic performance of TS-1 zeolites. It is evident that using aromatic compounds, especially DHBDC, to regulate the crystallization of TS-1 is an effective strategy to improve catalytic performance.

CONCLUSIONS
This paper not only develops a facile aromatic compoundsmediated synthesis method for fabricating hierarchical anatase-free TS-1 zeolites but also provides a new path for microstructure optimization of zeolites assisted by machine learning strategy. High-performance TS-1 zeolites were synthesized by introducing aromatic compounds containing different functional groups during the crystallization process. Aromatic compounds lead to the oriented-attachment of zeolite nanoparticles and contribute to the evolution of intercrystalline mesopores in the ripening process. The synergistic coordination of carboxyl group and hydroxyl group in DHBDC with Ti(OH) 4 helps to realize the uniform distribution of titanium species and eliminate the generation of anatase. In addition, 8 machine learning models were used to study the effects of the types and quantities of additive functional groups and preparation parameters on the formation of anatase by combining machine learning analysis with experimental data. And the DT model provides the preparation paths of anatase-free TS-1 under the guidance of crystallization time and the addition amount of hydroxyl and carboxyl groups. The larger mesopore volume and proper Ti chemical environment make TS-DHBDC-48h a universal catalyst in the epoxidation of 1-hexene and cyclohexene. This study provides a novel aromatic compounds-mediated synthesis strategy and demonstrates the potential of the developed machine learning method for accelerating microstructural optimization of zeolites.

C O N F L I C T O F I N T E R E S T
The authors declare no competing financial interest.

D ATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.