Diffusion Dependence of the Dual-Cycle Mechanism for MTO

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Diffusion Dependence of the Dual-Cycle Mechanism for MTO Reaction Inside ZSM-12 and ZSM-22 Zeolites Zhiqiang Liu, Yueying Chu, Xiaomin Tang, Ling Huang, guangchao Li, Xianfeng Yi, and Anmin Zheng J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b07374 • Publication Date (Web): 12 Sep 2017 Downloaded from http://pubs.acs.org on September 15, 2017

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The Journal of Physical Chemistry

1

Diffusion Dependence of the Dual-cycle Mechanism for MTO

2

Reaction Inside ZSM-12 and ZSM-22 Zeolites

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Zhiqiang Liua,b,#, Yueying Chua,#, Xiaomin Tanga,b,#, Ling Huang a,*,

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Guangchao Lia,b, Xianfeng Yia,b, Anmin Zhenga,*

6 7

a

8

National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and

9

Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China.

State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics,

b

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University of Chinese Academy of Sciences, Beijing 100049, P. R. China.

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Corresponding authors: Fax: +86 27 87199291.

13

*

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E-mail addresses: [email protected]; [email protected]

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[ # ] These authors contributed equally to this work.

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ACS Paragon Plus Environment


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Abstract: The “dual-cycle” pathway (i.e., olefins-based cycle and aromatics-based

18

cycle) of methanol-to-olefin (MTO) has been generally accepted as hydrocarbon pool

19

mechanism. Understanding the role of diffusion of reactant, intermediate and product

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in the MTO process is essential in revealing its reaction mechanism. By using

21

molecular dynamics (MD) simulations for two one-dimensional zeolites (ZSM-12 and

22

ZSM-22) with a channel difference being only 0.3 Å in pore sizes, the diffusion

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behaviors of some representative species following “dual-cycle” mechanism (e.g.,

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methanol, polymethylbenzenes and olefins molecules) have been theoretically

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investigated in this work. It was found that the diffusion coefficients of methanol and

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olefins along ZSM-12 were ca. 2~3 times faster than that along ZSM-22 at 673 K. In

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the aromatics-based cycle, the polymethylbenzenes are crucial intermediates during

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the MTO reaction. 1,2,3,5-tetramethylbenzene is almost imprisoned inside ZSM-12,

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such slower diffusion of tetramethylbenzene offers more opportunities for the geminal

30

methylation reaction to form MTO activated pentamethylbenzenium cation, which

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would split into olefins through “paring” or “side-chain” pathways. However, in the

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ZSM-22 zeolite, since 1,2,4-trimethylbenzene is stacked, the following methylation

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reaction solely results in the formation of tetramethylbenzene, which is not a MTO

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activated species in ZSM-22 and more bulky polymethylbenzene further blocks the

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channel more seriously. When it comes to the olefins-based cycle, olefins can diffuse

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freely inside these two zeolites with methoxide intermediate bound to the zeolite

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frameworks, and thus facilitates formation of longer-chain olefin through olefin

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methylation reaction in these two zeolite catalysts. Combination of the higher reaction


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activity (from DFT calculation) and the longer contact time (from MD simulation)

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between the olefin and methoxide, is apparently illustrated the olefins-based cycle

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does more preferentially occur inside ZSM-22 than ZSM-12. Apparently, the MTO

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reaction mechanism is strongly determined by the diffusion behaviors of reaction

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species inside the zeolite confined pores.

44 45

1. Introduction

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Owing to the high energy density and easy transportability, olefins play an essential

47

part in the chemical supply chain.1 Nevertheless, the increasing demand for energy

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and growing depletion of crude oil require the search for new routes for the

49

production of olefins from non-oil sources.2-3 Among these alternative reaction routes,

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the methanol-to-olefins (MTO) conversion on zeolites has drawn considerable

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attention from both fundamental research and industrial application in the last several

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decades.4-8 The interest in the use of methanol as a source feedstock has risen since it

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can be produced from coal, natural gas, biomass and even CO2.2, 9 Currently, many

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researches focus on studying the MTO reaction mechanism with the aim to enhance

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catalyst performance and promote product selectivity.10-12

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It remains highly challenging to unravel the MTO reaction mechanism and its

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dependence on the zeolite framework structures, which results from the complexity in

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product distribution and the difficulty in the identification of active intermediates.5

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13-14

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for the formation of light olefins during MTO reaction.15-16 Aromatics (e.g.,

An indirect hydrocarbon pool (HP) mechanism is now gaining wider acceptance



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polymethylbenzenes (PMBs)) and olefins represent two kinds of important HP species

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for MTO conversion. As illustrated in Scheme 1, the different properties of PMBs and

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olefins result in two distinct catalytic cycles in principle, known as dual-cycle

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concept.10, 17-18 During the aromatic-based cycle, light olefins split off from alkyl side

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chains of cyclic compounds.19-21 In the olefin-based cycle, similar methylation and

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cracking pathway for aliphatic chains are followed.22-23 Extensive experimental and

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theoretical results indicated that aromatic species are likely to be the dominating

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hydrocarbon pool species in H-SAPO-34.24 Nevertheless, a series of contradictory

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proposals have been arisen on which cycle was followed in some specific zeolite

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catalysts. For example, it’s experimentally demonstrated by Svelle et al. that ethene is

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exclusively produced from the aromatic-based cycle, while propene and higher olefins

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are mainly produced via the olefin-based cycle over H-ZSM-5.10, 18, 25 Therefore, it is

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crucial for understanding the dependence of the HP species on zeolite frameworks and

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the relative contribution of each cycle to control the MTO catalytic performance and

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olefin selectivity.

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Scheme 1. The reaction mechanisms of the MTO process in Zeolites. In recent years, studies of the MTO mechanism on two unidimensional ZSM-12


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and ZSM-22 zeolite catalysts have been debated as well.26-32 ZSM-12 and ZSM-22

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zeolites possess very similar pore dimensions (5.6 Å × 6.0 Å for ZSM-12 and 4.6 Å ×

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5.7 Å for ZSM-22) (see Figure 1). Song et al. proposed that 0.3 Å difference in the

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zeolite pores would cause a dramatic difference in their MTO catalytic

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performances.14 28 It is revealed that ZSM-12 exhibited a high MTO activity at 673 K,

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while ZSM-22 was inactive under the same reaction conditions. This difference

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means that the aromatic hydrocarbon-pool mechanism worked on the ZSM-12 zeolite

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with the 6.0 Å pore size, while the catalytic cycle was almost inhibited inside the

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ZSM-22 due to its smaller pore. However, it has been observed experimentally by

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Svelle29, 33-34 and Liu et al.4, 26-27 that the ZSM-22 zeolite exhibited a great catalytic

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activity for the MTO reaction through olefin methylation cracking cycle. To reveal the

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influence of the channel difference for these two zeolites on the reaction routes and

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catalytic performances, systematic DFT calculations have been performed in our

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previous work.31 The calculated activation barriers and reaction energies

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demonstrated that the 0.3 Å channel difference between ZSM-12 and ZSM-22 zeolites

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lead to a dramatic discrepancy in their transition state selectivity associated with the

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aromatic-based hydrocarbon pool (HCP) mechanism.

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It is well known that, besides the activation barriers, the diffusion behaviors of

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reactant, intermediate and product inside confined pores of zeolite are also essential

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factors to strongly determine the catalytic performances and product selectivity during

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catalytic processes.13,

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35-36

The diffusivity can be measured experimentally by the

pulsed field gradient nuclear magnetic resonance (PFG-NMR) and quasi-elastic



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neutron scattering (QENS) methods.37-38 Recently, microimaging by IR microscopy

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has been successfully applied to get the diffusivities, by monitoring the distribution of

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the reactant molecules over the catalyst particles in the moderate experimental

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conditions.39-40 Nevertheless, it is a challenge to in situ obtain the diffusion coefficient

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in the reaction conditions (e.g., high temperature and high pressure). As a complement

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to the experimental approach, the molecular dynamics (MD) theoretical simulation is

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an effective approach to obtain the diffusion coefficient. It's demonstrated that such

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state-of-the-art MD calculation has been already used to reveal the influences of

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temperature and loading on the selectivity of specific hydrocarbon in zeolite

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catalysts.13 Ghysels and coworkers clarified the dependence of temperature,

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composition, acidity, and topology on the diffusion of ethene inside microporous

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zeolites (AEI, CHA, AFX) with 8-ring channel based on the MD simulations.41

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Furthermore, Wang et al. performed MD to provide an insight into the topology effect

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on the diffusion of ethene and propene in CHA, MFI, BEA and FAU zeolites with 8-,

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10- and 12-ring channels.42 Recently, Bu et al. studied the diffusion mechanism of

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xylene isomers in ZSM-5 using theoretical simulations as well.43

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As mentioned before, MD calculation has been used to provide the diffusion

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information of the alkane, olefin and aromatic species inside various zeolite channels

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with different shapes and sizes.13 However, little systematic work has been done so far

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for the diffusion of reaction species in the MTO reaction, especially for the

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co-adsorbed condition with methanol (reactant), polymethylbenzenes/methoxide

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(intermediate), and olefin (product) inside the zeolite catalysts.


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In this work, MD simulations were carried out to investigate the diffusion

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behaviors of reaction species in ZSM-12 and ZSM-22 zeolite materials which have

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received much interest in the MTO process. Base on the diffusion coefficient, contact

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time and probability density function, we have established the connection between

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diffusion behaviors and MTO reaction, and then systematically discussed the

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diffusion-depended dual-cycle mechanism (aromatics-based and olefins-based cycle)

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for MTO reaction inside these two zeolites.

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2. Model and Computational Details

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2.1 Zeolite Frameworks

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Figure 1. Pore size and geometry of (a) MTW along the [010] one-dimensional

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12-ring channel and (b) TON down the [001] one-dimensional 10-ring channel.

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As shown in Figure 1, ZSM-12 (MTW) and ZSM-22 (TON) are two typical

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one-dimensional zeolites respectively with the pore sizes of 5.6 Å × 6.0 Å and 4.6 Å ×



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5.7 Å. Notably, a 0.3 Å difference in pore size is present for these two zeolite catalysts.

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In this work, based on the accessibility of adsorbed molecules, the Si1-O2-Al3 and

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Si1-O1-Al1 sites were chosen as the locations of Brønsted acidic protons, methoxide

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(-OCH3) and ethoxide (-OC2H5) species for ZSM-12 and ZSM-22, respectively. (see

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Figure 1). Initial framework structures of MTW and TON were extracted from the

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International Zeolite Associations (IZA) database.44 1 × 5 × 2 and 2 × 2 × 5 supercells

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with the corresponding crystal lattice sizes of 25.6× 26.3 × 24.2 Å3 and 28.2 × 35.7 ×

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26.3 Å3 were selected to represent ZSM-12 and ZSM-22 zeolites, respectively. There

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were four acidic protons or alkoxide species (i.e., -OCH3 and -OC2H5) separately

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distribute inside the different channel in ZSM-12 and ZSM-22 zeolites. During the

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MD simulation, four guest molecules (e.g., methanol) located inside the different

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channel of these two zeolites were used for the model of infinite dilution. Furthermore,

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detailed parameters used in the theoretical calculation were summarized in Table S1.

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2.2 Molecular Dynamics Simulation.

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All MD simulations were performed in the canonical ensemble (NVT) where the

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number of particles (N), simulation volume (V), and temperature (T) were kept

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constant. The simulated temperature was held at 673 K and controlled by a

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Nosé-Hoover thermostat with a coupling time constant of 1 ps. The velocity Verlet

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algorithm was used throughout to integrate the Newton’s equations of motion. The

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consistent valence force field (CVFF), which has been proven to be able to accurately

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predict the adsorption and diffusion of hydrocarbons in various zeolites materials,45-47


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was adopted in this work. The long-range electrostatic interactions were calculated

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using the Ewald summation method48 and Lennard-Jones interactions were calculated

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with a 9.5 Å cutoff radius.

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Each MD simulation started with an annealing and followed by 6×106 time steps

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equilibration with 0.5 fs time step. Then, a production run of 4×107 time steps was

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performed. At least 5 independent MD simulations were carried out for each system

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for better statistics and thus the total MD simulation time was 100 ns. The trajectories

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were recorded every 1000 steps to analyze the mean square displacement, diffusion

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coefficients and contact time.

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2.3 Diffusion Coefficient The mean square displacement (MSD) of an adsorbed molecule is defined as the following equation (1) 1 MSD(τ ) = Nm

174

Nm

1 ∑i N τ

2

Nt

∑ [r (t i

0

+ τ ) − ri (t0 )]

(1)

t0

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where N m is the number of adsorbed molecules, Nτ is the number of time origins

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used in calculating the average, and ri is the coordinate of the center of mass of

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molecule i .

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For one-dimensional diffusion, the slope of the MSD as a function of time

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determines the self-diffusion coefficient, Ds, defined according to the so-called

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Einstein relation49

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MSD (τ ) = 2 Dsτ + b

(2)

where b is the thermal factor arising from atomic vibrations. The line is fitted in the



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range 50-1000 ps using a least-squares fit. The reported MSD curves and

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corresponding Ds values in section 3 are calculated as the average of five independent

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MD trajectories.

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2.4 Free Energy The free energy profile is a reliable method to explain the diffusion behavior of

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hydrocarbon molecule passing through the zeolites.41,

49

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coordinate of the molecule is chosen along the direction of unidimensional channel in

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each zeolite, starting with zero and ending at point of the length of the unite cell. Then

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the normalized histogram of trajectory ξ(t) is the probability distribution P(ξ) of the

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gas with respects to the direction of channel. Here ξ represents the coordinate of the

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center of mass of molecule. By taking the logarithm of P(ξ), the free energy profile

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F(ξ) = -kBTlnP(ξ) is obtained up to an arbitrary constant, where T and kB are the

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temperature and the Boltzmann constant, respectively.

Firstly, the reaction

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2.5 Contact Time and Probability Density Function

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For most chemical reactions inside zeolite confined pores, diffusion plays a crucial

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role to bring the reactants in close proximity, which is also an essential prerequisite

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for MTO reaction.13 Contact time is defined as the average residence time for the

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diffused reactant around each active site in certain region, and this parameter can

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reflect the spatial proximity during the diffusion and reaction.

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In this work, the contact time between hydrocarbon and active site of zeolites was


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used to investigate the methylation and dimerization in the MTO reaction. Taking

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methylation of ethene as an example, the contact time is calculated as following.

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In order to calculate the contact time, the sizes of zeolites were amplified by N

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times (i.e., 1×N×1, 1×1×N for ZSM-12 and ZSM-22 respectively) to completely

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cover the trajectory based on MD simulation of adsorbed molecules. The total time (T)

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along the trajectory direction was count when the distance between ethene and

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methoxide is shorter than the certain distance. Therefore, the average contact time

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(CT) can be calculated by using T/N.

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The probability density function is defined as

ρ AB ( r

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)

=

∆Nr + ∆r NA

(3)

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where r is the distance between the active site A and adsorbed molecules B, and NA is

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the number of active sites A. ∆Nr + ∆r

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nearest-neighbor of A and located in the distance between r and r + ∆r .

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means the average over all trajectory.

describes number of B which is the

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Results and Discussion

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3.1 Diffusion Characteristics of Reactant and Product Inside ZSM-12 and

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ZSM-22 Zeolites in MTO Reaction

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As a complement to the experimental approach, the molecular dynamics (MD)

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simulation is an effective approach to obtain the diffusion coefficient. Since there are

226

lots of reaction species involved in the MTO reaction, such as reactant (methanol),



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products (ethene, propene, butene), intermediate species (p-xylene, trimethylbenzene,

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tetramethylbenzene, methoxide) and zeolite frameworks (ZSM-12, ZSM-22), it is

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better to use a generalized force field in the MD calculations. The consistent valence

230

force field (CVFF), which is a generalized valence force field developed by

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Dauber-Osguthorpe, has been widely used in studying the adsorption and diffusion of

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gas inside zeolites.45-47 First of all, the reliability of CVFF for the zeolite framework

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and hydrocarbon species involving the MTO reactions has also been verified (see

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Figure S1 and Table S2, S3, S4, S5 in Supporting Information).

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It’s experimentally illustrated that 0.3 Å difference in the pore sizes between

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ZSM-12 and ZSM-22 zeolite catalysts can lead to the significant difference in the

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MTO catalytic performances.28 As is well known, the diffusion is an important factor

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in heterogeneous catalytic processes, especially for the catalytic reaction confined

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inside the porous catalysts.13, 35 Besides the thermodynamic and kinetic factors as

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mentioned in our previous work by the DFT calculation, the diffusion of MTO

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reaction species inside two nano-size zeolite pores strongly mediate the reaction

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pathways and their reactivities. In order to comprehensively understand the

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relationship between the zeolite structure and the catalytic performance, it’s necessary

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to deeply and systematically investigate the diffusion behaviors of reactant (e.g.,

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methanol), possible olefin products (e.g., ethene, propene and butane) and

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intermediate (methoxide and PMB), particularly under their co-adsorbed conditions

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inside these two zeolite catalysts in MTO reaction processes.


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Figure 2. Initial atomic structure and solvent surface of ZSM-12 and ZSM-22 zeolites.

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Ethene adsorbed in (a) ZSM-12 (along [0 1 0] direction) and (b) ZSM-22 (along [0 0

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1] direction). Both ethene and p-xylene molecules co-adsorbed in (c) ZSM-12 (along

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[1 0 0] direction) and (d) ZSM-22 (along [1 0 0] direction).

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The initial structures of ethene adsorbed inside ZSM-12 and ZSM-22 are shown in

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Figure 2a and 2b. In the MD simulations, five different initial configures were

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generated after being annealed, then the analyses of mean square displacement (MSD)

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and diffusion coefficient were carried out by averaging over all five MD results. The

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MSD of infinitely diluted methanol, ethene, propene, and butene molecules in

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ZSM-12 and ZSM-22 zeolites under the MTO reaction temperature (673 K) are



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displayed in Figure 3. From the slope of MSD, the relative diffusion rate can be

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qualitatively determined and lead to conclusion that the diffusion of both methanol

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and olefins in ZSM-12 are faster than that in ZSM-22 zeolite.

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Figure 3. MSD with error bars of methanol, ethene, propene and butene molecules in

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(a) ZSM-12 and (b) ZSM-22 zeolites at 673 K and infinite dilution.

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Table 1. Self-diffusion coefficient (Ds) with standard error in parentheses and isosteric

269

heat (Qst) of hydrocarbon in ZSM-12 and ZSM-22 zeolites at 673 K and infinite

270

dilution. Ds (10-8· m2·s-1) Molecule methanol ethene propene 1-butene

ZSM-12 11.15 10.72 8.10 5.19

(1.17) (1.48) (0.88) (1.09)

Qst (kcal·mol-1)

ZSM-22 4.65 3.29 3.47 1.47

ZSM-12 (1.07) (0.23) (0.45) (0.42)

7.3 9.8 12.7 15.9

ZSM-22 7.9 10.9 14.0 17.5

271 272

Furthermore, the self-diffusion coefficient (Ds) for gas inside zeolites can be

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extracted using the Einstein relation by equation (2). Thus, on the basis of MSD


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predicted by MD calculations, the according Ds for methanol, ethene, propene, and

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1-butene molecules in ZSM-12 and ZSM-22 zeolites can be determined quantitatively.

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As listed in Table 1, Ds for methanol in ZSM-12 and ZSM-22 are 11.15×10-8 and

277

4.65×10-8 m2/s, respectively. Apparently, 0.3 Å difference in the pore size of ZSM-12

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zeolite has led to ca. 2.5 times enhancement in the methanol diffusion in comparison

279

with that inside ZSM-22 zeolite. Similar trends are also observed for the olefins

280

(ethene, propene and butene) whose diffusion coefficients range from 10.72×10-8 to

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5.19×10-8 m2/s in ZSM-12 while from 3.29×10-8 to 1.47×10-8 m2/s inside ZSM-22. It

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is generally accepted that the pore size plays a vital role in the diffusivity in the

283

heterogeneous catalysis, and the gas diffuse faster in micropores zeolites with

284

relatively larger pores.50 Moreover, the kinetic diameters of adsorbed olefins with the

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similar structure are important parameter to mediate the diffusion in the zeolites.13 It’s

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generally accepted that a molecule with the smaller kinetic diameters and molecular

287

weight always leads to relatively faster diffusion in the same zeolite catalyst. Taking

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olefin as an example, the diffusion coefficients for ethene and propene inside the

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ZSM-12 zeolite were 10.72×10-8 and 8.1×10-8 m2/s, respectively, and this tendency is

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in inverse proportion to their kinetic diameters (ethene: 3.9 Å < propene: 4.5 Å).51 It

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should be pointed out that although butene (4.5 Å) has a similar kinetic diameter with

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propene, the relatively larger molecular weight leads to its relatively slower diffusion

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(5.19×10-8 m2/s) inside ZSM-12 zeolite.51 Interestingly, the diffusion of propene

294

(3.47×10-8 m2/s) is found to be a little higher than that of ethene (3.29×10-8 m2/s) in

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ZSM-22 zeolite, which might be attributed to the resonant diffusion. Resonant



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diffusion, as a unique property for gas molecule diffusion inside confined space (such

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as zeolites) when the length of molecule matches the lattice result in low activation

298

energy barrier for diffusion, has been studied by both experiment and theory.52 It’s

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well known that, there are low- and high-energy areas inside the confined space of

300

zeolites. For small gas molecule with short chain, such as methanol, it locates at either

301

low-energy area or high-energy area in the channel, and the diffusion barrier can be

302

obtained by the energy difference of these two areas. During the diffusion process,

303

methanol must overcome this barrier. However, when the end-to-end chain length of a

304

molecule matches the lattice periodicity (in the case of resonant diffusion), the

305

molecule synchronously occupied both low- and high-energy areas of the zeolite,

306

hence would experience a relatively lower diffusion barrier. Exactly, propene rather

307

than ethane molecule meets the requirement of the resonant diffusion, therefore, it’s

308

accepted that a relatively faster diffusion of propene compared with ethene inside

309

ZSM-22 zeolites. Yoo et al. provided experimental evidence for resonant diffusion of

310

normal alkanes in ZSM-12 and Schuring et al. also found the enhanced diffusivity of

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n-octane in ZSM-22 at 333 K by MD simulation.52-54

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313


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Figure 4. (a) Scatter diagram of the diffusion coefficients and isosteric heat of

315

methanol, ethene, propene and butane molecules. (b) Free energy of methanol and

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propene in ZSM-12 and ZSM-22 zeolites.

317 318

On the other hand, the diffusion behavior can be understood from the interactions

319

between host (zeolite framework) and guest (adsorbed molecule) systems as well. It’s

320

well known that isosteric heat is directly connected with the sorption affinity of guest

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molecules in zeolites at the Henry regime, where the guest-host interactions govern

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the sorption. As shown in Table 1, the isosteric heat are in the order: methanol

Diffusion Dependence of the Dual-Cycle Mechanism for MTO

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