Peng Bai

Ultra-selective high-flux membranes from directly synthesized zeolite nanosheets

A zeolite with structure type MFI is an aluminosilicate or silicate material that has a three-dimensionally connected pore network, which enables molecular recognition in the size range 0.5–0.6 nm. These micropore dimensions are relevant for many valuable chemical intermediates, and therefore MFI-type zeolites are widely used in the chemical industry as selective catalysts or adsorbents. As with all zeolites, strategies to tailor them for specific applications include controlling their crystal size and shape. Nanometre-thick MFI crystals (nanosheets) have been introduced in pillared and self-pillared (intergrown) architectures, offering improved mass-transfer characteristics for certain adsorption and catalysis applications. Moreover, single (non-intergrown and non-layered) nanosheets have been used to prepare thin membranes that could be used to improve the energy efficiency of separation processes. However, until now, single MFI nanosheets have been prepared using a multi-step approach based on the exfoliation of layered MFI, followed by centrifugation to remove non-exfoliated particles. This top-down method is time-consuming, costly and low-yield and it produces fragmented nanosheets with submicrometre lateral dimensions. Alternatively, direct (bottom-up) synthesis could produce high-aspect-ratio zeolite nanosheets, with improved yield and at lower cost. Here we use a nanocrystal-seeded growth method triggered by a single rotational intergrowth to synthesize high-aspect-ratio MFI nanosheets with a thickness of 5 nanometres (2.5 unit cells). These high-aspect-ratio nanosheets allow the fabrication of thin and defect-free coatings that effectively cover porous substrates. These coatings can be intergrown to produce high-flux and ultra-selective MFI membranes that compare favourably with other MFI membranes prepared from existing MFI materials (such as exfoliated nanosheets or nanocrystals).

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and >330,000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.

Water 26-mers Drawn from Bulk Simulations: Benchmark Binding Energies for Unprecedentedly Large Water Clusters and Assessment of the Electrostatically Embedded Three-Body and Pairwise Additive Approximations

It is important to test methods for simulating water, but small water clusters for which benchmarks are available are not very representative of the bulk. Here we present benchmark calculations, in particular CCSD(T) calculations at the complete basis set limit, for water 26-mers drawn from Monte Carlo simulations of bulk water. These clusters are large enough that each water molecule participates in 2.5 hydrogen bonds on average. The electrostatically embedded three-body approximation with CCSD(T) embedded dimers and trimers reproduces the relative binding energies of eight clusters with a mean unsigned error (MUE, kcal per mole of water molecules) of only 0.009 and 0.015 kcal for relative and absolute binding energies, respectively. Using only embedded dimers (electrostatically embedded pairwise approximation) raises these MUEs to 0.038 and 0.070 kcal, and computing the energies with the M11 exchange-correlation functional, which is very economical, yields errors of only 0.029 and 0.042 kcal.

Multicomponent adsorption of alcohols onto silicalite-1 from aqueous solution: isotherms, structural analysis, and assessment of ideal adsorbed solution theory.

Configurational-bias Monte Carlo (CBMC) simulations in the isobaric-isothermal version of the Gibbs ensemble (GE) were carried out to probe the adsorption from aqueous solutions of methanol and/or ethanol onto silicalite-1. This methodology does require neither specification of the chemical potential nor any reference to activity models based on experimental data. The CBMC-GE methodology can be applied to the complete range of mixture compositions from pure water to pure alcohols and can also be used when multiple solute types are present at high concentration. The simulations demonstrate high selectivities for the alcohols (α(ethanol) > α(methanol)) almost over the entire composition range. The ideal adsorbed solution theory is found to substantially underpredict the amount of sorbed water and leads to very large errors for low alcohol solution concentrations. The simulations indicate that, at lower loadings, the adsorbed alcohol molecules can serve as seeds for water adsorption but, at higher loadings, alcohols displace water molecules from their preferred region.

Accurate and precise determination of critical properties from Gibbs ensemble Monte Carlo simulations

Since the seminal paper by Panagiotopoulos [Mol. Phys. 61, 813 (1997)], the Gibbs ensemble Monte Carlo (GEMC) method has been the most popular particle-based simulation approach for the computation of vapor-liquid phase equilibria. However, the validity of GEMC simulations in the near-critical region has been questioned because rigorous finite-size scaling approaches cannot be applied to simulations with fluctuating volume. Valleau [Mol. Simul. 29, 627 (2003)] has argued that GEMC simulations would lead to a spurious overestimation of the critical temperature. More recently, Patel et al. [J. Chem. Phys. 134, 024101 (2011)] opined that the use of analytical tail corrections would be problematic in the near-critical region. To address these issues, we perform extensive GEMC simulations for Lennard-Jones particles in the near-critical region varying the system size, the overall system density, and the cutoff distance. For a system with N = 5500 particles, potential truncation at 8σ and analytical tail corrections, an extrapolation of GEMC simulation data at temperatures in the range from 1.27 to 1.305 yields T(c) = 1.3128 ± 0.0016, ρ(c) = 0.316 ± 0.004, and p(c) = 0.1274 ± 0.0013 in excellent agreement with the thermodynamic limit determined by Potoff and Panagiotopoulos [J. Chem. Phys. 109, 10914 (1998)] using grand canonical Monte Carlo simulations and finite-size scaling. Critical properties estimated using GEMC simulations with different overall system densities (0.296 ≤ ρ(t) ≤ 0.336) agree to within the statistical uncertainties. For simulations with tail corrections, data obtained using r(cut) = 3.5σ yield T(c) and p(c) that are higher by 0.2% and 1.4% than simulations with r(cut) = 5 and 8σ but still with overlapping 95% confidence intervals. In contrast, GEMC simulations with a truncated and shifted potential show that r(cut) = 8σ is insufficient to obtain accurate results. Additional GEMC simulations for hard-core square-well particles with various ranges of the attractive well and for n-decane molecules represented by the TraPPE force field yield data that support the trends observed for Lennard-Jones particles. The finite-size dependence of the critical properties obtained from GEMC simulations is significantly smaller than those from grand-canonical ensemble simulations. Thus, when resources are not available for a rigorous finite-size scaling study, GEMC simulations provide a straightforward route to determine fairly accurate critical properties using relatively small system sizes.

Adsorptive Separation of 1-Butanol from Aqueous Solutions Using MFI- and FER-Type Zeolite Frameworks: A Monte Carlo Study

Anaerobic fermentation can transform carbohydrates to yield a multicomponent mixture comprising mainly of acetone, 1-butanol, and ethanol (ABE) in a typical weight ratio of 3:6:1. Compared to ethanol, 1-butanol, the main product of ABE fermentation, offers significant advantages as a biofuel or a fuel additive. However, the toxicity of 1-butanol for cell cultures requires broth concentrations to be low in 1-butanol (≈1-2 wt %). An energy-efficient recovery method that performs well even at low 1-butanol concentrations is therefore necessary to ensure economic feasibility of the ABE fermentation process. In this work, configurational-bias Monte Carlo simulations in the Gibbs ensemble are performed to probe the adsorption of 1-butanol/water solutions onto all-siliceous zeolites with the framework types MFI and FER. At low solution concentration, the selectivity and capacity for 1-butanol in MFI are larger than those in FER, while the opposite is true for concentrations at or above those of ABE broths. Structural analysis at various loadings sheds light on the different sorbate-sorbate and sorbate-sorbent interactions that govern trends in adsorption in each zeolite.

Understanding Diffusion in Hierarchical Zeolites with House-of-Cards Nanosheets

Introducing mesoporosity to conventional microporous sorbents or catalysts is often proposed as a solution to enhance their mass transport rates. Here, we show that diffusion in these hierarchical materials is more complex and exhibits non-monotonic dependence on sorbate loading. Our atomistic simulations of n-hexane in a model system containing microporous nanosheets and mesopore channels indicate that diffusivity can be smaller than in a conventional zeolite with the same micropore structure, and this observation holds true even if we confine the analysis to molecules completely inside the microporous nanosheets. Only at high sorbate loadings or elevated temperatures, when the mesopores begin to be sufficiently populated, does the overall diffusion in the hierarchical material exceed that in conventional microporous zeolites. Our model system is free of structural defects, such as pore blocking or surface disorder, that are typically invoked to explain slower-than-expected diffusion phenomena in experimental measurements. Examination of free energy profiles and visualization of molecular diffusion pathways demonstrates that the large free energy cost (mostly enthalpic in origin) for escaping from the microporous region into the mesopores leads to more tortuous diffusion paths and causes this unusual transport behavior in hierarchical nanoporous materials. This knowledge allows us to re-examine zero-length-column chromatography data and show that these experimental measurements are consistent with the simulation data when the crystallite size instead of the nanosheet thickness is used for the nominal diffusional length.

Understanding the Unusual Adsorption Behavior in Hierarchical Zeolite Nanosheets

Hierarchical zeolites are advanced materials possessing the catalytic and adsorption properties of conventional zeolites while eliminating their transport limitations through the introduction of mesopores. Recent experiments comparing the adsorption in hierarchical self-pillared pentasils (SPP) and silicalite-1 (MFI) revealed an interesting crossover in sorbate loading for branched or long-chain alkanes but not for shorter linear alkanes, but an explanation for this behavior is not readily available through experimental probes due to the complications arising from the presence of multiple adsorption sites. Here we present a molecular simulation study on the adsorption of alkane isomers and show that a multi-step mechanism, found here for all molecules, is responsible for the observed phenomena.

Solvent-enabled control of reactivity for liquid-phase reactions of biomass-derived compounds

The use of organic solvents in biomass conversion reactions can lead to high rates and improved selectivities. Here, we elucidate the effects of organic solvent mixtures with water on the kinetics of acid-catalysed dehydration reactions of relevance to biomass conversion. Based on results from reaction kinetics studies, combined with classical and ab initio molecular dynamics simulations, we show that the rates of acid-catalysed reactions in the liquid phase can be enhanced by altering the extents of solvation of the initial and transition states of these catalytic processes. The extent of these effects increases as the number of vicinal hydroxyl or oxygen-containing groups in the reactant increases, moving from an alcohol (butanol), to a diol (1,2-propanediol), to a carbohydrate (fructose). We demonstrate that the understanding of these solvation effects can be employed to optimize the rate and selectivity for production of the biomass platform molecule hydroxymethylfurfural from fructose.The choice of solvent system has important implications regarding the catalytic upgrading of carbohydrate-containing biomass. Here, Dumesic and co-workers study solvation effects in organic solvent/water mixtures and employ the obtained information to control the rate and selectivity of the acid-catalysed dehydration of fructose.

Assessment and Optimization of Configurational-Bias Monte Carlo Particle Swap Strategies for Simulations of Water in the Gibbs Ensemble

Particle swap moves between phases are usually the rate-limiting step for Gibbs ensemble Monte Carlo (GEMC) simulations of fluid phase equilibria at low reduced temperatures because the acceptance probabilities for these moves can become very low for molecules with articulated architecture and/or highly directional interactions. The configurational-bias Monte Carlo (CBMC) technique can greatly increase the acceptance probabilities, but the efficiency of the CBMC algorithm is influenced by multiple parameters. In this work we assess the performance of different CBMC strategies for GEMC simulations using the SPC/E and TIP4P water models at 283, 343, and 473 K, demonstrate that much higher acceptance probabilities can be achieved than previously reported in the literature, and make recommendations for CBMC strategies leading to optimal efficiency.