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).

Configurational-bias Monte Carlo simulations in the Gibbs ensemble are used to study the thermodynamic and structural properties associated with the miscibility of binary olefin oligomer mixtures representing poly(ethylene-altpropylene), polypropylene, and head-to-head polypropylene. Single-component simulations are performed to compute the cohesive energy densities, Π CED , of different oligomers that are often utilized in estimating the miscibilities of compounds in the liquid phase but are not measurable for high-boiling compounds, such as polymers. Extrapolating simulation data for C5 to C36 oligomers allows for determination of the infinite-chain-length Π CED values of three polyolefins. The results agree remarkably well with values deduced from small-angle neutron scattering experiments on high-molecular-weight polymers. In addition, the Flory−Huggins χ parameters based on the free energy of mixing for pairs of olefins are calculated directly from simulations of binary mixtures. The binary propylene and head-to-head propylene oligomer blend is found to exhibit stabilized irregular mixing behavior, in agreement with its polymeric counterpart. This chain-length independence of the mixing behavior is interpreted via insights from structural analysis. Our results identify simulations of oligomeric systems as a promising route to predict and understand polymer blend phase behavior.

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper–cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.