ZIF-8 Vibrational Spectra: Peak Assignments and Defect Signals.

Zeolitic imidazolate framework (ZIF-8) is a promising material for gas separation applications. It also serves as a prototype for numerous ZIFs, including amorphous ones, with a broader range of possible applications, including sensors, catalysis, and lithography. It consists of zinc coordinated with 2-methylimidazolate (2mIm) and has been synthesized with methods ranging from liquid-phase to solvent-free synthesis, which aim to control its crystal size and shape, film thickness and microstructure, and incorporation into nanocomposites. Depending on the synthesis method and postsynthesis treatments, ZIF-8 materials may deviate from the nominal defect-free ZIF-8 crystal structure due to defects like missing 2mIm, missing zinc, and physically adsorbed 2mIm trapped in the ZIF-8 pores, which may alter its performance and stability. Infrared (IR) spectroscopy has been used to assess the presence of defects in ZIF-8 and related materials. However, conflicting interpretations by various authors persist in the literature. Here, we systematically investigate ZIF-8 vibrational spectra by combining experimental IR spectroscopy and first-principles molecular dynamics simulations, focusing on assigning peaks and elucidating the spectroscopic signals of putative defects present in the ZIF-8 material. We attempt to resolve conflicting assignments from the literature and to provide a comprehensive understanding of the vibrational spectra of ZIF-8 and its defect-induced variations, aiming toward more precise quality control and design of ZIF-8-based materials for emerging applications.

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials.

Using knowledge from statistical thermodynamics and crystallography, we develop an image-image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate-adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. Employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Self-Assembly of Unusually Stable Thermotropic Network Phases by Cellobiose-Based Guerbet Glycolipids.

Bicontinuous thermotropic liquid crystal (LC) materials, e.g., double gyroid (DG) phases, have garnered significant attention due to the potential utility of their 3D network structures in wide-ranging applications. However, the utility of these materials is significantly constrained by the lack of robust molecular design rules for shape-filling amphiphiles that spontaneously adopt the saddle curvatures required to access these useful supramolecular assemblies. Toward this aim, we synthesized anomerically pure Guerbet-type glycolipids bearing cellobiose head groups and branched alkyl tails and studied their thermotropic LC self-assembly. Using a combination of differential scanning calorimetry, polarized optical microscopy, and small-angle X-ray scattering, our studies demonstrate that Guerbet cellobiosides exhibit a strong propensity to self-assemble into DG morphologies over wide thermotropic phase windows. The stabilities of these assemblies sensitively depend on the branched alkyl tail structure and the anomeric configuration of the glycolipid in a previously unrecognized manner. Complementary molecular simulations furnish detailed insights into the observed self-assembly characteristics, thus unveiling molecular motifs that foster network phase self-assembly that will enable future designs and applications of network LC materials.

Large-scale molecular dynamics simulations of bubble collapse in water: Effects of system size, water model, and nitrogen.

Molecular dynamics simulations in the microcanonical ensemble are performed to study the collapse of a bubble in liquid water using the single-site mW and the four-site TIP4P/2005 water models. To study system size effects, simulations for pure water systems are performed using periodically replicated simulation boxes with linear dimensions, L, ranging from 32 to 512 nm with the largest systems containing 8.7 × 106 and 4.5 × 109 molecules for the TIP4P/2005 and mW water models, respectively. The computationally more efficient mW water model allows us to reach converging behavior when the bubble dynamics results are plotted in reduced units, and the limiting behavior can be obtained through linear extrapolation in L-1. Qualitative differences are observed between simulations with the mW and TIP4P/2005 water models, but they can be explained by the models' differences in predicted viscosity and surface tension. Although bubble collapse occurs on time scales of only hundreds of picoseconds, the system sizes used here are sufficiently large to obtain bubble dynamics consistent with the Rayleigh-Plesset equation when using the models' thermophysical properties as input. For the conditions explored here, extreme heating of the interfacial water molecules near the time of collapse is observed for the larger mW water systems (but the model underpredicts the viscosity), whereas heating is less pronounced for the TIP4P/2005 water systems because its larger viscosity contribution slows the collapse dynamics. The presence of nitrogen within the bubble only starts to affect bubble dynamics near the very end of the initial collapse, leading to an incomplete collapse and strong rebound for the mW water model. Although nitrogen is non-condensable at 300 K, it becomes highly compressed and reaches a liquid-like density near the collapse point. We find that the dissolution of nitrogen is much slower than the movement of the collapsing water front, and the re-expansion of the dense nitrogen droplet gives rise to bubble rebound. The incompatibility of the collapse and dissolution time scales should be considered for continuum-scale modeling of bubble dynamics. We also confirm that the diffusion coefficient for dissolved nitrogen is insensitive to pressure as the liquid transitions from a compressed to a stretched state.

Shaping the Water-Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models.

We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g-1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.

A Nanocavitation Approach to Understanding Water Capture, Water Release, and Framework Physical Stability in Hierarchically Porous MOFs.

Chemically stable metal-organic frameworks (MOFs) featuring interconnected hierarchical pores have proven to be promising for a remarkable variety of applications. Nevertheless, the framework's susceptibility to capillary-force-induced pore collapse, especially during water evacuation, has often limited practical applications. Methodologies capable of predicting the relative magnitudes of these forces as functions of the pore size, chemical composition of the pore walls, and fluid loading would be valuable for resolution of the pore collapse problem. Here, we report that a molecular simulation approach centered on evacuation-induced nanocavitation within fluids occupying MOF pores can yield the desired physical-force information. The computations can spatially pinpoint evacuation elements responsible for collapse and the chemical basis for mitigation of the collapse of modified pores. Experimental isotherms and difference-electron density measurements of the MOF NU-1000 and four chemical variants validate the computational approach and corroborate predictions regarding relative stability, anomalous sequence of pore-filling, and chemical basis for mitigation of destructive forces.

Molecular Modeling of Double Retrograde Vaporization Using Monte Carlo Simulations and Equations of State.

Vapor-liquid equilibria of binary systems consisting of a low-boiling (i.e., more volatile) and a high-boiling compound may exhibit unexpected behavior near the critical point of the low-boiling compound. Near the critical temperature of the low-boiling compound and for compositions rich in the low-boiling compound, increasing the pressure may result in multiple crossings of the dew- and bubble-point curves. This phenomenon is often called double retrograde vaporization (or condensation) and may play a role in oil field operations and gas transport through pipelines, but the microscopic driving forces for the unusual shape of the dew-point curve are not well understood. Monte Carlo simulations in the constant-pressure, constant-temperature Gibbs ensemble using the united-atom version of the TraPPE force field were carried out for the methane/n-butane mixture at temperatures ranging from 0.95 to 1.05 of the reduced (T/Tc) temperature of methane. The simulations predict a wealth of additional thermodynamic data (densities and free energies of transfer) and structural data that are used to provide much needed molecular-level insights into the fluid properties associated with double retrograde vaporization. Simulated thermodynamic data are also compared with calculations using the Peng-Robinson and PC-SAFT equations of state.

MOF Linker Extension Strategy for Enhanced Atmospheric Water Harvesting.

A linker extension strategy for generating metal-organic frameworks (MOFs) with superior moisture-capturing properties is presented. Applying this design approach involving experiment and computation results in MOF-LA2-1 {[Al(OH)(PZVDC)], where PZVDC2- is (E)-5-(2-carboxylatovinyl)-1H-pyrazole-3-carboxylate}, which exhibits an approximately 50% water capacity increase compared to the state-of-the-art water-harvesting material MOF-303. The power of this approach is the increase in pore volume while retaining the ability of the MOF to harvest water in arid environments under long-term uptake and release cycling, as well as affording a reduction in regeneration heat and temperature. Density functional theory calculations and Monte Carlo simulations give detailed insight pertaining to framework structure, water interactions within its pores, and the resulting water sorption isotherm.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials.

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Influence of 1-Butene Adsorption on the Dimerization Activity of Single Metal Cations on UiO-66 Nodes.

Grafting metal cations to missing linker defect sites in zirconium-based metal-organic frameworks, such as UiO-66, produces a uniquely well-defined and homotopic catalytically active site. We present here the synthesis and characterization of a group of UiO-66-supported metal catalysts, M-UiO-66 (M = Ni, Co, Cu, and Cr), for the catalytic dimerization of alkenes. The hydrogen-deuterium exchange via deuterium oxide adsorption followed by infrared spectroscopy showed that the last molecular water ligand desorbs from the sites after evacuation at 300 °C leading to M(OH)-UiO-66 structures. Adsorption of 1-butene is studied using calorimetry and density functional theory techniques to characterize the interactions of the alkene with metal cation sites that are found active for alkene oligomerization. For the most active Ni-UiO-66, the removal of molecular water from the active site significantly increases the 1-butene adsorption enthalpy and almost doubles the catalytic activity for 1-butene dimerization in comparison to the presence of water ligands. Other M-UiO-66 (M = Co, Cu, and Cr) exhibit 1-3 orders of magnitude lower catalytic activities compared to Ni-UiO-66. The catalytic activities correlate linearly with the Gibbs free energy of 1-butene adsorption. Density functional theory calculations probing the Cossee-Arlman mechanism for all metals support the differences in activity, providing a molecular level understanding of the metal site as the active center for 1-butene dimerization.

Stabilizing a Double Gyroid Network Phase with 2 nm Feature Size by Blending of Lamellar and Cylindrical Forming Block Oligomers.

Molecular dynamics simulations are used to study binary blends of an AB-type diblock and an AB2-type miktoarm triblock amphiphiles (also known as high-χ block oligomers) consisting of sugar-based (A) and hydrocarbon (B) blocks. In their pure form, the AB diblock and AB2 triblock amphiphiles self-assemble into ordered lamellar (LAM) and cylindrical (CYL) structures, respectively. At intermediate compositions, however, the AB2-rich blend (0.2 ≤ x AB ≤ 0.4) forms a double gyroid (DG) network, whereas perforated lamellae (PL) are observed in the AB-rich blend (0.5 ≤ x AB ≤ 0.8). All of the ordered mesophases present domain pitches under 3 nm, with 1 nm feature sizes for the polar domains. Structural analyses reveal that the nonuniform interfacial curvatures of DG and PL structures are supported by local composition variations of the LAM- and CYL-forming amphiphiles. Self-consistent mean field theory calculations for blends of related AB and AB2 block polymers also show the DG network at intermediate compositions, when A is the minority block, but PL is not stable. This work provides molecular-level insights into how blending of shape-filling molecular architectures enables network phase formation with extremely small feature sizes over a wide composition range.

Nonane and Hexanol Adsorption in the Lamellar Phase of a Nonionic Surfactant: Molecular Simulations and Comparison to Ideal Adsorbed Solution Theory.

Adsorption of n-nonane/1-hexanol (C9/C6OH) mixtures into the lamellar phase formed by a 50/50 w/w triethylene glycol mono-n-decyl ether (C10E3)/water system was studied using configurational-bias Monte Carlo simulations in the osmotic Gibbs ensemble. The interactions were described by the Shinoda-Devane-Klein coarse-grained force field. Prior simulations probing single-component adsorption indicated that C9 molecules preferentially load near the center of the bilayer, increasing the bilayer thickness, whereas C6OH molecules are more likely to be found near the interface of the polar and nonpolar moieties, swelling the bilayer in the lateral dimension. Here, we extend this work to binary C9/C6OH adsorption to probe whether the difference in the spatial preferences may lead to a synergistic effect and enhanced loadings for the mixture. Comparing loading trends and the thermodynamics of binary adsorption to unary adsorption reveals that C9-C9 interactions lead to the largest enhancement, whereas C9-C6OH and C6OH-C6OH interactions are less favorable for this bilayer system. Ideal adsorbed solution theory yields satisfactory predictions of the binary loading.

Ethanol and Water Adsorption in Conventional and Hierarchical All-Silica MFI Zeolites.

Hierarchical zeolites containing both micro- (<2 nm) and mesopores (2-50 nm) have gained increasing attention in recent years because they combine the intrinsic properties of conventional zeolites with enhanced mass transport rates due to the presence of mesopores. The structure of the hierarchical self-pillared pentasil (SPP) zeolite is of interest because all-silica SPP consists of orthogonally intergrown single-unit-cell MFI nanosheets and contains hydrophilic surface silanol groups on the mesopore surface while its micropores are nominally hydrophobic. Therefore, the distribution of adsorbed polar molecules, like water and ethanol, in the meso- and micropores is of fundamental interest. Here, molecular simulation and experiment are used to investigate the adsorption of water and ethanol on SPP. Vapor-phase single-component adsorption shows that water occupies preferentially the mesopore corner and surface regions of the SPP material at lower pressures (P/P 0 < 0.5) while loading in the mesopore interior dominates adsorption at higher pressures. In contrast, ethanol does not exhibit a marked preference for micro- or mesopores at low pressures. Liquid-phase adsorption from binary water-ethanol mixtures demonstrates a 2 orders of magnitude lower ethanol/water selectivity for the SPP material compared to bulk MFI. For very dilute aqueous solutions of ethanol, the ethanol molecules are mostly adsorbed inside the SPP micropore region due to stronger dispersion interactions and the competition from water for the surface silanols. At high ethanol concentrations (C EtOH > 700 g L-1), the SPP material becomes selective for water over ethanol.

Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures.

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning.

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.

Development of a PointNet for Detecting Morphologies of Self-Assembled Block Oligomers in Atomistic Simulations.

Molecular simulations with atomistic or coarse-grained force fields are a powerful approach for understanding and predicting the self-assembly phase behavior of complex molecules. Amphiphiles, block oligomers, and block polymers can form mesophases with different ordered morphologies describing the spatial distribution of the blocks, but entirely amorphous nature for local packing and chain conformation. Screening block oligomer chemistry and architecture through molecular simulations to find promising candidates for functional materials is aided by effective and straightforward morphology identification techniques. Capturing 3-dimensional periodic structures, such as ordered network morphologies, is hampered by the requirement that the number of molecules in the simulated system and the shape of the periodic simulation box need to be commensurate with those of the resulting network phase. Common strategies for structure identification include structure factors and order parameters, but these fail to identify imperfect structures in simulations with incorrect system sizes. Building upon pioneering work by DeFever et al. [Chem. Sci. 2019, 10, 7503-7515] who implemented a PointNet (i.e., a neural network designed for computer vision applications using point clouds) to detect local structure in simulations of single-bead particles and water molecules, we present a PointNet for detection of nonlocal ordered morphologies of complex block oligomers. Our PointNet was trained using atomic coordinates from molecular dynamics simulation trajectories and synthetic point clouds for ordered network morphologies that were absent from previous simulations. In contrast to prior work on simple molecules, we observe that large point clouds with 1000 or more points are needed for the more complex block oligomers. The trained PointNet model achieves an accuracy as high as 0.99 for globally ordered morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star-block oligomers, and it also allows for the discovery of emerging ordered patterns from nonequilibrium systems.

From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains.

Using molecular dynamics simulations and transferable force fields, we designed a series of symmetric triblock amphiphiles (or high-χ block oligomers) comprising incompatible sugar-based (A) and hydrocarbon (B) blocks that can self-assemble into ordered nanostructures with sub-1 nm domains and full domain pitches as small as 1.2 nm. Depending on the chain length and block sequence, the ordered morphologies include lamellae, perforated lamellae, and hexagonally perforated lamellae. The self-assembly of these amphiphiles bears some similarities, but also some differences, to those formed by symmetric triblock polymers. In lamellae formed by ABA amphiphiles, the fraction of B blocks "bridging" adjacent polar domains is nearly unity, much higher than that found for symmetric triblock polymers, and the bridging molecules adopt elongated conformations. In contrast, "looping" conformations are prevalent for A blocks of BAB amphiphiles. Above the order-disorder transition temperature, the disordered states are locally well-segregated yet the B blocks of ABA amphiphiles are significantly less stretched than in the lamellar phases. Analysis of both hydrogen-bonded and nonpolar clusters reveals the bicontinuous nature of these network phases. This simulation study furnishes detailed insights into structure-property relationships for mesophase formation on the 1 nm length scale that will aid further miniaturization for numerous applications.

Temperature effects on the spatial distribution of electrolyte mixtures at the aqueous liquid-vapor interface.

The microscopic picture of ions at the aqueous liquid-vapor interface continues to be an important and active area of research. Both experiments and simulations have shown that certain ions, such as Br- and I-, prefer to adsorb at the interface, but there is not yet a consensus as to the relative importance of various ion-specific properties that influence surface solvation. In a previous study, we systematically explored the effect of ion size on determining whether or not a monovalent ion would adsorb at the surface, and found that, for electrolyte mixtures represented by non-polarizable models, the larger/smaller anions are enriched/depleted at the interface. Here, we extend that study to include temperature effects enabling a van't Hoff analysis of the enthalpic and entropic contributions. We perform configurational-bias Monte Carlo simulations in the Gibbs ensemble to investigate the partitioning of mixtures of differently sized ions at the aqueous liquid-vapor interface from 284.09 K to 347.22 K at a pressure of 1 atm. Ions are represented using our own previously developed models that vary only in size (i.e., the Lennard-Jones σ parameter changes, while all other parameters are held constant across ion types). System properties studied include surface tension, interfacial width, ion surface excess, number density profiles, z-dependent transfer free energy, enthalpy, entropy, and anion-cation coordination numbers.

One-dimensional intergrowths in two-dimensional zeolite nanosheets and their effect on ultra-selective transport.

Zeolite MFI is a widely used catalyst and adsorbent that also holds promise as a thin-film membrane. The discovery of nanometre-thick two-dimensional (2D) MFI nanosheets has facilitated methods for thin-film zeolite fabrication that open new horizons for membrane science and engineering. However, the crystal structure of 2D-MFI nanosheets and their relationship to separation performance remain elusive. Using transmission electron microscopy, we find that one- to few-unit-cell-wide intergrowths of zeolite MEL exist within 2D-MFI. We identify the planar distribution of these 1D or near-1D-MEL domains, and show that a fraction of nanosheets have high (~25% by volume) MEL content while the majority of nanosheets are MEL-free. Atomistic simulations show that commensurate knitting of 1D-MEL within 2D-MFI creates more rigid and highly selective pores compared to pristine MFI nanosheets, and permeation experiments show a separation factor of 60 using an industrially relevant (undiluted 1 bar xylene mixture) feed. Confined growth in graphite is shown to increase the MEL content in MFI nanosheets. Our observation of these intergrowths suggests strategies for the development of ultra-selective zeolite membranes.

Synthesis, Simulation, and Self-Assembly of a Model Amphiphile To Push the Limits of Block Polymer Nanopatterning.

Efforts to create block-polymer-based templates with ultrasmall domain sizes has stimulated integrated experimental and theoretical work in an effort to design and prepare self-assembled systems that can achieve unprecedented domain sizes. We recently reported the utilization of molecular dynamics simulations with transferable force fields to identify amphiphilic oligomers capable of self-assembling into ordered layered and cylindrical morphologies with sub-3 nm domain sizes. Motivated by these predictions, we prepared a sugar-based amphiphile with a hydrocarbon tail that shows thermotropic self-assembly to give a lamellar mesophase with a 3.5 nm pitch and sub-2 nm nanodomains above the melting temperature and below the liquid-crystalline clearing temperature. Complementary atomistic simulations of the molecular assemblies gave morphologies and spacings that were in near-perfect agreement with the experimental results. The effective combination of molecular design, simulation, synthesis, and structural characterization demonstrates the power of this integrated approach for next-generation templating technologies.