Pervaporation Separation of Isopropanol/Water Using Zeolite Nanosheets: A Molecular Simulation Study.

Reducing greenhouse gas emissions plays a crucial role in slowing down the rise of the global temperature. One of the viable options is to employ renewable energy sources such as alcohols that can be produced from biomass. Specifically, one of the most common alcohols is isopropanol (IPA). Energy-intensive distillation processes are however involved in its production because of the rather low product concentration from fermentation. Membrane technologies, specifically pervaporation (PV), represent a promising alternative to the IPA/water separation. Particularly, employing zeolite nanosheets as PV membranes may provide great opportunities to extract IPA owing to their ultrathin and hydrophobic nature. By employing molecular dynamics simulations, this study conducts a systematic study on a diverse set of nanosheet candidates with the aim of exploring their potential and identifying top-performing structures. The best candidate among structures studied herein is predicted to offer an exceptional IPA/water selectivity of more than 400 with an unprecedentedly large flux. Structure-property-performance relationships have also been established to offer insights into the rational design of PV membranes with improved performance.

Adsorption-driven reverse osmosis separation of ethanol/water using zeolite nanosheets.

Developing more energy-efficient and cost-effective membrane processes for the separation of ethanol and water represents a strategically important direction to facilitate the production of renewable biofuels. In this study, by employing state-of-the-art molecular simulations, the potential of zeolite nanosheets as reverse osmosis (RO) membranes in ethanol/water separation is investigated. These materials are predicted to offer unprecedentedly high fluxes and more importantly, the ethanol-to-water separation factor can be as large as approximately 800 if the structure is meticulously selected. The separation achieved herein can in fact be considered counter-intuitive as the membrane allows the larger ethanol molecules to permeate through while blocking smaller water molecules. Further investigations reveal that the observed selectivity is strongly correlated with the adsorption selectivity of the bulk materials, suggesting an adsorption-driven mechanism. Promising candidates also appear to have the largest cavity diameter of approximately 6 Å, a size that can be commensurate with the dimensions of ethanol to facilitate its adsorption. The hydrophilicity on the membrane surfaces is as well found to play a non-negligible role. Overall, this study demonstrates the great promise of zeolite nanosheets as RO membranes for extracting anhydrous ethanol from its aqueous mixture and provides guidance toward the selection of promising membrane candidates.

Tailoring parameters for QM/MM simulations: accurate modeling of adsorption and catalysis in zirconium-based metal-organic frameworks.

Quantum mechanics/molecular mechanics (QM/MM) simulations offer an efficient way to model reactions occurring in complex environments. This study introduces a specialized set of charge and Lennard-Jones parameters tailored for electrostatically embedded QM/MM calculations, aiming to accurately model both adsorption processes and catalytic reactions in zirconium-based metal-organic frameworks (Zr-MOFs). To validate our approach, we compare adsorption energies derived from QM/MM simulations against experimental results and Monte Carlo simulation outcomes. The developed parameters showcase the ability of QM/MM simulations to represent long-range electrostatic and van der Waals interactions faithfully. This capability is evidenced by the prediction of adsorption energies with a low root mean square error of 1.1 kcal mol-1 across a wide range of adsorbates. The practical applicability of our QM/MM model is further illustrated through the study of glucose isomerization and epimerization reactions catalyzed by two structurally distinct Zr-MOF catalysts, UiO-66 and MOF-808. Our QM/MM calculations closely align with experimental activation energies. Importantly, the parameter set introduced here is compatible with the widely used universal force field (UFF). Moreover, we thoroughly explore how the size of the cluster model and the choice of density functional theory (DFT) methodologies influence the simulation outcomes. This work provides an accurate and computationally efficient framework for modeling complex catalytic reactions within Zr-MOFs, contributing valuable insights into their mechanistic behaviors and facilitating further advancements in this dynamic area of research.

Understanding Carbon Monoxide Binding and Interactions in M-MOF-74 (M = Mg, Mn, Ni, Zn).

Small molecules may adsorb strongly in metal-organic frameworks (MOFs) through interactions with under-coordinated open metal sites (OMS) that often exist within these structures. Among adsorbates, CO is attractive to study both for its relevance in energy-related applications and for its ability to engage in both σ-donation and π-backbonding interactions with the OMS in MOFs. Concomitant with strong adsorption, structural changes arise due to modifications of the electronic structure of both the adsorbate and adsorbent. These structural changes affect the separation performance of materials, and accurately capturing these changes and the resulting energetics is critical for accurate predictive modeling of adsorption. Traditional approaches to modeling using classical force fields typically do not capture or account for changes at the electronic level. To characterize the structural and energetic effects of the local structural changes, we employed density functional theory (DFT) to study CO adsorption in M-MOF-74s. M-MOF-74s feature OMS at which CO is known to adsorb strongly and can be synthesized with a variety of divalent metal cations with distinct performance in adsorption. We considered M-MOF-74s with a range of metals of varied d-band occupations (Mg (3d0), Mn (3d5), Ni (3d8), and Zn (3d10)) with various structural constraints ranging from geometrically constrained adsorbent and adsorbate ions to fully optimized geometries to deconvolute the relative contributions of various structural effects to the adsorption energetics and binding distances observed. Our data indicate that the most significant structural changes during adsorption correlate with the greatest π-backbonding behaviors and commensurately result in a sizable binding energy change observed for CO adsorption. The insights built from this work are relevant to two longstanding research challenges within the MOF community: rational design of materials for separations and the design of force fields capable of accurately modeling adsorption.

Asymmetric nanoporous membranes for ethanol/water pervaporation separation and their design.

To explore the design of pervaporation membranes for ethanol recovery, zeolite nanosheets with different surface characteristics on the feed and permeate sides are investigated via molecular dynamics simulations. The results demonstrate the significant role of the permeate-side surface in the separation performance. By adopting an asymmetric membrane design with a hydrophobic feed-side surface and a hydrophilic one on the permeate side, the separation factor can be enhanced by nearly three-fold as compared to that of both hydrophobic surfaces, with an improved permeation flux.

Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods.

Mixture adsorption properties of porous materials are critical to determine their potential as adsorbents in separation applications. Toward the discovery of optimal adsorbents, in silico screening studies typically employ the grand canonical Monte Carlo (GCMC) technique to compute adsorption properties of gas mixtures in materials of interest at a given condition (i.e., composition, total pressure, and temperature) or to compute their adsorption properties for each component, followed by utilizing methods to predict mixture adsorption isotherms. However, the former approach results in the need for repeated calculations when different conditions such as compositions are considered. For the latter, the predictions may involve uncertainties, sometimes originating from the fitting quality to the pure component isotherms, and repeated simulations may also be needed for different temperatures. To this end, this study demonstrates the potential of flat histogram Monte Carlo methods in addressing the abovementioned shortfalls. Specifically, the so-called NVT + W method, first reported by Smit and co-workers, is extended herein to determine the macrostate probability distribution (MPD) of binary mixtures in porous materials. The obtained MPD can be reweighted to any conditions, yielding accurate adsorption isotherms of any desired compositions and temperatures. This approach, denoted as 2D NVT + W, is also compared with the widely adopted ideal adsorbed solution theory (IAST) method, and the former is found to offer more reliable predictions. Overall, the 2D NVT + W approach represents an efficient and effective alternative to compute mixture adsorption isotherms for porous materials, and the obtained MPD can be conveniently reused by peer researchers. A user-friendly Python code is also provided along with this article to employ this method.

Connectivity Analysis of Adsorption Sites in Metal-Organic Frameworks for Facilitated Water Adsorption.

Metal-organic frameworks (MOFs) have recently drawn considerable attention as promising adsorbents to harvest atmospheric water. To achieve an efficient harvesting process, seeking MOFs that demonstrate sharp condensation behavior is the key. Given that the clustering of water molecules in MOFs should be driven by not only MOF-water interactions but also water-water interactions, the spatial arrangement of water adsorption sites in a MOF is therefore crucial. Specifically, this study demonstrates the critical role of continuous adsorption channels (CACs) in MOFs. Such CACs will enable water molecules to stay in proximity and in a continuous manner, thus promoting the formation of hydrogen bonds and, consequently, the clustering of water molecules. We have developed an automatic algorithm to detect CACs based on the energy grid of host-guest interactions and applied the algorithm to more than 2000 diverse structures. The results show that more than 80% of the studied MOFs displaying water condensation at 298 K and 20% relative humidity predicted by Monte Carlo simulations indeed have CACs. The developments herein are anticipated to largely facilitate the future discovery of optimal adsorbents for water harvesting or water-adsorption-related applications in general. A Python-based code for detecting CACs in porous materials is also provided along with this article to employ this approach.

Understanding Rapid PET Degradation via Reactive Molecular Dynamics Simulation and Kinetic Modeling.

As the demand for PET plastic products continues to grow, developing effective processes to reduce their pollution is of critical importance. Pyrolysis, a promising technology to produce lighter and recyclable components from wasted plastic products, has therefore received considerable attention. In this work, the rapid pyrolysis of PET was studied by using reactive molecular dynamics (MD) simulations. Mechanisms for yielding gas species were unraveled, which involve the generation of ethylene and TPA radicals from ester oxygen-alkyl carbon bond dissociation and condensation reactions to consume TPA radicals with the products of long chains containing a phenyl benzoate structure and CO2. As atomistic simulations are typically conducted at the time scale of a few nanoseconds, a high temperature (i.e., >1000 K) is adopted for accelerated reaction events. To apply the results from MD simulations to practical pyrolysis processes, a kinetic model based on a set of ordinary differential equations was established, which is capable of describing the key products of PET pyrolysis as a function of time and temperature. It was further exploited to determine the optimal reaction conditions for low environmental impact. Overall, this study conducted a detailed mechanism study of PET pyrolysis and established an effective kinetic model for the main species. The approach presented herein to extract kinetic information such as detailed kinetic constants and activation energies from atomistic MD simulations can also be applied to related systems such as the pyrolysis of other polymers.

Inverse CO2 /C2 H2 Separation with MFU-4 and Selectivity Reversal via Postsynthetic Ligand Exchange.

Although many porous materials, including metal-organic frameworks (MOFs), have been reported to selectively adsorb C2 H2 in C2 H2 /CO2 separation processes, CO2 -selective sorbents are much less common. Here, we report the remarkable performance of MFU-4 (Zn5 Cl4 (bbta)3 , bbta=benzo-1,2,4,5-bistriazolate) toward inverse CO2 /C2 H2 separation. The MOF facilitates kinetic separation of CO2 from C2 H2 , enabling the generation of high purity C2 H2 (>98 %) with good productivity in dynamic breakthrough experiments. Adsorption kinetics measurements and computational studies show C2 H2 is excluded from MFU-4 by narrow pore windows formed by Zn-Cl groups. Postsynthetic F- /Cl- ligand exchange was used to synthesize an analogue (MFU-4-F) with expanded pore apertures, resulting in equilibrium C2 H2 /CO2 separation with reversed selectivity compared to MFU-4. MFU-4-F also exhibits a remarkably high C2 H2 adsorption capacity (6.7 mmol g-1 ), allowing fuel grade C2 H2 (98 % purity) to be harvested from C2 H2 /CO2 mixtures by room temperature desorption.

Brunauer-Emmett-Teller Areas from Nitrogen and Argon Isotherms Are Similar.

Despite recommendations from the 2015 International Union of Pure and Applied Chemistry (IUPAC) technical report, surface areas of porous materials continue to be characterized by an N2 adsorption isotherm using the Brunauer-Emmett-Teller (BET) method. In this study, we provide the basis for such a practice by carrying out systematic large-scale molecular simulations on homogeneous and heterogeneous model surfaces. Specifically, we investigated the purported "orientational effect" of the N2 molecule on these surfaces. Grand canonical Monte Carlo (GCMC) simulation results from 257 diverse metal-organic frameworks show that the BET areas from Ar and N2 are similar in the range of 250-7500 m2/g with a mean deviation of 4%. Detailed analyses based on the consistency criteria for BET equations reveal that the large deviation (>10%) between the BET areas from Ar and N2 are materials specific and more prone to materials that are not able to satisfy the 3 and 4 consistency criteria. For materials that satisfy all four consistency criteria, the BET areas predicted from Ar and N2 isotherms are comparable.

X-ray Diffraction and Molecular Simulations in the Study of Metal-Organic Frameworks for Membrane Gas Separation.

For more than a decade, researchers have been developing metal-organic frameworks (MOFs) in the form of pure MOF membranes as well as MOF-containing mixed-matrix membranes. MOF membranes have been used for H2/CO2 or C3H6/C3H8 separation, but relatively few MOF membranes enable the high-performance separation of CO2/N2, CO2/CH4, or N2/CH4. This article describes the use of in situ XRD analysis and molecular simulation to elucidate gas transport within MOFs and derivative membranes at the molecular level. In a review of recent studies by the authors and other research groups, this article examines the flexibility of MOFs initiated by activation, gas adsorption, and aging effects during gas permeation. This article also discusses the application of XRD analysis in conjunction with computational methods to investigate the CO2-MOF Coulombic interaction and its effects on CO2 separation. Note that this combined analysis approach is also useful in studying the effects of linker rotation on N2/CH4 separation. This article also examines the use of computational tools in identifying new MOFs for gas separation and, more importantly, in elaborating the relationship between the structure of MOFs and their corresponding gas transport properties.

Computational Study of Alkane Adsorption in Brønsted Acid Zeolites for More Efficient Alkane Cracking.

Alkane cracking using Brønsted acid zeolites, catalytically converting long-chain molecules into smaller ones, is critical to fuel and chemical production. To enable more energy-efficient cracking processes, developing zeolite catalysts with enhanced performance (i.e., a faster reaction rate with reduced methane formation) plays a substantial role. Given the adsorption thermodynamics of alkanes onto the protons of Brønsted acid zeolites is a key step in the overall cracking reactions; therefore, catalysts possessing a more negative Gibbs free energy of adsorption for alkanes with a larger central-to-terminal bond adsorption selectivity to promote central cracking are of particular interest. This Feature Article discusses recent computational developments and discoveries by Lin and co-workers in studying the adsorption of alkanes in Brønsted acid zeolites. Their developed approach, employing configurational bias Monte Carlo with domain decomposition, with a newly parametrized molecular potential to compute the adsorption properties is first introduced. With these developments, the roles of the Si/Al ratio and Al sitting are explored and discussed. Subsequently, the Feature Article discusses the key findings obtained from a large-scale computational screening of studying more than 100 000 possible zeolite structures. The performance of identified top candidates and associated key structural features leading to desirable adsorption properties are highlighted.

Superhydrophobic Carbon Nanotube Network Membranes for Membrane Distillation: High-Throughput Performance and Transport Mechanism.

Despite increasing sustainable water purification, current desalination membranes still suffer from insufficient permeability and treatment efficiency, greatly hindering extensive practical applications. In this work, we provide a new membrane design protocol and molecule-level mechanistic understanding of vapor transport for the treatment of hypersaline waters via a membrane distillation process by rationally fabricating more robust metal-based carbon nanotube (CNT) network membranes, featuring a superhydrophobic superporous surface (80.0 ± 2.3% surface porosity). With highly permeable ductile metal hollow fibers as substrates, the construction of a superhydrophobic (water contact angle ∼170°) CNT network layer endows the membranes with not only almost perfect salt rejection (over 99.9%) but a promising water flux (43.6 L·m-2·h-1), which outperforms most existing inorganic distillation membranes. Both experimental and molecular dynamics simulation results indicate that such an enhanced water flux can be ascribed to an ultra-low liquid-solid contact interface (∼3.23%), allowing water vapor to rapidly transport across the membrane structure via a combined mechanism of Knudsen diffusion (more dominant) and viscous flow while efficiently repelling high-salinity feed via forming a Cassie-Baxter state. A more hydrophobic surface is more in favor of not only water desorption from the CNT outer surface but superfast and frictionless water vapor transport. By constructing a new superhydrophobic triple-phase interface, the conceptional design strategy proposed in this work can be expected to be extended to other membrane material systems as well as more water treatment applications.

Robust ultrathin nanoporous MOF membrane with intra-crystalline defects for fast water transport.

Rational design of high-performance stable metal-organic framework (MOF) membranes is challenging, especially for the sustainable treatment of hypersaline waters to address critical global environmental issues. Herein, a molecular-level intra-crystalline defect strategy combined with a selective layer thinning protocol is proposed to fabricate robust ultrathin missing-linker UiO-66 (ML-UiO-66) membrane to enable fast water permeation. Besides almost complete salt rejection, high and stable water flux is achieved even under long-term pervaporation operation in hash environments, which effectively addresses challenging stability issues. Then, detailed structural characterizations are employed to identify the type, chemical functionality, and density of intra-crystalline missing-linker defects. Moreover, molecular dynamics simulations shed light on the positive atomistic role of these defects, which are responsible for substantially enhancing structural hydrophilicity and enlarging pore window, consequently allowing ultra-fast water transport via a lower-energy-barrier pathway across three-dimensional sub-nanochannels during pervaporation. Unlike common unfavorable defect effects, the present positive intra-crystalline defect engineering concept at the molecular level is expected to pave a promising way toward not only rational design of next-generation MOF membranes with enhanced permeation performance, but additional water treatment applications.

Suppressing Defect Formation in Metal-Organic Framework Membranes via Plasma-Assisted Synthesis for Gas Separations.

Metal-organic frameworks (MOFs) are considered as promising materials for membrane gas separations. Structural defects within a pure MOF membrane can considerably reduce its selectivity and possibly result in a nonselective separation. This work proposes a solution-phase synthesis with dielectric barrier discharge (DBD) plasma to suppress the formation of defects in the pure MOF membrane of CPO-8-BPY. Through comprehensive solid-state characterization with XRD, SEM, XPS, solid-state NMR, and XAFS, DBD plasma is demonstrated to facilitate deprotonation in the H2aip linker, which leads to a smaller and more uniform particle size of CPO-8-BPY. The narrow grain size distribution effectively reduces the pinhole-type defects in the pure CPO-8-BPY membrane and endows it with good ideal selectivity for H2/CH4 (αH2/CH4 = 28.2) and N2/CH4 (αN2/CH4 = 5.4). The selectivity for H2/CH4 of this membrane from a mixed-gas permeation test is found to be 15.4. Molecular simulations are also performed to gain insights into the gas transport properties of this MOF. The results suggest that ligand rotation plays an important role in CPO-8-BPY when being applied to the membrane separation of N2/CH4.

Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties.

Nanoporous materials can be effective adsorbents for various energy applications. Because of their abundant number, brute-force-based material discovery can, however, be challenging. Data-driven approaches can be advantageous for such purposes. In this study, we demonstrate for the first time the applicability of a 3D convolutional neural network (CNN) in material recognition for predicting adsorption properties. 2D CNNs have been widely applied to image recognition, where the CNN self-learns important features of images, without the need of handcrafting features that are subject to human bias. This study explores methane adsorption in zeolites as a case study, where ∼6500 hypothetical zeolites are utilized to train/validate our designed CNN model. The CNN model offers highly accurate predictions, and the self-learned features resemble the channel and pore-like geometry of structures. This study demonstrates the extension of computer vision to materials science and paves the way for future studies such as carbon capture.

Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method.

Surface areas of porous materials such as metal-organic frameworks (MOFs) are commonly characterized using the Brunauer-Emmett-Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials.

Hexagonal Superalignment of Nano-Objects with Tunable Separation in a Dilute and Spacer-Free Solution.

Manipulating building-block nanomaterials to form an ordered superstructure in a dilute and spacer-free solution phase challenges the existing 5-nm node lithography and nanorobotics. The cooperative nature of nanocrystals, polymers, and cells can lead to superarrays or colloidal crystals. For known highly ordered systems, the characteristic length of materials, defined as the shortest dimension of objects, is generally larger than their separations. A spacer (small-molecule surfactant or polymer) is typically required to diminish short range van der Waals attraction, which results in a glassy or liquid state. Herein we propose a new concept of achieving highly ordered nano-objects in a dilute and spacer-free system via the synergistic effects of excellent solvation and appropriate constraints on rotational motion. As a proof of concept, this study demonstrates that aluminosilicate nanotubes (AlSiNTs) suspended in water under dilute conditions (e.g., 1.0 wt%) can spontaneously form hexagonal arrays with an intertubular distance as large as tens of nanometers. The separation distance of the ordered superstructure is also tunable via controlling the concentration and length of nanotubes. These superaligned structures are probed using small-angle x-ray scattering and cryo-TEM characterizations, with underlying mechanisms investigated at an atomic level using molecular dynamics simulations. The concept and discovery of this work can open up opportunities to a variety of applications including visible-UV photonics and nanolithography, and may be generalizable to other nano-object systems that fulfill similar requirements.

Electrostatic Potential Optimized Molecular Models for Molecular Simulations: CO, CO2, COS, H2S, N2, N2O, and SO2.

Molecular simulations have been widely employed in the discovery of nanoporous materials, such as metal-organic frameworks (MOFs) and zeolite, for energy- and environment-related applications. To achieve simulation predictions with better accuracy, we herein present a collection of molecular models, including carbon monoxide (CO), carbon dioxide (CO2), carbonyl sulfide (COS), hydrogen sulfide (H2S), nitrogen (N2), nitrous oxide (N2O), and sulfur dioxide (SO2). These models, denoted as electrostatic potential optimized molecular models (ESP-MMs), are systematically developed to not only reproduce experimental vapor-liquid equilibrium but also have accurate electrostatic potential representation surrounding the molecules. Our results show that, with accurate electrostatic potential representations, ESP-MMs can offer improved predictions in a variety of adsorption properties for porous materials, including MOFs with open-metal sites and all-silica zeolites. Specifically, by using ESP-MMs, the binding geometry and adsorption energy landscape can be well captured. This enables these models to be employed to unravel the fundamental mechanism of gaseous adsorption in materials of interest as well as to facilitate the parametrization of adsorbent-adsorbate interactions. We also demonstrate that, combined with generic force fields for adsorbents, ESP-MMs can offer reasonable predictions in adsorption isotherms. Although these ESP-MMs use a relatively simple and nonpolarizable potential form for the sake of efficiency and applicability, their accuracy has been extensively validated in this study. Furthermore, the set of Lennard-Jones potentials with static point charges adopted for ESP-MMs can be readily implemented in all available simulation packages. We anticipate that these ESP-MMs can largely facilitate future computational studies of porous materials for gas separation and removal.

Response to "Impact of Zeolite Structure on Entropic-Enthalpic Contributions to Alkane Monomolecular Cracking: An IR Operando Study".

This is a response to the paper published by S. A. Kadam, H. Li, R. F. Wormsbacher, A. Travert, Chem. Eur. J. 2018, 24, 5489. Key consistencies between our reported results and those reported in this work are also highlighted.