A molecular simulation protocol for membrane pervaporation

Synergistic Computational and Experimental Investigation of Covalent Organic Frameworks for Efficient Alcohol Dehydration

Covalent organic frameworks (COFs), a promising class of nanoporous materials, have received significant attention for membrane separation. Currently, several COFs are reported for alcohol dehydration, but they are not efficient owing to the pervasive challenge to separate small-sized molecular mixture. Herein, first we have computationally explored a series of COFs with different functionality and aperture size as pervaporation (PV) membrane and identified a novel COF for efficient dehydration of water/alcohol mixtures (90 wt % IPA, 90 wt % n-butanol and 90 wt % t-butanol). Subsequently, the best-performing COF was experimentally synthesized and characterized, and its sorption properties were correlated with computational results. Molecular dynamics (MD) simulations revealed that solvent permeation fluxes are predominantly influenced by the pore aperture of COFs, and larger pore aperture exhibits higher flux. Conversely, the separation factor is primarily determined by the polarity of the pore functional groups. Among the tested COF membranes, TpPa-1-OC3H6OCH3 demonstrated superior performance, surpassing the current state-of-the-art membranes. The activation energy (Ea) for water permeation in alcohol mixtures through TpPa-1-OC3H6OCH3 is mostly governed by water-alcohol interactions. Furthermore, experimental evaluation of the COFs indicated a plate-like morphology for TpPa-1-OC3H6OCH3 which ascertained a 2D-sheet-like structure. TpPa-1 showed greater sorption than TpPa-1-OC3H6OCH3 with all of the solvents tested owing to the inability of the solvent molecules to enter the relatively small pores in the later COF. This is in accordance with the MD simulation predictions, which indicated that the solvent molecules cannot penetrate the small pores of TpPa-1-OC3H6OCH3. This work synergistically integrates computational and experimental approaches to develop novel COFs with superior performance compared to previously reported PV membranes, paving the way for advanced membranes for sustainable solvent recovery.

Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO₂ during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Molecular Simulation of Pervaporation on Polyurethane Membranes

This article discusses a molecular simulation of membrane processes for the separation of liquid mixtures during pervaporation. A method for simulating the structure of polyurethane membranes was developed. The method was based on the known mechanisms of the formation of macromolecules from constituent monomers. For the formation of a chemical bond between the monomers, values of the parameters of the potentials of intermolecular interactions were set so that bonds were formed only between the corresponding atoms. The algorithm was validated to produce polymer films from diphenylmethane diisocyanate (MDI) and amino ethers of boric acid (AEBA). The polymer film obtained according to the developed algorithm was used to study the adsorption of ethanol and water. The concentration distributions of the components inside the polymer film were obtained for films of various thicknesses. Modifications of the DCV-GCMD method were proposed for the molecular simulation of pervaporation. The algorithm was based on maintaining a constant density of the mixture in the control volume. After the molecules were added to the control volume, thermodynamic equilibrium was established. During this process, molecules moved only in the control volume, while the rest of the molecules were fixed. The proposed algorithm was used to calculate the flows of water and ethanol through the polymer film.

Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

Pervaporation (PV) is considered as a robust membrane-based separation technology for liquid mixtures. However, the development of PV membranes is impeded largely by the lack of adequate models capable of reliably predicting the performance of PV membranes. In this study, we collect an experimental data set with a total of 681 data samples including 16 polymers and 6 organic solvents for a wide variety of water/organic mixtures under various operating conditions. Then, two types of machine learning (ML) models are developed for prediction and high-throughput screening of polymer membranes for PV separation. Based on the intrinsic properties of polymer and solvent (water contact angle of polymer and solubility parameter of solvent) as gross descriptors, the first type accurately predicts PV separation performance (total flux and separation factor). The second type is based on the molecular representation of polymer and solvent, giving accuracy comparable to the first type, and applied to screen ∼1 million hypothetical polymers for PV separation of water/ethanol mixtures. With a threshold of 700 for the PV separation index, 20 polymers are shortlisted, with many surpassing experimental samples. Among these, 10 are further identified to be synthesizable in terms of a synthetic complexity score. The ML models developed in this study would facilitate the optimization of operating conditions and accelerate the development of new polymer membranes for high-performance PV separation.

Metal-Organic Frameworks for Liquid Phase Applications

In the last two decades, metal-organic frameworks (MOFs) have attracted overwhelming attention. With readily tunable structures and functionalities, MOFs offer an unprecedentedly vast degree of design flexibility from enormous number of inorganic and organic building blocks or via postsynthetic modification to produce functional nanoporous materials. A large extent of experimental and computational studies of MOFs have been focused on gas phase applications, particularly the storage of low-carbon footprint energy carriers and the separation of CO2-containing gas mixtures. With progressive success in the synthesis of water- and solvent-resistant MOFs over the past several years, the increasingly active exploration of MOFs has been witnessed for widespread liquid phase applications such as liquid fuel purification, aromatics separation, water treatment, solvent recovery, chemical sensing, chiral separation, drug delivery, biomolecule encapsulation and separation. At this juncture, the recent experimental and computational studies are summarized herein for these multifaceted liquid phase applications to demonstrate the rapid advance in this burgeoning field. The challenges and opportunities moving from laboratory scale towards practical applications are discussed.