From Crystal to Adsorption Column: Challenges in Multiscale Computational Screening of Materials for Adsorption Separation Processes

Leveraging Diffusion Kinetics to Reverse Propane/Propylene Adsorption in Zeolitic Imidazolate Framework-8

The separation challenge posed by propylene/propane mixtures arises from their nearly identical molecular sizes and physicochemical properties. Metal-organic frameworks (MOFs) have demonstrated potential in addressing this challenge through the precision tailoring of pore sizes and surface chemistry. However, introducing modifications at the molecular level remains a considerable hurdle. This work presents an approach to reversibly tune the propylene/propane adsorption preference in zeolitic imidazolate framework-8 (ZIF-8) by manipulating the particle size and gas flow rate. Systematically increasing the ZIF-8 crystals from 9 to 224 μm restricts propane diffusion, thereby reversing its preferential adsorption over propylene. Furthermore, raising the gas flow rate of mixed propylene/propane shifts the rate-determining breakthrough step from thermodynamic equilibrium to kinetics, again reversing the adsorption preference in a particular ZIF-8 sample. We propose "dynamic selectivity (Sd(t))" as a concept that incorporates both thermodynamic and kinetic factors to elucidate these unexpected findings. Moreover, the driving force equation, grounded on the concept of Sd(t), has improved the precision and stability of the computational simulation for fixed-bed adsorption processes. This work underscores the potential of diffusion-based modulation, implemented through manageable external changes, as a viable strategy to optimize separation performance in porous adsorbent materials.

Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation

CO2 concentration in the atmosphere has increased by about 40% since the 1960s. Among various technologies available for carbon capture, adsorption and membrane processes have been receiving tremendous attention due to their potential to capture CO2 at low costs. The kernel for such processes is the sorbent and membrane materials, and tremendous progress has been made in designing and fabricating novel porous materials for carbon capture. Covalent organic frameworks (COFs), a class of porous crystalline materials, are promising sorbents for CO2 capture due to their high surface area, low density, controllable pore size and structure, and preferable stabilities. However, the absence of synergistic developments between materials and engineering processes hinders achieving the qualitative leap for net-zero emissions. Considering the lack of a timely review on the combination of state-of-the-art COFs and engineering processes, in this Tutorial Review, we emphasize the developments of COFs for meeting the challenges of carbon capture and disclose the strategies of fabricating COFs for realizing industrial implementation. Moreover, this review presents a detailed and basic description of the engineering processes and industrial status of carbon capture. It highlights the importance of machine learning in integrating simulations of molecular and engineering levels. We aim to stimulate both academia and industry communities for joined efforts in bringing COFs to practical carbon capture.

How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

ConspectusAdsorption using solid sorbents is emerging as a serious contender to amine-based liquid absorption for postcombustion CO2 capture. In the last 20+ years, significant efforts have been invested in developing adsorption processes for CO2 capture. In particular, significant efforts have been invested in developing new adsorbents for this application. These efforts have led to the generation of hundreds of thousands of (hypothetical and real) adsorbents, e.g., zeolites and metal-organic frameworks (MOFs). Identifying the right adsorbent for CO2 capture remains a challenging task. Most studies are focused on identifying adsorbents based on certain adsorption metrics. Recent studies have demonstrated that the performance of an adsorbent is intimately linked to the process in which it is deployed. Any meaningful screening should thus consider the complexity of the process. However, simulation and optimization of adsorption processes are computationally intensive, as they constitute the simultaneous propagation of heat and mass transfer fronts; the process is cyclic, and there are no straightforward design tools, thereby making large-scale process-informed screening of sorbents prohibitive.This Account discusses four papers that develop computational methods to incorporate process-based evaluation for both bottom-up (chemistry to engineering) screening problems and top-down (engineering to chemistry) inverse problems. We discuss the development of the machine-assisted adsorption process learning and emulation (MAPLE) framework, a surrogate model based on deep artificial neural networks (ANNs) that can predict process-level performance by considering both process and material inputs. The framework, which has been experimentally validated, allows for reliable, process-informed screening of large adsorbent databases. We then discuss how process engineering tools can be used beyond adsorbent screening, i.e., to estimate the practically achievable performance and cost limits of pressure vacuum swing adsorption (PVSA) processes should the ideal bespoke adsorbent be made. These studies show what conditions stand-alone PVSA processes are attractive and when they should not be considered. Finally, recent developments in physics-informed neural networks (PINNS) enable the rapid solution of complex partial differential equations, providing tools to potentially identify optimal cycle configurations. Ultimately, we provide areas where further developments are required and emphasize the need for strong collaborations between chemists and chemical engineers to move rapidly from discovery to field trials, as we do not have much time to fulfill commitments to net-zero targets.

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO₂ and N₂ mixture isotherms using ideal adsorbed solution theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modeling. To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that, for the design of processes that capture CO₂ from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

Carbon dioxide separation and capture by adsorption: a review

Rising adverse impact of climate change caused by anthropogenic activities is calling for advanced methods to reduce carbon dioxide emissions. Here, we review adsorption technologies for carbon dioxide capture with focus on materials, techniques, and processes, additive manufacturing, direct air capture, machine learning, life cycle assessment, commercialization and scale-up.

A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO2 Capture from Diluted Sources

The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO₂ capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO₂ removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, y_CO2 = 0.1%, and y_CO2 = 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.

Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture

SO₂ presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal-organic frameworks (MOFs), cost-effective separation processes for SO₂ emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO₂/CH₄, SO₂/CO₂, and SO₂/N₂ separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO₂/CH₄ selectivities of 62.4-16899.7, SO₂ working capacities of 0.3-20.1 mol/kg, and SO₂ regenerabilities of 5.8-98.5%. Regarding SO₂/CO₂ separation, they possess SO₂/CO₂ selectivities of 13.3-367.2, SO₂ working capacities of 0.1-17.7 mol/kg, and SO₂ regenerabilities of 1.9-98.2%. For the SO₂/N₂ separation, their SO₂/N₂ selectivities, SO₂ working capacities, and SO₂ regenerabilities span the ranges of 137.9-67,338.9, 0.4-20.6 mol/kg, and 7.0-98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.

Computational Screening of Metal-Organic Frameworks for Ethylene Purification from Ethane/Ethylene/Acetylene Mixture

Identification of high-performing sorbent materials is the key step in developing energy-efficient adsorptive separation processes for ethylene production. In this work, a computational screening of metal-organic frameworks (MOFs) for the purification of ethylene from the ternary ethane/ethylene/acetylene mixture under thermodynamic equilibrium conditions is conducted. Modified evaluation metrics are proposed for an efficient description of the performance of MOFs for the ternary mixture separation. Two different separation schemes are proposed and potential MOF adsorbents are identified accordingly. Finally, the relationships between the MOF structural characteristics and its adsorption properties are discussed, which can provide valuable information for optimal MOF design.

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Characterization of Nanoporous Materials

Detailed analysis of textural properties, e.g., pore size and connectivity, of nanoporous materials is essential to identify correlations of these properties with the performance of gas storage, separation, and catalysis processes. The advances in developing nanoporous materials with uniform, tailor-made pore structures, including the introduction of hierarchical pore systems, offer huge potential for these applications. Within this context, major progress has been made in understanding the adsorption and phase behavior of confined fluids and consequently in physisorption characterization. This enables reliable pore size, volume, and network connectivity analysis using advanced, high-resolution experimental protocols coupled with advanced methods based on statistical mechanics, such as methods based on density functional theory and molecular simulation. If macro-pores are present, a combination of adsorption and mercury porosimetry can be useful. Hence, some important recent advances in understanding the mercury intrusion/extrusion mechanism are discussed. Additionally, some promising complementary techniques for characterization of porous materials immersed in a liquid phase are introduced.

Representative Pores: An Efficient Method to Characterize Activated Carbons

We propose a pore size analysis methodology for carbonaceous materials that reduces complexity while maintaining the significant elements of the structure-property relationship. This method chooses a limited number of representative pores, which will constitute a simplified kernel to describe the pore size distribution (PSD) of an activated carbon. In this study we use the representative pore sizes of 7.0, 8.9, 18.5, and 27.9 Å and N2 isotherms at 77.4 K to determine the PSD which is later applied to predict the adsorption equilibrium of other gases. In this study we demonstrate the ability to predict adsorption of different gas molecules on activated carbon from the PSD generated with representative pores (PSDrep). The methodology allows quick solutions for large-scale calculations for carbonaceous materials screening, in addition to make accessible an easily understood and prompt evaluation of the structure-property relationship of activated carbons. In addition to the details of the methodology already tested in different fields of application of carbonaceous materials, we present a new application related to the removal of organic contaminants in dilute aqueous solutions.

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models

Postcombustion CO₂ capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO₂ capture processes are considered too energetically expensive for widespread deployment. Vacuum swing adsorption (VSA) is a low-energy CCS that has the potential for industrial implementation if the right sorbents can be found. Metal-organic framework (MOF) materials are often promoted as sorbents for low-energy CCS by highlighting select adsorption properties without a clear understanding of how they perform in real-world VSA processes. In this work, atomistic simulations have been fully integrated with a detailed VSA simulator, validated at the pilot scale, to screen 1632 experimentally characterized MOFs. A total of 482 materials were found to meet the 95% CO₂ purity and 90% CO₂ recovery targets (95/90-PRTs)-365 of which have parasitic energies below that of solvent-based capture (∼290 kWh_e/MT CO₂) with a low value of 217 kWh_e/MT CO₂. Machine learning models were developed using common adsorption metrics to predict a material's ability to meet the 95/90-PRT with an overall prediction accuracy of 91%. It was found that accurate parasitic energy and productivity estimates of a VSA process require full process simulations.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.