High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Computational and Machine Learning Methods for CO2 Capture Using Metal-Organic Frameworks

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO2) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO2 capture is presented in this review article.

Theory guided engineering of zeolite adsorbents for acaricide residue adsorption from the environment

CONTEXT

Zeolites have attracted attention for their potential in adsorbing environmental contaminants. However, contaminants, such as acaricides used extensively in livestock production to control ticks and mites, have received limited exploration regarding their adsorption onto zeolite surfaces. This study aimed to identify the most appropriate zeolite frameworks for the adsorption of acaricide residues, deduce the mechanism underlying the adsorption process, and evaluate the impact of surface modification on the adsorption capabilities of zeolites.

METHODS

Grand Canonical Monte Carlo (GCMC) was used to screen the entire zeolite database to analyze their adsorption properties, where the cloverite zeolite framework (CLO) exhibits the highest adsorption capacity (percentage weight, 54%). Machine learning was employed to rank structural feature importance on adsorption. Density and helium void fraction appeared to be the most important structural features. Thus, engineering these features is of utmost significance in harvesting the desired acaricides. The second step involved engineering the structural and electronic properties of the shortlisted zeolite frameworks via cation substitution with suitable atoms. DFT calculations involving natural bond orbital (NBO) analysis and quantum theory of atoms in molecules (QTAIM) have been done to understand the influence of cation substitution on the electronic structure.

Using scalable computer vision to automate high-throughput semiconductor characterization

High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1-xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

NaK alloy as a versatile reagent for template-free synthesis of porous metal- and metalloid-based nanostructures

Using the strong reduction potential of the liquid NaK-78 alloy, we present a new versatile template-free approach to the synthesis of porous metal- and metalloid-based nanomaterials. With this novel approach, NaK can be simultaneously used as an agent for reduction, structure directing, and pore formation without the use of additional reagents.

Spontaneous Dipole Reorientation in Confined Water and Its Effect on Wetting/Dewetting of Hydrophobic Nanopores

The properties of nanoconfined fluids are important for a broad range of natural and engineering systems. In particular, wetting/dewetting of hydrophobic nanoporous materials is crucial due to their broad applicability for molecular separation and liquid purification; energy storage, conversion, recuperation, and dissipation; for catalysis, chromatography, and so on. In this work, a rapid, orchestrated, and spontaneous dipole reorientation was observed in hydrophobic nanotubes of various pore sizes d (7.9-16.5 Å) via simulations. This phenomenon leads to the fragmentation of water clusters in the narrow nanopores (d = 7.9, 10 Å) and strongly affects dewetting through cluster repulsion. The cavitation in these pores has an electrostatic origin. The dependence of hydrogen-bonded network properties on the tube aperture is obtained and is used to explain wetting (intrusion)-dewetting (extrusion) hysteresis. Computer simulations and experimental data demonstrate that d equals ca. 12.5 Å is a threshold between a nonhysteretic (spring) behavior, where intrusion-extrusion is reversible, and a hysteretic one (shock absorber), where hysteresis is prominent. This work suggests that water clustering and the electrostatic nature of cavitation are important factors that can be effectively exploited for controlling the wetting/dewetting of nanoporous materials.

Fe-MOF Catalytic Nanoarchitectonic toward Electrochemical Ammonia Production

Electrochemical reduction of nitrate into ammonia has lately been identified as one among the promising solutions to address the challenges triggered by the growing global energy demand. Exploring newer electrocatalyst materials is vital to make this process effective and feasible. Recently, metal-organic framework (MOF)-based catalysts are being well investigated for electrocatalytic ammonia synthesis, accounting for their enhanced structural and compositional integrity during catalytic reduction reactions. In this study, we investigate the ability of the PCN-250-Fe3 MOF toward ammonia production in its pristine and activated forms. The activated MOF catalyst delivered a faradaic efficiency of about 90% at -1 V vs RHE and a yield rate of 2.5 × 10-4 mol cm-2 h-1, while the pristine catalyst delivered a 60% faradaic efficiency at the same potential. Theoretical studies further provide insights into the nitrate reduction reaction mechanism catalyzed by the PCN-250-Fe3 MOF catalyst. In short, simpler and cost-effective strategies such as pretreatment of electrocatalysts have an upper hand in aggravating the intrinsic material properties, for catalytic applications, when compared to conventional material modification approaches.

Chemical complexity for targeted function in heterometallic titanium-organic frameworks

Research on metal-organic frameworks is shifting from the principles that control the assembly, structure, and porosity of these reticular solids, already established, into more sophisticated concepts that embrace chemical complexity as a tool for encoding their function or accessing new properties by exploiting the combination of different components (organic and inorganic) into these networks. The possibility of combining multiple linkers into a given network for multivariate solids with tunable properties dictated by the nature and distribution of the organic connectors across the solid has been well demonstrated. However, the combination of different metals remains still comparatively underexplored due to the difficulties in controlling the nucleation of heterometallic metal-oxo clusters during the assembly of the framework or the post-synthetic incorporation of metals with distinct chemistry. This possibility is even more challenging for titanium-organic frameworks due to the additional difficulties intrinsic to controlling the chemistry of titanium in solution. In this perspective article we provide an overview of the synthesis and advanced characterization of mixed-metal frameworks and emphasize the particularities of those based in titanium with particular focus on the use of additional metals to modify their function by controlling their reactivity in the solid state, tailoring their electronic structure and photocatalytic activity, enabling synergistic catalysis, directing the grafting of small molecules or even unlocking the formation of mixed oxides with stoichiometries not accessible to conventional routes.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal-Organic Frameworks

Advancements in hypothetical metal-organic framework (hMOF) databases and construction tools have resulted in a rapidly expanding chemical design space for nanoporous materials. The bulk of these hypothetical structures are constructed using structural building units (SBUs) derived from experimental MOF structures, often collected from the CoRE-MOF database. Recent investigations into the state of these deposited experimental structures' chemical accuracy identified an array of common structural errors, including omitted protons, missing counterions, and disordered structures. These structural errors propagate into the SBUs mined from experimental MOFs, culminating in inaccurate hMOF structures possessing net charges or missing atoms which were not accounted for previously. This work demonstrates how manual investigation was applied to diagnose structural errors in SBUs obtained from several popular hMOF construction tools and databases. An analysis of the prevailing errors discovered during the examination process is provided along with representative cases to aid with error detection in future studies involving SBU extraction and hMOF construction. A novel repair protocol was established and employed to generate a library of SBUs that are hand-examined and labeled with enhanced detail (HEALED). This repaired library of SBUs contains 952 inorganic SBUs and 568 organic SBUs ideally suited for the generation of hypothetical frameworks that are chemically accurate and properly charge labeled. Additionally, case studies following the effects of SBU errors on electrostatic potential-fitted charges and GCMC-simulated gas adsorption predictions are presented to highlight the significance of using chemically accurate hMOF structures exclusively in all screening efforts going forward.

High-Throughput Preparation of Supramolecular Nanostructures on Metal Surfaces

One of the contemporary challenges in materials science lies in the rapid materials screening and discovery. Experimental sample libraries can be generated by high-throughput parallel synthesis to map the composition space for rapid material discoveries. Molecular self-assembly on surfaces has proved a useful way to construct nanostructures with interesting topologies or properties. Despite the strong dependence of molecular stoichiometry on the structures, high-throughput preparations of supramolecular surface nanostructures have been far less explored. Here, by integrating a physical mask into the standard ultra-high-vacuum (UHV) molecular preparation system we show a high-throughput approach for preparing supramolecular nanostructures of continuous composition spreads on metal surfaces. The spatially addressable sample libraries of supramolecular self-assemblies are characterized by high-resolution scanning probe microscopy. We could explore different binary nanostructures of varying molecular ratios on one single substrate. Moreover, we use the minimum spanning tree approach to qualitatively and quantitatively study the structural properties of the formed nanostructures. This high-throughput approach may accelerate the screening and exploration of surface-supported, low-dimensional nanostructures not limited to supramolecular interactions.

Rapid Generation of Metal-Organic Framework Phase Diagrams by High-Throughput Transmission Electron Microscopy

Metal-organic frameworks (MOFs) constructed from Zr₆ nodes and tetratopic carboxylate linkers display high structural diversity and complexity in which various crystal topologies can result from identical building units. To determine correlations between MOF topologies and experimental parameters, such as solvent choice or modulator identity and concentration, we demonstrate the rapid generation of phase diagrams for Zr₆-MOFs with 1,4-dibromo-2,3,5,6-tetrakis(4-carboxyphenyl)benzene linkers under a variety of conditions. We have developed a full set of methods for high-throughput transmission electron microscopy (TEM), including automated sample preparation and data acquisition, to accelerate MOF characterization. The use of acetic acid as a modulator yields amorphous, NU-906, NU-600, and mixed-phase structures depending on the ratio of N,N-dimethylformamide to N,N-diethylformamide solvent and the quantity of the modulator. Notably, the use of formic acid as a modulator enables direct control of crystal growth along the c direction through variation of the modulator quantity, thus realizing aspect ratio control of NU-1008 crystals with different catalytic hydrolysis performance toward a nerve agent simulant.

Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers

A combination of machine learning and high throughput simulation has identified several potential zeolite structures that appear to outperform the leading commercially used material and explained the key factors for high selectivity.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

High-Throughput Discovery of a Rhombohedral Twelve-Connected Zirconium-Based Metal-Organic Framework with Ordered Terephthalate and Fumarate Linkers

We report a metal-organic framework where an ordered array of two linkers with differing length and geometry connect [Zr6 (OH)4 O4 ]12+ clusters into a twelve-connected fcu net that is rhombohedrally distorted from cubic symmetry. The ordered binding of equal numbers of terephthalate and fumarate ditopic carboxylate linkers at the trigonal antiprismatic Zr6 core creates close-packed layers of fumarate-connected clusters that are connected along the single remaining threefold axis by terephthalates. This well-defined linker arrangement retains the three-dimensional porosity of the Zr cluster-based UiO family while creating two distinct windows within the channels that define two distinct guest diffusion paths. The ordered material is accessed by a restricted combination of composition and process parameters that were identified by high-throughput synthesis.

Ionic Covalent Organic Frameworks for Energy Devices

Covalent organic frameworks (COFs) are a class of porous crystalline materials whose facile preparation, functionality, and modularity have led to their becoming powerful platforms for the development of molecular devices in many fields of (bio)engineering, such as energy storage, environmental remediation, drug delivery, and catalysis. In particular, ionic COFs (iCOFs) are highly useful for constructing energy devices, as their ionic functional groups can transport ions efficiently, and the nonlabile and highly ordered all-covalent pore structures of their backbones provide ideal pathways for long-term ionic transport under harsh electrochemical conditions. Here, current research progress on the use of iCOFs for energy devices, specifically lithium-based batteries and fuel cells, is reviewed in terms of iCOF backbone-design strategies, synthetic approaches, properties, engineering techniques, and applications. iCOFs are categorized as anionic COFs or cationic COFs, and how each of these types of iCOFs transport lithium ions, protons, or hydroxides is illustrated. Finally, the current challenges to and future opportunities for the utilization of iCOFs in energy devices are described. This review will therefore serve as a useful reference on state-of-the-art iCOF design and application strategies focusing on energy devices.

Exploring and expanding the Fe-terephthalate metal-organic framework phase space by coordination and oxidation modulation

The synthesis of phase pure metal-organic frameworks (MOFs) - network solids of metal clusters connected by organic linkers - is often complicated by the possibility of forming multiple diverse phases from one metal-ligand combination. For example, there are at least six Fe-terephthalate MOFs reported to date, with many examples in the literature of erroneous assignment of phase based on diffraction data alone. Herein, we show that modulated self-assembly can be used to influence the kinetics of self-assembly of Fe-terephthalate MOFs. We comprehensively assess the effect of addition of both coordinating modulators and pH modulators on the outcome of syntheses, as well as probing the influence of the oxidation state of the Fe precursor (oxidation modulation) and the role of the counteranion on the phase(s) formed. In doing so, we shed light on the thermodynamic landscape of this phase system, uncover mechanistics of modulation, provide robust routes to phase pure materials, often as single crystals, and introduce two new Fe-terephthalate MOFs to an already complex system. The results highlight the potential of modulated self-assembly to bring precision control and new structural diversity to systems that have already received significant study.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

Recent Advances of Porous Materials Based on Cyclodextrin

Porous materials have attracted significant attention because of their rising applications in many fields. Cyclodextrins (CDs) are suitable building units in the fabrication of porous materials owing to their intrinsic nanoporous structure, easy modification, and biocompatibility, which may result in the formation of CD-based organic frameworks (including cyclodextrin metal-organic frameworks (CD-MOFs) and cyclodextrin covalent organic frameworks (CD-COFs)), and CD-based polymer hybrid porous materials. This review focuses on the recent progress in the fabrication and applications of CD-based porous materials with novel structures and functionalities.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

Synchrotron infrared spectroscopic high-throughput screening of multi-composite photocatalyst films for air purification

Synchrotron-based infrared microscope was used for the high-throughput screening of Fe³⁺/Nb⁵⁺ doped TiO₂ photocatalysts for air purification.