Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

Latest developments in the synthesis of metal-organic frameworks and their hybrids for hydrogen storage

Metal-organic frameworks (MOFs) are promising materials for hydrogen (H2) storage due to their versatile structures, high surface areas and substantial pore volumes. This paper provides a comprehensive review of MOF synthesis and characterization, as well as their practical applications for H2 storage. We explore various MOF synthesis techniques, highlighting their impact on the nanopore structure and functionality. Special emphasis is placed on strategies for enhancing H2 storage capacities by increasing specific surface areas, optimizing pore size distributions, and facilitating H2 release by improving thermal conductivity. Key advances in MOF-based hybrids, such as MOFs combined with carbonaceous materials, metals or other inorganic materials, are discussed. This review also addresses the effectiveness of linker functionalization and the introduction of unsaturated metal centers to optimize H2 storage under ambient conditions. We conclude that the development of competitive MOF-based hybrids, particularly those that incorporate carbons, offers significant potential for improving H2 storage and recovery, enhancing thermal stability and increasing thermal conductivity. These advancements are in line with the US Department of Energy (DOE) specifications and pave the way for future research into the optimization of MOFs for practical H2 storage applications.

Functionalization of monolithic MOF thin films with hydrocarbon chains to achieve superhydrophobic surfaces with tunable water adhesion strength

While the accessible pores render an enormous variety of functionalities to the bulk of metal-organic frameworks (MOFs), the outer surfaces exposed by these crystalline materials also offer unique characteristics not available when using conventional substrates. By grafting hydrocarbon chains to well-defined MOF thin films (SURMOFs) prepared using layer-by-layer methods, we were able to fabricate superhydrophobic substrates with static water contact angles over 160°. A detailed theoretical modelling of the hydrocarbon chains grafted on the outer SURMOF surface with well-defined spacing between anchoring points reveals that the grafted hydrocarbon chains behave similarly to polymer brushes during wetting, where conformational entropy is traded with mixing entropy. The chains are coiled and can access many different conformations, as evidenced directly by infrared spectroscopy. The entropic contributions from the coiled state lead to a pronounced reduction of the surface free energy, rendering superhydrophobic properties to the functionalized SURMOFs. On the other side, the water adhesion strength could be decreased by increasing the surface roughness on the nanometer scale.

Application of a porous zirconium-based MOF nanoplate as an affinity ECL platform for the detection of protein kinase activity and inhibitor screening

Abnormal kinase expression affects phosphorylation in the human body, which is associated with numerous diseases, including cancer, diabetes mellitus, and Alzheimer's disease. In this study, we synthesized a highly stable, two-dimensional, luminescence-functionalized metal-organic framework with remarkable electrochemiluminescence (ECL) by immobilizing 9,10-Di(p-carboxyphenyl) anthracene (dca) on a zirconium cluster (dca-Zr₁₂) via a strong coordination bond between -COO⁻ and Zr⁴⁺. This novel and simple platform relies on the highly specific identification of phosphate molecules by the ultra-thin dca-Zr₁₂ nanoplate through carboxylate-Zr⁴⁺-phosphate chemistry. The ferrocene-labeled peptide substrate (Fc-S-Peptide) was phosphorylated in the presence of protein kinase A (PKA) and adenosine 5'-triphosphate (ATP), and the resulting phosphopeptide could subsequently be precisely captured by the zirconium sites of the dca-Zr12-modified electrode and, eventually, quench the ECL and gain a signal-off state. This rapid and simple detection strategy was successfully employed to measure PKA activity, with a detection limit as low as 0.35 mU mL-1. Based on the results, it exhibited high selectivity and can be applied for screening PKA inhibitors. The technique was subsequently applied to detect protein kinase activity in drug-stimulated MCF-7 cell lysates, demonstrating its potential for kinase-related investigations. Further, this platform could identify the activity of other kinase types with universal applicability.

Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation

The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations.

Investigation of combustion characteristics of critical quenching hydrogen mixing ratios in the presence of ordered porous media

In order to promote low-carbon sustainable development in the ecological environment and improve the efficiency of hydrogen and natural gas energy utilization, this project carried out research on the explosive effects of different thicknesses of ordered porous media on the hydrogen-methane gas mixture. A detailed discussion was conducted based on the critical quenching hydrogen blending ratio under the thicknesses of 50 mm and 60 mm of ordered porous media. The results indicate that the critical quenching hydrogen blending ratio is 9% for a thickness of 50 mm and 20% for a thickness of 60 mm, indicating that greater thickness enhances flame suppression capabilities. Between the critical quenching hydrogen blending ratio range for thicknesses of both 50 mm and 60 mm, the peak values of flame front velocity, reverse diffusion flame length, and explosion pressure initially decrease and then subsequently increase with an increasing hydrogen content. As the thickness of the flame retardant medium augments, there is an increase in both the flame velocity and the reverse diffusion length at the critical hydrogen concentration. However, the pressure peak observed at a thickness of 50 mm surpasses that at 60 mm. The pressure curve experiences sudden fluctuations due to the combined effects of explosion pressure and heat transfer, with the initial point of this abrupt change closely linked to the thickness of the ordered porous media. Therefore, it is imperative to maintain hydrogen content below the critical quenching hydrogen blending ratio to ensure the safe transport and utilization of hydrogen and natural gas energy.

The Potential of Polymers and Glass to Enhance Hydrogen Storage Capacity: A Mathematical Approach

This manuscript contributes to understanding the role of hydrogen in different materials, emphasizing polymers and composite materials, to increase hydrogen storage capacity in those materials. Hydrogen storage is critical in advancing and optimizing sustainable energy solutions that are essential for improving their performance. Capillary arrays, which offer increased surface area and optimized storage geometries, present a promising avenue for enhancing hydrogen uptake. This work evaluates various polymers and glass for their mechanical properties and strength with 700 bar inner pressure loads within capillary tubes. A theoretical mathematical approach was employed to quantify the impact of material properties on storage capacity. Our results demonstrate that certain polymers (e.g., Zylon AS, Dyneema SK99) and glass types (S-2 Glass) exhibit superior hydrogen storage potential due to their enhanced strength and low density. These findings suggest that integrating the proposed materials into capillary array systems can significantly improve hydrogen storage efficiency (15-37 wt.% and 37-40 g/L), making them viable candidates for next-generation energy storage systems. This study provides valuable insights into material selection and structural design strategies for high-capacity hydrogen storage technologies.

The Coupling of Synthesis and Electrochemistry to Enable the Reversible Storage of Hydrogen as Metal Hydrides

Given its high gravimetric energy density and status as a clean fuel when derived from renewables, hydrogen (H2) is considered a premier candidate for energy storage; however, its low volumetric density limits its broader application. Chemical storage through the reversible incorporation of H2 into chemical bonds offers a promising solution to its low volumetric density, circumventing subpar energy densities and substantial infrastructure investments associated with physical storage methods. Metal hydrides are promising candidates for chemical storage because of their high gravimetric capacity and tunability through nanostructuring and alloying. Moreover, metal hydride/H2 interconversion may be interfaced with electrochemistry, which offers potential solutions to some of the challenges associated with traditional thermochemical platforms. In this Perspective, we describe anticipated challenges associated with electrochemically mediated metal hydride/H2 interconversion, including thermodynamic efficiencies of metal hydride formation, sluggish kinetics, and electrode passivation. Additionally, we propose potential solutions to these problems through the design of molecular mediators that may control factors such as metal hydride solubility, particle morphology, and hydride affinity. Realization of an electrochemically mediated metal hydride/H2 interconversion platform introduces new tools to address challenges associated with hydrogen storage platforms and contributes toward the development of room-temperature hydrogen storage platforms.

Ultrahigh-surface area covalent organic frameworks for methane adsorption

Developing porous materials with ultrahigh surface areas for gas storage (for example, methane) is attractive but challenging. Here, we report two isostructural three-dimensional covalent organic frameworks (COFs) with a rare self-catenated alb-3,6-Ccc2 topology and a pore size of 1.1 nanometer. Notably, these imine-linked microporous COFs show both high gravimetric Brunauer-Emmett-Teller (BET) surface areas (~4400 square meters per gram) and volumetric BET surface areas (~1900 square meters per cubic centimeter). Moreover, their volumetric methane uptake reaches up to 264 cubic centimeter (standard temperature and pressure) per cubic centimeter [cm3 (STP) cm-3] at 100 bar and 298 kelvin, and they exhibit the highest volumetric working capacity of 237 cm3 (STP) cm-3 at 5 to 100 bar and 298 kelvin among all reported porous crystalline materials.

Improving the sulfite-detection performance of a fluorescent probe via post-synthetic modification with a metal-organic framework

In this work, a post-synthetic modification strategy was attempted to improve the performance of the probe for sulfite detection. The assembled platform UiO-66-NH-DQA, which was acquired by anchoring the sulfite-response fluorescent probe DQA onto the surface of UiO-66-NH2via amide covalent bonds, exhibited enhanced fluorescence intensity and practical intracellular imaging capability. In spite of the structural similarity, as verified by characterization tests, the conversion rate of post-synthetic modification was calculated as 35%, equaling an approximate assembly ratio of 1 : 2 between UiO-66-NH2 and DQA. Most significantly, conversion into UiO-66-NH-DQA led to a 5.6-fold enhancement in the reporting signal with a red shift of 20 nm. For sulfite detection, the linear range was 0-150 μM, with a limit of detection value of 0.025 μM. UiO-66-NH-DQA retained advantages including high stability (within pH 5.0-9.0), rapid response (within 15 min) and high selectivity. Based on low cytotoxicity and relatively rapid cellular uptake, UiO-66-NH-DQA achieved the imaging of both the exogenous and endogenous sulfite levels in living cells. In particular, its rapid cell-permeating capability was guaranteed during the modification. The post-synthetic modification strategy reported herein has potential for improving the practical properties of fluorescent monitoring materials.

From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks

Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed innovations brought by data-driven machine learning methods, which have enabled a deeper understanding of the chemical nature of MOFs and led to the development of novel structures. Notably, machine learning is continuously and rapidly advancing as new methodologies, architectures, and data representations are actively being investigated, and their implementation in materials discovery is vigorously pursued. Under these circumstances, it is important to closely monitor recent research trends and identify the technologies that are being introduced. In this Perspective, we focus on emerging trends of machine learning within the field of MOFs, the challenges they face, and the future directions of their development.

Nanoscopic Insights into the Collapse of Metal-Organic Frameworks during Solvent Evacuation: Molecular Simulation Investigation

Many metal-organic frameworks (MOFs) undergo structural collapse upon solvent evacuation during activation, which is attributed to the capillary force generated by the solvent. However, little effort has been devoted to unveiling the nature of such a force. Herein, we employ molecular dynamics (MD) simulations to investigate the evacuation of different solvents in two MOFs (MOF-5 and UMCM-9). The contractive stress induced by solvent evacuation is quantified and unraveled to positively correlate with the surface tension of the solvent. Moreover, the mechanical strength (or amorphization) of the MOF is calculated using reactive MD simulations. By comparing the contractive stress with the amorphization stress, for the first time, we predict the likelihood of collapse of MOFs during activation by different solvents, which agrees well with the experiments. The methodology developed provides nanoscopic insights into the activation process; it can assist in avoiding structural collapse by judiciously selecting a proper solvent for activation or by modifying a framework.

Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties

Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find optimal MOFs with properties that may depend on both chemistry and structure. We statistically assessed VBO by its ability to optimize three NH3-adsorption dependent performance metrics that depend, to different degrees, on MOF chemistry and structure. With ten simulated campaigns done for each metric, VBO consistently outperformed random search to find high-performing designs within a 1000-MOF subset for (i) NH3 storage, (ii) NH3 removal from membrane plasma reactors, and (iii) NH3 capture from air. Then, with one campaign dedicated to finding optimal MOFs for NH3 storage in a "hybrid" ∼10 000-MOF database, we identify twelve extant and eight hypothesized MOF designs with potentially record-breaking working capacity ΔN NH3 between 300 K and 400 K at 1 bar. Specifically, the best MOF designs are predicted to (i) achieve ΔN NH3 values between 23.6 and 29.3 mmol g-1, potentially surpassing those that MOFs previously experimentally tested for NH3 adsorption would have at the proposed operation conditions, (ii) be thermally stable at the operation conditions and (iii) require only ca. 10% of the energy content in NH3 to release the stored molecule from the MOF. Finally, the analysis of the generated simulation data during the search indicates that a pore size of around 10 Å, a heat of adsorption around 33 kJ mol-1, and the presence of Ca could be part of MOF design rules that could help optimize NH3 working capacity at the proposed operation conditions.

pyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules

Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models

A machine learning (ML) model is developed for predicting useable methane (CH4) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH4. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH4 capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH4 capacity for a prescribed operating condition.

Nano-Schottky-junction-engineered Pd/SnO2 nanotube array for ultrasensitive hydrogen sensing at room temperature

Detecting H2 at low concentrations is important due to it being a major safety concern in practical applications. However, semiconductor chemiresistive gas sensors always suffer from high operating temperatures and power consumption, as well as a limited concentration detection range, which restricts their widespread use. Herein, we developed a 3D nanostructured gas sensor employing a Pd-nanocluster-decorated SnO2 nanotube array as the sensing layer. The sensor showed sensitive and selective properties for detecting low concentrations of H2 at room temperature, with a low limit of detection of 1.6 ppb. It also showed good long-term stability, as long as 100 days. Moreover, systematical characterizations were performed in conjunction with density functional theory (DFT) calculations to determine the ability of Pd/SnO2 junctions to improve the gas-sensing properties. The engineering of the nano-Schottky junction allows us to expand the library for designing low-power-consumption H2 sensors for widespread applications.

Simulation of Hydrogen Adsorption in Hierarchical Silicalite: Role of Electrostatics and Surface Chemistry

Adsorption in nanoporous materials is one strategy that can be used to store hydrogen at conditions of temperature and pressure that are economically viable. Adsorption capacity of nanoporous materials depends on surface area which can be enhanced by incorporating a hierarchical pore structure. We report grand canonical Monte Carlo (GCMC) simulation results on the adsorption of hydrogen in hierarchical models of silicalite that incorporate 4 nm wide mesopores in addition to the 0.5 nm wide micropores at 298 K, using different force fields to model hydrogen. Our results suggest that incorporating mesopores in silicalite can enhance adsorption by at least 20 % if electrostatic interactions are not included and up to 100 % otherwise. Incorporating electrostatic interactions results in higher adsorption by close to 100 % at lower pressures for hierarchical silicalite whereas for unmodified silicalite, it is less significant at all pressures. Hydroxylating the mesopore surface in hierarchical silicalite results in an enhancement in adsorption at pressures below 1 atm and suppression by up to 20 % at higher pressures. Temperature dependence at selected pressures exhibits expected decrease in adsorption amounts at higher temperatures. These findings can be useful in the engineering, selection, and optimization of nanoporous materials for hydrogen storage.

Maximizing Onboard Hydrogen Storage Capacity by Exploring High-Strength Novel Materials Using a Mathematical Approach

Hydrogen fuel holds promise for clean energy solutions, particularly in onboard applications such as fuel cell vehicles. However, the development of efficient hydrogen storage systems remains a critical challenge. This study addresses this challenge by exploring the potential of high-strength novel materials, including glass, to maximize onboard hydrogen storage capacity. A mathematical approach was employed to evaluate the feasibility and efficacy of various high-strength materials for hydrogen storage. This study focused on capillary arrays as a promising storage medium and utilized mathematical modeling techniques to estimate the storage capacity enhancement achievable with different materials. The analysis revealed significant variations in storage capacity enhancements in different high-strength novel materials, with glass having promising results. Glass-based materials demonstrated the potential to meet or exceed US Department of Energy (DOE) targets for both gravimetric and volumetric hydrogen storage capacities in capillary arrays. By leveraging a mathematical approach, this study identified high-strength novel materials, including glass and polymers, capable of substantially improving onboard hydrogen storage capacity: 29 wt.% with 40 g/L for quartz glass and 25 wt.% with 38 g/L for Kevlar compared to 5.2 wt.% with 26.3 g/L from a conventional type IV tank. These findings underscore the importance of material selection in optimizing hydrogen storage systems and provide valuable insights for the design and development of next-generation hydrogen storage technologies for onboard applications.

Chromium(II)-isophthalate 2D MOF with Redox-Tailorable Gas Adsorption Selectivity

Redox-active metal-organic frameworks (MOFs) are very promising materials due to their potential capabilities for postsynthetic modification aimed at tailoring their application properties. However, the research field related to redox-active MOFs is still relatively underdeveloped, which limits their practical application. We investigated the self-assembly process of Cr(II) ions and isophthalate (m-bdc) linkers, which have been previously demonstrated to yield 0D metal-organic polyhedra. However, using the diffusion-controlled synthetic approach, we demonstrate the selective preparation of a 2D-layered Cr(II)-based MOF material [Cr(m-bdc)]·H2O (1·H2O). Remarkably, the controlled oxidation of the developed 2D MOF using nitric oxide or dry oxygen resulted in modified porous materials with excellent H2/N2 adsorption selectivities.

Designed additive suppresses interpenetration in IRMOF-10

IRMOF-10, derived from biphenyl-4,4'-dicarboxylic acid and zinc, is a prototype for an open cubic structure prone to interpenetration. Interpenetration can compromise MOF pore volume and surface area which drives the need to develop strategies to synthesize non-interpenetrated MOFs. In this work, an additive design strategy was employed to suppress interpenetration of IRMOF-10. The presence of the additive during traditional solvothermal synthesis yielded an activated material with the highest surface area reported to date and enabled the determination of the single crystal structure. In situ monitoring of the nucleation of crystals under polarized light provided insights into the mechanism behind interpenetration in the IRMOF-9/10 system. This work provides a roadmap to suppress interpenetration more generically in other MOFs and achieve improved surface areas and pore volumes for this prominent class of porous sorbents.

Exploring Nanomaterials for Hydrogen Storage: Advances, Challenges, and Perspectives

Hydrogen energy heralded for its environmentally friendly, renewable, efficient, and cost-effective attributes, stands poised as the primary alternative to fossil fuels in the future. Despite its great potential, the low volumetric density presents a formidable challenge in hydrogen storage. Addressing this challenge necessitates exploring effective storage techniques for a sustainable hydrogen economy. Solid-state hydrogen storage in nanomaterials (physically or chemically) holds promise for achieving large-scale hydrogen storage applications. Such approaches offer benefits, including safety, compactness, lightness, reversibility, and efficient generation of pure hydrogen fuel under mild conditions. This article presents solid-state nanomaterials, specifically nanoporous carbons (activated carbon, carbon fibers), metal-organic frameworks, covalently connected frameworks, nanoporous organic polymers, and nanoscale metal hydrides. Furthermore, new developments in hydrogen fuel cell technology for stationary and mobile applications have been demonstrated. The review outlines significant advancements thus far, identifies key barriers to practical implementation, and presents a perspective for future sustainable energy research. It concludes with recommendations to enhance hydrogen storage performance for cost-effective and long-lasting utilization.

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials

The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) are crucial for addressing global challenges like climate change, energy security, and environmental degradation. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the complex design spaces of NPMs for gas storage applications. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO using a curated set of surrogate model and acquisition function couples. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, including the conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimisation python library (BO Torch) outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery and addresses urgent needs in energy storage and environmental sustainability. It is to be noted that rather than identifying new MOFs, BO primarily enhances computational efficiency by reducing the reliance on more demanding calculations, such as those involved in Grand Canonical Monte Carlo (GCMC) or Density Functional Theory (DFT).

Recent Development of Physical Hydrogen Storage: Insights into Global Outlook and Future Applications

Transition of global energy market towards an environment-friendly sustainable society requires a profound transformation from fossil fuel to zero carbon emission fuel. To cope with this goal production ofrenewable energy is accelerating worldwide. Research in renewable energy from production and storage to practical utilization requires an organized approach. One of the best renewable energy carrier is the hydrogen, due to its clean combustion and abundance. Nonetheless, its storage is a critical challenge to its success. Hydrogen must be stored long after being produced and transported to a storage site. Physical hydrogen storage is vital among hydrogen storage modes, and its shortcoming needs to overcome for its successful and economic benefits. This review intends to discuss the techniques and applications of physical hydrogen storage in the state of compressed gas, liquefied hydrogen gas, and cold/cryo compressed gas concerning their working principle, chemical and physical properties, influencing factors for physical hydrogen storage, and transportation, economics, and global outlook. Insights of several probable physical hydrogen storage (PHS) systems are highlighted. The outcomes of this review envisioned that the PHS still necessitates technological advancements despite having remarkable success. The Liquid Hydrogen Gas storage marks better sustainability than Compressed and Cryo Compressed Gas. The physical hydrogen storage method can store hydrogen in tanks in any state (liquid or gas) under 20 K for the liquid state and ambient temperature for the gaseous state The Bibliographic analysis depicts that the research in hydrogen rising with time and mostly the research in conducted in USA with 231 articles. Prospects and challenges with lessons learned and the limitation opens the door to further research, which would be helpful for efficient and long-term physical hydrogen storage.

Metal-Organic Frameworks (MOFs) As Hydrogen Storage Materials At Near-Ambient Temperature

Hydrogen may play a critical role in our efforts to de-carbonize by 2050. However, there remain technical challenges in the storage and transport of hydrogen. Metal-organic frameworks (MOFs) have shown significant promise for hydrogen storage at cryogenic temperatures. A material that can meet the US department of energy (DOE) ultimate goal of 6.5 wt. % for gravimetric performance and 50 g/L for volumetric storage at near-ambient temperatures would unlock hydrogen as a future fuel source for on-board applications. Metal-organic frameworks typically have low heat of adsorptions (i. e. 4-7 kJ/mol), whereas for storing significant quantities of hydrogen at near-ambient temperatures, 15-25 kJ/mol is likely required. In this review we explore the current methods used (i. e., open-metal sites, alkali dopants and hydrogen spillover) for promoting strong adsorption within MOFs. Further we discuss MOF-based materials with respect to the technical aspects of deliverable capacity, kinetics and stability.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

State-of-the-art review on hydrogen's production, storage, and potential as a future transportation fuel

Global energy consumption is expected to reach 911 BTU by the end of 2050 as a result of rapid urbanization and industrialization. Hydrogen is increasingly recognized as a clean and reliable energy vector for decarbonization and defossilization across various sectors. Projections indicate a significant rise in global demand for hydrogen, underscoring the need for sustainable production, efficient storage, and utilization. In this state-of-the-art review, we explore hydrogen production methods, compare their environmental impacts through life cycle analysis, delve into geological storage options, and discuss hydrogen's potential as a future transportation fuel. Combining electrolysis to make hydrogen and storing it in porous underground materials like salt caverns and geological reservoirs looks like a good way to balance out the variable supply of renewable energy and meet the demand at peak times. Hydrogen is a key component of our sustainable economy, and this article gives a broad overview of the process from production to consumption, touching on technical, economic, and environmental concerns along the way. We have made an attempt in this paper to compile different methods for the production of hydrogen and its storage, the challenges faced by current methods in the manufacturing of hydrogen gas, and the role of hydrogen in the future. This review paper will serve as a very good reference for hydrogen system engineering applications. The paper concludes with some suggestions for future research to help improve the technological efficiency of certain production methods, all with the goal of scaling up the hydrogen economy.

Recent developments of (bio)-sensors for detection of main microbiological and non-biological pollutants in plastic bottled water samples: A critical review

The importance of water in all biological processes is undeniable. Ensuring access to clean and safe drinking water is crucial for maintaining sustainable water resources. To elaborate, the consumption of water of inadequate quality can have a repercussion on human health. Furthermore, according to the instability of tap water quality, the consumption rate of bottled water is increasing every day at the global level. Although most people believe bottled water is safe, it can also be contaminated by microbiological or chemical pollution, which can increase the risk of disease. Over the last decades, several conventional analytical tools applied to analyze the contamination of bottled water. On the other hand, some limitations restrict their application in this field. Therefore, biosensors, as emerging analytical method, attract tremendous attention for detection both microbial and chemical contamination of bottled water. Biosensors enjoy several facilities including selectivity, affordability, and sensitivity. In this review, the developed biosensors for analyzing contamination of bottled water were highlighted, as along with working strategies, pros and cons of studies. Challenges and prospects were also examined.

Long Duration Energy Storage Using Hydrogen in Metal-Organic Frameworks: Opportunities and Challenges

Materials-based H2 storage plays a critical role in facilitating H2 as a low-carbon energy carrier, but there remains limited guidance on the technical performance necessary for specific applications. Metal-organic framework (MOF) adsorbents have shown potential in power applications, but need to demonstrate economic promises against incumbent compressed H2 storage. Herein, we evaluate the potential impact of material properties, charge/discharge patterns, and propose targets for MOFs' deployment in long-duration energy storage applications including backup, load optimization, and hybrid power. We find that state-of-the-art MOF could outperform cryogenic storage and 350 bar compressed storage in applications requiring ≤8 cycles per year, but need ≥5 g/L increase in uptake to be cost-competitive for applications that require ≥30 cycles per year. Existing challenges include manufacturing at scale and quantifying the economic value of lower-pressure storage. Lastly, future research needs are identified including integrating thermodynamic effects and degradation mechanisms.

Construction of a novel Eu-MOF material based on different detection mechanisms and its application in sensing pollutants aniline, F- and Hg2

Aniline is an organic pollutant with carcinogenicity and teratogenicity, while F- and Hg2+ are toxic ions that are easily soluble in water. When they are released to the environment, they will pose a threat to human health. Designing a material that can simultaneously detect three types of pollutants is of great significance. In this paper, a novel rare earth metal organic framework material (Eu-MOF) with three-dimensional structure based on 1-methylimidazole-4,5-dicarboxylic acid was synthesized for the first time through solvent thermal method. It has excellent luminescent performance and can be used as a multifunctional fluorescent probe to detect aniline, F-, and Hg2+ based on photoinduced electron transfer, energy competitive absorption, and ion exchange mechanisms, with detection limits of 1.79 × 10-8, 8.13 × 10-8, and 8.83 × 10-7 M, respectively. It is worth noting that Eu-MOF can detect F- and Hg2+ in real water samples, such as lake water and green tea water, with favorable recovery rates.

High Hydrogen Storage in Trigonal Prismatic Monomer-Based Highly Porous Aromatic Frameworks

Hydrogen storage is crucial in the shift toward a carbon-neutral society, where hydrogen serves as a pivotal renewable energy source. Utilizing porous materials can provide an efficient hydrogen storage solution, reducing tank pressures to manageable levels and circumventing the energy-intensive and costly current technological infrastructure. Herein, two highly porous aromatic frameworks (PAFs), C-PAF and Si-PAF, prepared through a Yamamoto C─C coupling reaction between trigonal prismatic monomers, are reported. These PAFs exhibit large pore volumes and Brunauer-Emmett-Teller areas, 3.93 cm3 g-1 and 4857 m2 g-1 for C-PAF, and 3.80 cm3 g-1 and 6099 m2 g-1 for Si-PAF, respectively. Si-PAF exhibits a record-high gravimetric hydrogen delivery capacity of 17.01 wt% and a superior volumetric capacity of 46.5 g L-1 under pressure-temperature swing adsorption conditions (77 K, 100 bar → 160 K, 5 bar), outperforming benchmark hydrogen storage materials. By virtue of the robust C─C covalent bond, both PAFs show impressive structural stabilities in harsh environments and unprecedented long-term durability. Computational modeling methods are employed to simulate and investigate the structural and adsorption properties of the PAFs. These results demonstrate that C-PAF and Si-PAF are promising materials for efficient hydrogen storage.

Exceptional Hydrogen Uptake in Crystalline InxGa1-xN Semiconductors

The irradiation of InN and InxGa1-xN samples with low-energy H ions results in exceptionally high hydrogen uptake in a crystalline semiconductor. This phenomenon is attributed to specific In-H complex formation. By exploiting spectral fingerprints of the In-H complexes observable in In L3-edge X-ray absorption spectroscopy, we provide direct evidence of complex formation. Density functional theory calculations assist in interpreting the X-ray absorption spectra and offer insights into the energetics of complex formation. We quantify the total amount of reversibly incorporated hydrogen in these semiconductors and discuss their strengths and weaknesses as innovative materials for hydrogen storage.

Fe,Ni-Based Metal-Organic Frameworks Embedded in Nanoporous Nitrogen-Doped Graphene as a Highly Efficient Electrocatalyst for the Oxygen Evolution Reaction

The quest for economically sustainable electrocatalysts to replace critical materials in anodes for the oxygen evolution reaction (OER) is a key goal in electrochemical conversion technologies, and, in this context, metal-organic frameworks (MOFs) offer great promise as alternative electroactive materials. In this study, a series of nanostructured electrocatalysts was successfully synthesized by growing tailored Ni-Fe-based MOFs on nitrogen-doped graphene, creating composite systems named MIL-NG-n. Their growth was tuned using a molecular modulator, revealing a non-trivial trend of the properties as a function of the modulator quantity. The most active material displayed an excellent OER performance characterized by a potential of 1.47 V (vs. RHE) to reach 10 mA cm-2, a low Tafel slope (42 mV dec-1), and a stability exceeding 18 h in 0.1 M KOH. This outstanding performance was attributed to the synergistic effect between the unique MOF architecture and N-doped graphene, enhancing the amount of active sites and the electron transfer. Compared to a simple mixture of MOFs and N-doped graphene or the deposition of Fe and Ni atoms on the N-doped graphene, these hybrid materials demonstrated a clearly superior OER performance.

Insights into hydrogen and methane storage capacities: Grand canonical Monte Carlo simulations of SIGSUA

In the pursuit of sustainable energy solutions, the development of materials with efficient hydrogen and methane storage capacities is imperative, particularly for advancing hydrogen-powered vehicles. Metal-organic frameworks (MOFs) have emerged as promising candidates to meet the stringent targets set by the Department of Energy for both hydrogen and methane storage. This study employs Grand Canonical Monte Carlo simulations to investigate the usable hydrogen and methane gravimetric and volumetric storage capacities of the recently synthesized SIGSUA. A comparative analysis encompasses the selected MOFs with similar metal compositions, those with comparable density and average pore radius, and classical benchmarks, such as IRMOF-15 and IRMOF-20, all evaluated at room temperature and moderate pressures ranging from 25 to 35 MPa. The results reveal that SIGSUA demonstrates noteworthy gravimetric and volumetric storage capacities for both hydrogen and methane, rivaling or surpassing those of the selected MOFs for analysis. These findings underscore the potential of SIGSUA in advancing clean energy storage technologies.

Photo-thermal synergistic excitation: Feasible strategy to detect ethanol for wide bandgap ZIF-8 at low work temperature

Zeolitic Imidazolate Framework-8 (ZIF-8) material was prepared by chemical precipitation method. The microstructure and physical properties of the as-prepared samples were characterized by XRD, BET, FESEM and UV spectrophotometer. The self-made four-channel measurement device was used to test the gas sensitivity of ZIF-8 material toward ethanol gas under photo-thermal synergistic excitation. The results showed that the sample was typical ZIF-8 (Eg = 4.96 eV) with a regular dodecahedron shape and the specific surface is up to 1793 m2/g. The as-prepared ZIF-8 has a gas response value of 55.04 to 100 ppm ethanol at 75°C and it shows good gas sensing selectivity and repeated stability. The excellent gas sensitivity can be attributed to the increase of free electron concentration in the ZIF-8 conduction band by photo-thermal synergistic excitation, and the large specific surface area of ZIF-8 material provides more active sites for gas-solid surface reaction. The reaction mechanism of ZIF-8 material under multi-field excitation was also discussed.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks

Intrinsic properties of metal-organic frameworks (MOFs), such as their ultra porosity and high surface area, deem them promising solutions for problems involving gas adsorption. Nevertheless, due to their combinatorial nature, a huge number of structures is feasible which renders cumbersome the selection of the best candidates with traditional techniques. Recently, machine learning approaches have emerged as efficient tools to deal with this challenge, by allowing researchers to rapidly screen large databases of MOFs via predictive models. The performance of the latter is tightly tied to the mathematical representation of a material, thus necessitating the use of informative descriptors. In this work, a generalized framework to predict gaseous adsorption properties is presented, using as one and only descriptor the capstone of chemical information: the potential energy surface (PES). In order to be machine understandable, the PES is voxelized and subsequently a 3D convolutional neural network (CNN) is exploited to process this 3D energy image. As a proof of concept, the proposed pipeline is applied on predicting [Formula: see text] uptake in MOFs. The resulting model outperforms a conventional model built with geometric descriptors and requires two orders of magnitude less training data to reach a given level of performance. Moreover, the transferability of the approach to different host-guest systems is demonstrated, examining [Formula: see text] uptake in COFs. The generic character of the proposed methodology, inherited from the PES, renders it applicable to fields other than reticular chemistry.

Solvent-Directed Construction of a Nanoporous Metal-Organic Framework with Potential in Selective Adsorption and Separation of Gas Mixtures Studied by Grand Canonical Monte Carlo Simulations

In this report, a microporous metal-organic framework of [Ca(TDC)(DMA)]n (1) and a two-dimensional coordination polymer of [Ca(TDC)(DMF)2 ]n (2), (TDC2- =Thiophene-2,5-dicarboxylate, DMA=N, N'-dimethylacetamide and DMF=N, N'-dimethylformamide) based on Ca(II) were designed by the effect of solvent, and X-ray analysis was performed for the single crystals of 1 and 2. Then, compound 1 was synthesized in three different methods and identified with a set of analyses. Compared to other adsorbents, MOFs are widely used in the field of adsorption and separation of various gases due to a series of distinctive features such as diverse and adjustable structures pores with different dimensions, high porosity and surface area with regular distribution of active sites. Therefore, the ability of 1 to uptake single gases (CH4 , CO2 , C2 H2 , H2, and N2 ) and separation of several binary mixtures of gases (CO2 /CH4 , CO2 /N2 , CO2 /H2 and CO2 /C2 H2 ), were investigated using Grand Canonical Monte Carlo simulations. Volumetric and gravimetric adsorption isotherms in various operating conditions, the isosteric heat of adsorption (qst ), the chemical potential for each thermodynamic state, and snapshots during the simulation process were reported in all cases. The results obtained from the adsorption simulation indicate that compound 1 has a high capacity for uptake of H2 (16 mmol g-1 ) and N2 (12.5 mmol g-1 ), CO2 (6.6 mmol g-1 ), C2 H2 (5 mmol g-1 ) and CH4 (1.5 mmol g-1 ) gases at 1 bar. It also performs well in separating CO2 in binary mixtures, which can be attributed to the presence of open metal sites in nodes of 1 and their electrostatic tendency to interact with CO2 containing the higher quadrupole dipole moment compared to other components of the mixture.

Metal-organic frameworks (MOFs) for energy production and gaseous fuel and electrochemical energy storage applications

The increasing energy demands in society and industrial sectors have inspired the search for alternative energy sources that are renewable and sustainable, also driving the development of clean energy storage and delivery systems. Various solid-state materials (e.g., oxides, sulphides, polymer and conductive nanomaterials, activated carbon and their composites) have been developed for energy production (water splitting-H2 production), gaseous fuel (H2 and CH4) storage and electrochemical energy storage (batteries and supercapacitors) applications. Nevertheless, the low surface area, pore volume and conductivity, and poor physical and chemical stability of the reported materials have resulted in higher requirements and challenges in the development of energy production and energy storage technologies. Thus, to overcome these issues, the development of metal-organic frameworks (MOFs) has attracted significant attention. MOFs are a class of porous materials with extremely high porosity and surface area, structural diversity, multifunctionality, and chemical and structural stability, and thus they can be used in a wide range of applications. In the present review, we precisely discuss the interesting properties of MOFs and the various methodologies for their synthesis, and also the future dependence on the valorization of solid waste for the recovery of metals and organic ligands for the synthesis of new classes of MOFs. Subsequently, the utilization of these interesting characteristics for energy production (water splitting), storage of gaseous fuels (H2 and CH4), and electrochemical storage (batteries and supercapacitors) applications are described. However, although MOFs are efficient materials with versatile uses, they still have many challenges, limiting their practical applications. Therefore, finally, we highlight the challenges associated with MOFs and show the way forward in overcoming them for the development of these highly porous materials with large-scale practical utility.

Solid-State Solvent-Independent Excited-State Intramolecular Proton Transfer in a Coordination Polymer and Its Temperature Dependence

Increasing demand for futuristic switches and sensors around the world has created an intense interest in smart materials, which can show a rapid but feature-dependent change in the physical properties in the presence of external stimuli. Hitherto such changes in the photophysical property of materials, specifically in the solid state, are projected for the use of smart on-off switches. Materials having an external-stimuli-responsive change in the photophysical properties like excited-state intramolecular proton transfer (ESIPT) can also be utilized for these purposes. Although the event of solid-state ESIPT is not new in the domain of material chemistry, especially for organic molecules, it was never observed for coordination polymers (CPs). Previous instances of ESIPT in CPs have necessitated the presence of a solvent as a suspension medium, driving a solvent-assisted ESIPT phenomenon. However, the emergence of a solvent-independent ESIPT-enabled CP presents unique advantages. The well-defined periodic arrangement ensures reliable property variations, while the robust coordination bonds between the metal nodes and ligands provide durability in harsh environments. Addressing this gap, we present the first ever solid-state, solvent-free, and solvent-independent ESIPT-active CP. Remarkably, this CP exhibits temperature-dependent ESIPT on-off behavior, demonstrating its potential as a cutting-edge material in the field of smart switches and sensors.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

ZIF-8 Pellets as a Robust Material for Hydrogen Cryo-Adsorption Tanks

Cryoadsorption on the inner surface of porous materials is a promising solution for safe, fast, and reversible hydrogen storage. Within the class of highly porous metal-organic frameworks, zeolitic imidazolate frameworks (ZIFs) show high thermal, chemical, and mechanical stability. In this study, we selected ZIF-8 synthesized mechanochemically by twin-screw extrusion as powder and pellets. The hydrogen storage capacity at 77 K and up to 100 bar has been analyzed in two laboratories applying three different measurement setups showing a high reproducibility. Pelletizing ZIF-8 increases the packing density close to the corresponding value for a single crystal without loss of porosity, resulting in an improved volumetric hydrogen storage capacity close to the upper limit for a single crystal. The high volumetric uptake combined with a low and constant heat of adsorption provides ca. 31 g of usable hydrogen per liter of pellet assuming a temperature-pressure swing adsorption process between 77 K - 100 bar and 117 K - 5 bar. Cycling experiments do not indicate any degradation in storage capacity. The excellent stability during preparation, handling, and operation of ZIF-8 pellets demonstrates its potential as a robust adsorbent material for technical application in pilot- and full-scale adsorption vessel prototypes.

Investigating the mechanical stability of flexible metal-organic frameworks

As we continue to develop metal-organic frameworks (MOFs) for potential industrial applications, it becomes increasingly imperative to understand their mechanical stability. Notably, amongst flexible MOFs, structure-property relationships regarding their compressibility under pressure remain unclear. In this work, we conducted in situ variable pressure powder X-ray diffraction (PXRD) measurements up to moderate pressures (<1 GPa) using a synchrotron source on two families of flexible MOFs: (i) NU-1400 and NU-1401, and (ii) MIL-88B, MIL-88B-(CH3)2, and MIL-88B-(CH3)4. In this project scope, we found a positive correlation between bulk moduli and degree of flexibility, where increased rigidity (e.g., smaller swelling or breathing amplitude) arising from steric hindrance was deleterious, and observed reversibility in the unit cell compression of these MOFs. This study serves as a primer for the community to begin to untangle the factors that engender flexible frameworks with mechanical resilience.

In Situ Neutron Diffraction of Zn-MOF-74 Reveals Nanoconfinement-Induced Effects on Adsorbed Propene

Even though confinement was identified as a common element of selective catalysis and simulations predicted enhanced properties of adsorbates within microporous materials, experimental results on the characterization of the adsorbed phase are still rare. In this study, we provide experimental evidence of the increase of propene density in the channels of Zn-MOF-74 by 16(2)% compared to the liquid phase. The ordered propene molecules adsorbed within the pores of the MOF have been localized by in situ neutron powder diffraction, and the results are supported by adsorption studies. The formation of a second adsorbate layer, paired with nanoconfinement-induced short intermolecular distances, causes the efficient packing of the propene molecules and results in an increase of olefin density.

In Situ Observation of Solvent Exchange Kinetics in a MOF with Coordinatively Unsaturated Sites

Solvent exchange of synthesis solvent within metal-organic frameworks (MOFs) is an essential process for the activation of coordinatively unsaturated sites (CUS) to achieve an optimal surface area; activation of the CUS is required to exploit the versatile applications of MOFs. However, it is challenging to replace CUS-bound synthesis solvent prior to MOF activation, which can lead to a structural collapse and reduced surface area post-evacuation. Herein, we quantify the exchange behavior of a copper paddlewheel-based CUS-MOF (HKUST-1) in the presence of three different solvents: ethanol (EtOH), dichloromethane (DCM), and N,N-dimethylformamide (DMF). The DMF release profiles are monitored via in situ observation of the exchange solvent composition via 1H NMR and Raman spectroscopy at the macroscopic scale. Furthermore, the change in solvent within a single crystal is measured to directly elucidate the exchange behavior. We demonstrate the DMF release profile from HKUST-1 exhibits different rate laws depending on whether the solvent exchange occurs at the CUS or is purely diffusive through the pores. This contribution represents the first characterization of release from a CUS-MOF as a function exchange solvent and reveals that solvent exchange in a CUS-MOF is not diffusion-limited, but rather is limited by the solvent exchange kinetics at the metal center. Insights from this study can be generalized to the variety of copper-paddlewheel-based MOFs, informing best practices for solvent exchange.

Multidirectional Solvent-Induced Structural Transformation in Designing a Series of Polycatenated Cobalt(II) Coordination Polymers: Impact on Carbon Dioxide and Hydrogen Uptake

Coordination polymers with external stimuli-responsive structural transformation acquired paramount importance in the advanced material research field due to their eye-catching application to deal with the existing challenging issue, and Co(II) metal complex with d⁷ electronic configuration is a renowned candidate for kinetic accountability and has the potentiality of structural transformation. Bearing these factors in mind, here, a Co(II) congener of a previously reported high hydrogen-adsorbing Cu(II)-based coordination polymer (CP), {[Cu(4-bpe)(2-ntp)]}_n [where 2-ntp^2- = 2-nitroterephthalate and 4-bpe = 1,2-bis-(4-pyridyl)ethane], has been synthesized to study the metal change impact on hydrogen adsorption and solvent-induced structural transformation with their impact on hydrogen uptake. This modified framework has a 2D + 2D → 3D inclined polycatenated framework as comparable to our previously published Cu(II) framework. Here, on the variation of different solvents, the labile Co(II)-containing framework exhibits a structural change through single-crystal to single-crystal (SC-SC) structural transformation and results in three new framework structures. All four frameworks are structurally characterized by elemental analysis, IR, PXRD, TGA, and single-crystal X-ray diffraction. The desolvated parent framework with exposed metal centers exhibits excellent results of H₂ adsorption of 1.3 wt % (145 cc/g) at 77 K and pressure of 1 bar with structural sustainability and CO₂ uptake of 130 cc/g at 195 K and 1 bar. For the other three solvent-mediated structural derivatives, H₂ and CO₂ adsorption have been studied, and the results are correlated with their structure.

Pd-doped HKUST-1 MOFs for enhanced hydrogen storage: effect of hydrogen spillover

Extensive research has been devoted to developing metal nanoparticle (NP) doped porous materials with large hydrogen storage capacity and high hydrogen release pressure at ambient temperature. The ultra-sound assisted double-solvent approach (DSA) was applied for sample synthesis. In this study, tiny Pd NPs are confined into the pore space of HKUST-1, affording Pd@HKUST-1-DS with minimizing the aggregation of Pd NPs and subsequently the formation of Pd NPs on the external surface of HKUST-1. The experimental data reveal that the obtained Pd NP doped Pd@HKUST-1-DS possessed an outstanding hydrogen storage capacity of 3.68 wt% (and 1.63 wt%) at 77 K and 0.2 MPa H₂ (and 298 K and 18 MPa H₂), in comparison with pristine HKUST-1 and impregnated Pd/HKUST-1-IM. It is found that the storage capacity variation is not only ascribed to the different textural properties of materials but is also illustrated by the hydrogen spillover induced by different electron transport from Pd to the pores of MOFs (Pd@HKUST-1-DS > Pd/HKUST-1-IM), based on X-ray photoelectron spectroscopy and temperature desorption spectra. Pd@HKUST-1-DS, featuring high specific surface area, uniform Pd NP dispersion and strong interaction of Pd with hydrogen in the confined pore spaces of the support, displays the high hydrogen storage capacity. This work highlights the influence of spillover caused by Pd electron transport on the hydrogen storage capacity of metal NPs/MOFs, which is governed by both physical and chemical adsorption.

MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction

Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.