Search Results (747)

Hydrogen (H₂) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H₂ remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H₂ storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H₂ adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H₂ uptake. This offers engineers and researchers a practical means of screening potential MOFs for H₂ storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts. Full article

Metal–organic frameworks (MOFs) based on triazole have attracted considerable interest as promising porous materials for CO₂ capture due to their high surface area, ultramicroporosity, and excellent thermal and chemical stability. Nitrogen-rich triazole ligands contain abundant Lewis basic sites that promote CO₂ adsorption via dipole–quadrupole interactions, hydrogen bonding and cooperative interactions with open metal sites. The present review discusses recent developments in the synthesis of triazole-based MOFs, with special emphasis on the relation between structural features and CO₂ adsorption performance. The paper reviews different synthetic routes such as solvothermal, hydrothermal, mechanochemical and post-synthetic modification methods and their impact on crystallinity, porosity and scalability. The roles of metal centres, pore confinement and linker functionalization in tuning CO₂ uptake, selectivity and adsorption energetics are highlighted. Moreover, the mixed-linker strategies and defect engineering are explored to illustrate the use of the synergistic effect of nitrogen-rich sites and metal nodes for the improvement of the adsorption performance. Still, a number of challenges remain such as achieving an optimal balance between adsorption strength and regenerability, increasing stability in humid and realistic flue-gas conditions, and the development of scalable and sustainable synthesis routes. In summary, triazole-based MOFs provide a versatile platform for the design of high-performance CO₂ adsorbents by combining structural robustness with chemically active, nitrogen-rich adsorption environments. Full article

Metal–organic frameworks (MOFs) have emerged as a versatile class of porous nanomaterials with exceptional surface area, tunable pore architectures, and customizable chemical functionalities, creating new opportunities for advanced food applications. Increasing demand for functional foods, precision fermentation, and sustainable bioprocessing has stimulated interest in MOFs as multifunctional platforms for microbial encapsulation, biocatalyst stabilization, and resource recovery. This review examines recent advances in the design and application of MOFs for probiotic delivery, precision fermentation, and circular food systems. The relationships between MOF structure, physicochemical properties, and functional performance are discussed in the context of probiotic encapsulation, protection against environmental and gastrointestinal stress, and controlled release within the intestinal tract. Emerging applications in precision fermentation are evaluated, including microbial immobilization, enzyme stabilization, metabolite separation, and bioprocess intensification. The potential of MOFs to enable circular food systems through the valorization of fermentation by-products, nutrient recovery, and waste-to-value strategies is also assessed. Despite significant progress, challenges related to biocompatibility, food-grade synthesis, scalability, regulatory approval, and long-term safety continue to limit industrial implementation. Future research directions include the development of sustainable and biodegradable MOFs, data-driven material design, and standardized evaluation frameworks to accelerate the translation of MOF-enabled technologies from laboratory research to commercial food applications. Full article

Hydrogen, as a clean and renewable energy carrier, offers a promising solution to the global energy challenge, yet its safe and efficient storage remains a critical bottleneck. Metal–organic frameworks (MOFs), with their ultrahigh surface area, tunable porosity, and excellent stability, have emerged as leading candidates for physical hydrogen storage. This review systematically surveys recent progress in MOF-based hydrogen storage, organized by metal center type and examines the distinct adsorption mechanisms that govern hydrogen uptake. The regulatory effects of critical parameters including metal ion selection, pore architecture, and ligand functionalization on hydrogen storage capacity are analyzed in detail. Beyond material-level discussion, this review discusses the potential of MOFs for cryo-compressed hydrogen storage conditions. Key challenges facing practical deployment, including synthesis scalability, structural stability under cryogenic high-pressure cycling, and the knowledge gap in multi-cycle temperature-swing stability, are critically assessed. The roles of computational simulations and machine learning in accelerating MOF discovery and high-throughput screening are also reviewed. Finally, an application-oriented outlook is presented, mapping MOF performance to three specific industrial scenarios with reference to relevant economic analyses, thereby bridging fundamental materials chemistry with practical engineering requirements. Full article

Metal–organic framework (MOF)-derived materials have recently emerged as promising sensing materials because of their tunable composition, porous architecture, high surface area, and defect-rich structures. Therefore, MOF-derived materials have significantly attracted the scientific community to design and fabricate triethylamine (TEA) gas sensors. TEA is a toxic, volatile, and malodorous amine that is widely released from industrial processes, food spoilage, and environmental sources. The selective and sensitive detection of TEA is of great importance for health, safety, and environmental monitoring. Previous years have witnessed rapid growth in the development of MOF-derived materials based on TEA gas sensors. This review critically evaluates recent progress in the fabrication of MOF-derived metal oxides, mixed-metal oxides, doped systems, noble-metal-functionalized materials, carbon-containing composites, MXene-integrated architectures, and heterojunction-based TEA gas sensors. The response, selectivity, stability, and sensing mechanisms for TEA gas sensors are discussed. Furthermore, challenges and perspectives are discussed. We believe that this review may be beneficial for those actively working in the fabrication of MOF-based TEA gas sensors. Full article

Background: Metallic orthopedic implants face persistent clinical challenges that have proved resistant to incremental conventional development. Machine learning and artificial intelligence offer a complementary paradigm for navigating the high-dimensional design spaces governing implant performance, yet the literature remains fragmented across disciplinary silos with no comprehensive synthesis spanning the full development pipeline. Methods: A structured database search of PubMed/MEDLINE, Embase, and Cochrane (executed May 2026), supplemented by hand-searching of reference lists, identified 33 primary studies organized across five sequential domains: alloy composition discovery, additive manufacturing process–property optimization, lattice and porous structure design, surface engineering and coatings, and corrosion and wear prediction. Results: Across all five domains, machine learning approaches, including random forests, convolutional neural networks, Bayesian optimization, generative adversarial networks, physics-informed neural networks, and autonomous multi-agent platforms, have accelerated property prediction and design space exploration beyond experimental or simulation-based methods. Shared barriers to translation include small, heterogeneous datasets, reliance on internal rather than external validation, limited interpretability, and the absence of regulatory frameworks for AI-assisted device design. Representative performance included modulus predictions within ~4 GPa of first-principles values, ML-designed alloys reaching ~42.7 GPa (versus 103–120 GPa for Ti-6Al-4V), property prediction R² often above 0.90 (up to 0.96–0.9991), 98.3% corrosion severity classification accuracy, and acceleration from a roughly fivefold reduction in finite element simulations to surrogates compressing days into minutes. Conclusions: Addressing these limitations will require open standardized databases linking materials parameters to registry-level clinical outcomes, prospective clinical validation studies, and coordinated engagement between researchers, industry, and regulatory agencies. Full article

Silica–starch composites were synthesized via a modified sol–gel route and evaluated as low-cost, biodegradable adsorbents for the removal of organic dyes from aqueous solutions. The formation of mesoporous hybrid networks and structural reorganization of starch upon integration with the silica phase were confirmed. The physicochemical properties of the composites were examined using nitrogen adsorption–desorption analysis, SEM imaging, SAXS, and XRD, providing complementary insights into their porous structure, morphology, and structural organization. Adsorption performance and kinetics were evaluated through continuous UV–Vis spectroscopic monitoring of dye concentration during the sorption process. Adsorption studies using methylene blue demonstrated that dye uptake proceeds through a combination of electrostatic interactions, hydrogen bonding between dye molecules and hydroxyl-rich starch domains, and diffusion-driven retention within the mesoporous silica framework. The proposed materials offer significant advantages arising from their simple, inexpensive synthesis and fully biodegradable nature. These features position silica–starch composites as sustainable sorbents suitable for environmentally oriented water purification applications. Full article

Wastewater containing heavy metals and dyes poses serious environmental risks. This study developed a multifunctional composite by coating polyurethane foam (PUF) with milled activated carbon (mAC) and a zirconium-based metal–organic framework (Zr-MOF) for the simultaneous removal of hexavalent chromium (Cr(VI)) and Congo red (CR). The composite was synthesized using a hydrothermal method to grow Zr-MOF on the surface. The SEM analysis confirmed the successful incorporation of mAC and surface modification with Zr-MOF, which resulted in increased surface roughness and porous morphology. XRD and FTIR confirmed the presence of organic ligands connected to the metal structure and the functional groups of each component in composite materials. The optimum conditions for Zr-MOF/mAC/PUF adsorption (nearly 100% removal) in the binary Cr(VI)/CR solution (50 mg/L each) were 25 °C, pH 9, and 150 rpm for 24 h. The Zr-MOF/mAC/PUF was hydrophilic with a swelling ratio of 2.64 g/g. The thermodynamic investigation of Zr-MOF/mAC/PUF resulted in 141.6218 kJ/mol for Cr(VI) and 166.111 kJ/mol for CR of ΔH° (rapid adsorption), negative ΔG° (spontaneous adsorption), a high positive value of ΔS° (disorder structure) and low activation energy (approximately 2.5 to 2.8 kJ/mol). After analyzing the isotherm and reaction kinetics, the possible mechanism could be endothermic physicochemical adsorption and pseudo-second-order kinetic behavior, with electrostatic attraction and diffusion control. The study of 6-times-reused Zr-MOF/mAC/PUF adsorption identified as a decrease of 7.55 percentage point without changing notable morphology and functional groups, based on SEM and FTIR. Full article

Photocatalytic CO₂ reduction into CH₄ is a promising route for solar fuel production, but its efficiency is still limited by poor charge separation, insufficient CO₂ activation, and sluggish multi-electron transfer kinetics. Herein, Cu-modified TiO₂ (Cu/TiO₂) was prepared by calcining a Cu-modified defective MIL-125(Ti) precursor, denoted as Cu-MIL-125, through a temperature-controlled calcination strategy. The effects of calcination temperature on the structural evolution, surface chemical states, interfacial charge transport, and CO₂ photoreduction performance were examined. These results indicated that the Cu/TiO₂ was successfully prepared, while the crystallinity, porous structure, and interfacial electronic properties of Cu/TiO₂ were strongly dependent on the calcination temperature. Among the obtained samples, the Cu/TiO₂ sample obtained by calcining Cu-MIL-125 at 450 °C (450 Cu/TiO₂) exhibited the highest CH₄ formation rate, reaching 15.90 μmol g⁻¹ h⁻¹, corresponding to an approximately 9.8-fold enhancement over TiO₂ calcined from defective MIL-125(Ti) at 450 °C, together with a high CH₄ selectivity of 93.05%. Control experiments and ¹³CO₂ isotope-labeling tests confirmed that the detected carbon-containing products were generated from CO₂ under photocatalytic conditions. In situ diffuse reflectance infrared Fourier transform spectroscopy measurements further revealed the formation of carbonate, bicarbonate and hydrogenated carbon-containing intermediates during the reaction. This work offers a practical route for constructing metal–organic framework-derived Cu/TiO₂ photocatalysts for selective CH₄ production from CO₂. Full article

Porous coordination polymers, alternatively known as metal–organic framework (MOF) nanoparticles, have acquired increasing significance in nanomaterials science, especially with the increased importance and versatility in catalysis. The complex structures of MOFs allow the incorporation of metal nodes, enclosing substrates, and functional linkers, thus enabling synergistic structural and functional engineering to produce capable catalytic active sites that provide solutions to decrease human activities in designing new organic reactions. Recently, Cu-MOF-mediated organic reactions hold a significant promise to substitute homogenous and heterogeneous catalysts due to their promising structural features such as tailorable porous structures, high-density catalytic active sites and surface area, sufficient framework stability, minimal leaching, and facile recovery and recyclability. This review emphasizes the significance of Cu-MOFs in synthetic chemistry, in particular, in the synthesis of organic compounds. It examines their applicability in hydrogenation, oxidation, cross-coupling/condensation reactions, functionalization at terminal alkenes and alkynes, intramolecular C-H amination, and other multicomponent reactions. In addition to these organic transformations, recent progress in Cu-MOF-catalyzed CO₂ electroreduction and nitrate reduction is also briefly described. Subsequently, the state-of-the-art synthetic methods of certain decorated Cu-MOFs are thoroughly elaborated as well as the essential structural parameters that govern the stability and recyclability of MOFs in organic transformations. This focused examination of Cu-MOFs is expected to provide useful information for future research endeavors in the field of MOF catalytic applications. Full article

Photocatalytic extraction of uranium from radioactive wastewater is crucial for environmental safety and sustainable nuclear energy development. It is widely recognized that photocatalysts with donor-acceptor (D-A) or D-π-A structures exhibit enhanced charge separation efficiency, thereby showing excellent photocatalytic performance. Herein, we presented a counterintuitive design of a donor-donor covalent-organic framework (D-D COF) for efficient photocatalytic uranium extraction. A twisted D-D COF (COF-BCTB-Py) was synthesized via solvothermal condensation using bicarbazole and pyrene as dual electron-donor units. The COF featured a well-defined AA-stacked porous structure, high specific surface area (963 m²·g⁻¹), suitable band gap (2.44 eV), and exceptional chemical, thermal, and radiation stability. Impressively, in the presence of 5% methanol, it delivered an ultrahigh uranium uptake capacity of 4278 mg·g⁻¹ with fast kinetics and >97% removal efficiency in complex water matrices, challenging the traditional stereotype of low-activity D-D COFs. Mechanistic studies revealed that soluble U(VI) was converted into crystalline (UO₂)O₂·2H₂O via in situ generated hydrogen peroxide rather than being reduced to U(IV). This work provides an unconventional design strategy to design efficient photocatalysts for uranium recovery from nuclear wastewater. Full article

Electrical measurements of emerging materials such as thin films, two-dimensional materials, and fragile porous systems are often hindered by damage and contamination caused by conventional contact methods, including metal electrode deposition. In this study, we demonstrate the novelty and advantages of a mechanically compliant “soft probe” over conventional methods and conductive AFM. The non-destructive soft probe achieves stable electrical contact in the repulsive-force regime using a hairpin-shaped spring structure, allowing consistent measurements without active force control nor electrode fabrication. Case studies demonstrate that the soft probe prevents metal penetration and preserves intrinsic properties, as demonstrated in NiO resistive switching devices, and improves interface quality compared to deposited electrodes in ferroelectric measurements. It also enables electrical characterization of fragile materials such as metal–organic frameworks without inducing structural degradation. Furthermore, its mechanical compliance ensures stable operation under vibration and thermal stress, enabling measurements in vacuum and low-temperature environments. These results indicate that the soft probe provides a simple, versatile, and contamination-free platform for reliable electrical measurements, and represents a promising approach for the characterization of a wide range of emerging material systems. Full article

Carbon dioxide (CO₂) capture through adsorption using porous materials has emerged as a promising strategy for mitigating industrial greenhouse gas emissions. However, selecting an optimal adsorbent material under varying operating conditions remains a complex and time-consuming process when relying solely on experimental studies. In this project, a machine-learning-based framework is developed to predict CO₂ adsorption capacity and identify the most suitable adsorbent material using process and material parameters. A comprehensive dataset was constructed comprising multiple classes of adsorbent materials including activated carbon, zeolites, metal–organic frameworks (MOFs), porous organic polymers (POPs), alumina/silica, and amine-functionalized sorbents. The dataset includes key parameters such as temperature, pressure, CO₂ mole fraction, humidity, BET surface area, micropore characteristics, amine loading, heat of adsorption, particle density, pellet diameter, and bed void fraction. Two machine learning models based on the XGBoost algorithm were implemented. An XGBoost Regressor was used to predict the experimental CO₂ adsorption capacity, while an XGBoost Classifier was trained to identify the type of adsorbent used based on the input parameters. The models were trained and validated using a train–test split approach to ensure reliable performance evaluation. The results demonstrate that gradient boosting models can accurately capture complex nonlinear relationships between adsorption conditions, material properties, and adsorption performance. The developed framework provides a fast and efficient predictive tool that can assist researchers and engineers in screening adsorbent materials and optimizing CO₂ capture systems for industrial applications. Using this model, one can predict the adsorption capacity of any adsorbent used in the training dataset and predict its type with 95% accuracy. Full article

The development of highly sensitive luminescence sensing materials has attracted much attention in recent years. In this study, a new two-dimensional porous europium metal–organic framework (EuMOF, [Eu(DHDA)₁.₅·3H₂O]_n; DHDA = 2,2-dihydroxyacetic acid) is obtained, characterized by single-crystal X-ray diffraction, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), luminescence, and Fourier transform infrared spectroscopy (FT-IR). At the best excitation at 295 nm, EuMOF shows red luminescence (CIE: 0.6255, 0.3740) and has four obvious peaks at 582, 605, 641, and 689 nm, which are due to ⁵D₀ → ⁷F₁, ⁵D₀ → ⁷F₂, ⁵D₀ → ⁷F₃, and ⁵D₀ → ⁷F₄ transitions, respectively. Studies have shown that EuMOF is a stable, fast-responding, and highly sensitive luminescence sensor for isoprocarb and levofloxacin (Lvx) in aqueous solutions, apple peel and rice extract solutions, and real urine, which are closely associated with food safety and human health. The sensing behavior toward isoprocarb and Lvx may be attributed to the specific binding of the two analytes to EuMOF. The sensing of isoprocarb is a dynamic luminescence-quenching process, while that of Lvx is a dynamic luminescence-enhancing process. The limits of detection (LOD) for isoprocarb and Lvx are as low as 1.0 and 0.5 nM, respectively, which are much lower than the Chinese national standard (GB 28260-2011, 2.583 μM). EuMOF also demonstrates strong anti-interference detection of isoprocarb in apple peel and rice extract solutions, as well as Lvx in real urine, with excellent detection stability in a 0.01~9.0 nM range. The recovery rates for isoprocarb and Lvx in real samples are 99.12%~101.25%. This work provides the first bifunctional lanthanide sensor for pesticides and antibiotics. Full article

The increasing concentration of atmospheric CO₂ has intensified the urgent need for efficient and sustainable carbon capture technologies. Covalent organic frameworks (COFs) have emerged as a highly promising class of porous crystalline materials for CO₂ adsorption and separation owing to their structural tunability, high surface area, and precisely designable pore environments. This review summarizes recent advances in COF-based CO₂ capture systems, covering pristine COFs, functionalized frameworks, composite materials, and membrane-based architectures. In pristine COFs, CO₂ adsorption is mainly governed by micropore confinement and physisorption within well-defined channels, where surface area and pore size distribution play key roles. Functionalized COFs introduce additional active sites, including amine groups, heteroatoms, ionic functionalities, and alkali metal centers, which significantly enhance CO₂ affinity through stronger electrostatic and acid–base interactions, often leading to mixed physisorption–chemisorption behavior. Composite COFs and mixed-matrix membranes further improve performance through synergistic effects, interfacial engineering, and enhanced mass transport. Despite these advantages, challenges remain in achieving an optimal balance between capacity, selectivity, and regenerability under realistic conditions such as humidity, low CO₂ partial pressure, and multicomponent gas streams. Issues related to scalable synthesis, structural stability, and processability also limit practical applications. Overall, this review highlights key structure–property relationships and outlines future directions, including humid-stable COFs, direct air capture, computational design strategies, and advanced membrane technologies, for next-generation CO₂ capture materials. Full article