Search Results (49)

Electrochemical CO₂ reduction reactions (CO₂RR) offer a viable approach to mitigating anthropogenic CO₂ while simultaneously generating value-added chemicals. Among diverse electrocatalyst classes, metal–organic framework (MOF)-based materials have been extensively explored owing to their excellent tunability of chemical structure, high surface area, and the ability to tailor the coordination/electronic environment of active centers. This state-of-the-art review provides a critical assessment of recent progress in the development of pristine Cu-based MOFs, Cu MOF-derived catalysts, and hybrid Cu@MOFs for the selective production of C₁ products such as CO, CH₄, and formate/formic acid. Theoretical investigations into the roles of the copper center, ligands, pore structures, and interfacial effects reveal that product selectivity is influenced by more than just the oxidation state of Cu sites. Mixed-valence Cu⁺/Cu⁰ junctions, defect-rich surfaces, conductive frameworks, and coordination site tuning constitute fundamental design strategies for steering CO₂ reduction pathways. In addition to electrocatalytic performance, this review emphasizes the importance of life cycle assessment (LCA). Current studies identify electricity demand, separation steps, and operational lifetime as the dominant environmental impact factors in LCA analyses. Combining molecular-level catalyst design with systems-level sustainability considerations, this review highlights key challenges and future prospects for advancing Cu-MOF electrocatalysts toward efficient and sustainable C₁ formation from CO₂. Full article

Metal–organic frameworks (MOFs) are crystalline porous materials made of metal nodes coordinated by organic linkers. Their high surface areas, tunable pore sizes, adjustable chemical environments, and modular design make MOFs promising for two main application domains: environmental remediation and energy conversion or storage. In this review, we explore the applications of both newly designed MOFs and MOF-derived materials. These applications include catalysis, electrocatalysis, sensing, pollutant removal, batteries, supercapacitors, and other hybrid energy devices. We attempt to correlate MOF structure with key parameters, such as metal centers, ligands, defects, and porosity, to performance. We also discuss the future use of MOFs in real-world devices. This depends on overcoming challenges such as scalability, conductivity, stability, and environmental safety. Full article

Metal–organic frameworks (MOFs) exhibit high specific surface area and porosity, which may facilitate electron transfer during electrochemical reactions. Therefore, it is clear that MOFs are promising materials for the development of electrochemical sensors. In particular, zinc (Zn) based MOFs offer several advantages such as high specific surface area, porosity, environmental friendliness and low cost. Thus, Zn-based MOF materials and their composites have been extensively utilized in the detection of various pollutants, biomolecules and food additives. The Zn-MOF-based materials have been extensively utilized in electrochemical and fluorescence sensing applications. Previously, various Zn-MOF-based sensing systems such as pristine Zn-MOF, carbon-supported Zn-MOF composites, MXene hybrids with Zn-MOF, and bimetallic/trimetallic Zn-based MOFs were explored to enhance sensing performance. Such materials exhibit remarkable analytical performance, such as a low limit of detection (LOD) (nM to pM range), wide linear response range (LR), fast response times, and high selectivity in the presence of interfering species. In electrochemical sensing, Zn-MOF-modified electrodes demonstrated improved charge-transfer kinetics and sensitivity, enabling accurate determination of the biomolecules, drugs and heavy metal ions in real samples. Similarly, Zn-MOF-based fluorescence sensors showed high luminescent properties and displayed sensitive detection of pollutants and biomolecules. Despite such promising sensing performances, some challenges, such as low stability, reproducibility and selectivity in real-time monitoring, etc., remain that need to be overcome. This review article summarizes the previously reported literature on the fabrication of Zn-MOFs, their composites and Zn-MOF-derived materials for the development of electrochemical and fluorescence sensors. We have also discussed the future directions for the rational design of the high-performance Zn-MOF-based sensing systems for environmental and biomedical applications. We believe that the present review article would be useful for the scientific community working on the fabrication of Zn-MOF-based sensors. Full article

Electrochemical sensors have emerged as promising tools for rapid pesticide screening in food and environmental samples because they combine simple instrumentation, fast response, portability, and compatibility with disposable electrodes. This review organizes recent progress through a cross-system framework linking pesticide class, interfacial electrochemical process, and material design. Carbon materials, metal–organic frameworks and their derivatives, metal nanoparticles, metal compounds, conducting polymers, MXene-based composites, and selected emerging materials are compared in terms of enrichment capability, charge-transfer regulation, catalytic amplification, recognition-layer integration, and suitability for real-sample analysis. Emphasis is placed on issues that are often under-discussed in performance-centered surveys, including matrix interference, electrode fouling, batch-to-batch reproducibility, storage stability, scalability, and cost-effectiveness. Representative examples show that the most useful advances arise not simply from lowering the limit of detection but from improving structure–function understanding and translating interfacial design into robust analytical performance. Future work should prioritize standardized fabrication and benchmarking protocols, in situ and operando identification of active sites and interface evolution, matrix-specific antifouling validation, multiresidue and metabolite analysis, and hybrid portable devices coupled with intelligent readout. Full article

Textile wastewater remains one of the most challenging industrial effluents to remediate due to its intense and persistent coloration, high organic load, elevated salinity, and fluctuating pH and the presence of recalcitrant dye structures and auxiliary chemicals. Conventional physicochemical and biological treatments frequently achieve incomplete removal, generate secondary wastes, or fail under high-salt and toxic dye matrices. Advanced oxidation processes (AOPs) provide molecular-level degradation via reactive oxygen species (ROS), yet their deployment is often constrained by narrow operating windows, catalyst instability, chemical/energy demand, and scale-up limitations. In this context, metal–organic frameworks (MOFs) have emerged as tunable porous catalytic platforms that integrate adsorption and oxidation within a single architecture through controllable metal nodes, functional linkers, and engineered pore environments. This critical review reimagines textile effluent treatment through the lens of MOF-based hybrid catalysts, synthesizing progress across Fenton/photo-Fenton catalysis, photocatalytic MOFs, persulfate activation, and MOF-derived/composite systems. Mechanistic pathways are discussed by linking pollutant enrichment, cyclic redox reactions, charge-transfer processes, and ROS-driven degradation toward mineralization, with emphasis on the distinction between rapid decolorization and true organic removal. A critical comparison highlights how hybridization improves charge transport, stability, and catalyst recovery, while persistent gaps remain in hydrolytic robustness, metal leaching control, intermediate toxicity assessment, real-wastewater validation, continuous-flow reactor integration, and techno-economic feasibility. Finally, the review outlines actionable research directions, including water-stable and defect-engineered MOFs, immobilized and structured catalysts, solar-driven operation, standardized performance metrics, and life-cycle-informed design, to accelerate translation toward scalable and sustainable textile wastewater remediation. By bridging material chemistry with reactor-level feasibility and sustainability assessment, this review provides an implementation-oriented perspective for next-generation textile wastewater treatment. Full article

Heavy metal contamination of drinking water remains a persistent global challenge, exacerbated by salinity, industrial discharge, and the limitations of existing membrane technologies that are constrained by permeability–selectivity trade-offs. In this study, we develop a hybrid thin film nanocomposite (TFN) forward osmosis (FO) membrane by incorporating a zirconium-based metal–organic framework (UiO-66) and its conductive polymer-functionalized analogue (PANI@UiO-66) into the polyamide active layer via interfacial polymerization. The incorporation of UiO-66 enhances water transport through the introduction of hydrophilic microporous domains, while the polyaniline coating modulates nanoscale transport pathways and interfacial interactions. Systematic variation in filler type and loading reveals distinct functional roles of the two fillers. Membranes incorporating bare UiO-66 exhibit increased water flux, attributed to facilitated transport through MOF-derived nanochannels, but show a moderate increase in reverse solute flux. In contrast, PANI@UiO-66 incorporation results in reduced water flux but significantly suppresses reverse solute flux and enhances chromium rejection, indicating improved control over selective transport. At an optimal loading of 0.15 wt% (TFN-PU3), the membrane demonstrates an improved balance between water permeability and solute selectivity compared to the pristine thin film composite (TFC) membrane under FO conditions. The observed performance is attributed to the combined effects of modified transport pathways and interfacial interactions introduced by the hybrid filler system. The results highlight the potential of conductive polymer–MOF hybridization as a strategy for tuning membrane performance. This work provides a practical framework for designing TFN membranes for selective heavy-metal removal in saline and complex water environments. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Low-temperature (including room-temperature) gas sensors are crucial for energy-efficient and safe detection applications. In this study, we report the synthesis of In₂O₃-sensitized NiO nanoparticles (NPs) for NO₂ detection. The NiO/In₂O₃ hybrid materials were obtained by pyrolysis of Ni/In bimetallic metal–organic framework (MOF) nanosheets (NSs) fabricated through ultrasonic synthesis and cation exchange. Gas sensing tests revealed that the In₂O₃ sensitization significantly enhances the NO₂ sensing performance of NiO, enabling a response of 1.5 at room temperature (RT) and an optimal response at 100 °C. The NiO/In₂O₃ sensor demonstrates enhanced selectivity toward NO₂, an ultra-low detection limit (41 ppb), and long-term stability. This study presents an effective MOF-derived route for developing high-performance low-power gas sensors. Full article

Metal–organic frameworks (MOFs) have emerged as versatile precursors and templates for developing high-performance electrode materials for aqueous zinc-ion batteries (ZIBs), owing to their adjustable porosity, abundant metal-coordination sites, and structural flexibility. Among the diverse array of MOFs investigated, those based on manganese, copper, and cobalt, as well as their derivatives, have shown exceptional potential, exhibiting enhanced redox activity, structural integrity, and advantageous zinc-ion storage kinetics compared with many other MOF systems. This study emphasizes the synthesis methodologies, structural characteristics, and electrochemical benefits of these three significant MOF families. After a succinct overview of MOF chemistry, synthesis methodologies, and fundamental design principles for ZIB electrode materials, the article presents a systematic, comparative evaluation of Mn-MOFs, Cu-MOFs, Co-MOFs and V-MOFs, along with their corresponding metal oxides, sulfides, phosphates, carbon composites, and multidimensional hybrid structures. Recent publications for each MOF type are detailed in separate tables, including synthesis methods, morphological development, electrochemical behavior, and performance metrics. The discourse highlights the distinct properties of each metal center, Mn’s multivalent redox chemistry, Cu’s superior electron transport and coordination adaptability, and Co’s elevated activity and stable structures, which together facilitate improved ion diffusion, substantial reversible capacity, and prolonged cycling durability. Ultimately, existing obstacles and potential research avenues are delineated to advance MOF-based materials for next-generation aqueous ZIB systems. Full article

Generalized censoring, combined with a power-based distribution, improves inferential efficiency by capturing more detailed failure-time information in complex testing scenarios. Conventional censoring schemes may discard substantial failure-time information, leading to inefficiencies in parameter estimation and reliability prediction. To address this limitation, we develop a comprehensive inferential framework for the alpha-power Weibull (APW) distribution under a generalized progressive hybrid Type-II censoring scheme, a flexible design that unifies classical, hybrid, and progressive censoring while guaranteeing test completion within preassigned limits. Both maximum likelihood and Bayesian estimation procedures are derived for the model parameters, reliability function, and hazard rate. Associated uncertainty quantification is provided through asymptotic confidence intervals (normal and log-normal approximations) and Bayesian credible intervals obtained via Markov chain Monte Carlo (MCMC) methods with independent gamma priors. In addition, we propose optimal censoring designs based on trace, determinant, and quantile-variance criteria to maximize inferential efficiency at the design stage. Extensive Monte Carlo simulations, assessed using four precision measures, demonstrate that the Bayesian MCMC estimators consistently outperform their frequentist counterparts in terms of bias, mean squared error, robustness, and interval coverage across a wide range of censoring levels and prior settings. Finally, the proposed methodology is validated using real-life datasets from engineering (electronic devices), clinical (organ transplant), and physical (rare metals) studies, demonstrating the APW model’s superior goodness-of-fit, reliability prediction, and inferential stability. Overall, this study demonstrates that combining generalized censoring with the APW distribution substantially enhances inferential efficiency and predictive performance, offering a robust and versatile tool for complex life-testing experiments across multiple scientific domains. Full article

Supercapacitors are widely studied for their high energy density, low cost, and exceptional cycling durability. However, the decisive factor in determining the performance of supercapacitors is the electrode material. Among emerging materials, metal glycerates stand out as tunable organic-inorganic hybrids with well-controlled structures. Yet, progress in tailoring metal glycerates for supercapacitors has not been organized or consolidated into a coherent framework. Herein, we systematically summarize recent advances in the synthesis, structural evolution, and electrochemical applications of metal glycerates and their derivatives (including hydroxides, oxides, sulfides, phosphides, selenides, and composites) as electrodes for supercapacitors, emphasizing the intrinsic structure-performance correlations. Finally, the key challenges and future prospects, covering controlled synthesis, interfacial stability, mechanistic insight, and device-level integration, are discussed to guide the rational design of next-generation MG-based materials for high-performance, sustainable supercapacitor technologies. Full article

Metal–organic frameworks (MOFs) are novel porous crystalline materials formed through the self-assembly of metal ions and organic ligands. They have various advantages, including tunable chemical and electronic structures, high porosity, and large specific surface areas. Owing to their unique structural and physicochemical properties, MOFs have been widely applied in the fields of catalysis, supercapacitors, sensors, and drug recognition/delivery. However, the intrinsic poor stability and low electrical conductivity of conventional MOFs severely hinder their practical implementation. Graphdiyne (GDY), a unique carbon allotrope, features a new structure composed of both sp²- and sp-hybridized carbon atoms. Its distinct chemical and electronic configuration endow it with exceptional properties such as natural bandgap, uniform in-plane cavities, and excellent electronic conductivity. Integrating MOFs with GDY can effectively overcome the intrinsic limitations of MOFs and expand their potential applications. As emerging hybrid materials, MOF/GDY composites and their derivatives have attracted increasing attention in recent years. This article reviews recent advances in the synthesis strategies of MOF/GDY composites and their derivatives, along with their performance and applications in catalysis, energy storage, and biological sensors. It also discusses the future opportunities and challenges faced in the development of these promising composite materials, aiming to inspire interest and provide scientific guidance. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 s, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM), and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

Gallic acid (GA), a polyphenol extensively used in the food, wine, and pharmaceutical industries, is known for its inhibitory effects on soil microbial activity. Photocatalytic degradation offers an environmentally friendly solution for GA removal from water. In this work, graphitic carbon nitride (g-C₃N₄) photocatalysts were synthesized by two methods: thermal exfoliation (CN-E) and supramolecular assembly via hydrothermal processing (HCN-II). Structural analyses by XRD, FTIR, and XPS confirmed the formation of the g-C₃N₄ framework, while SEM revealed that CN-E consisted of folded and curled nanosheets, whereas HCN-II displayed a polyhedral–nanosheet hybrid architecture with internal channels. Both materials achieved approximately 80% GA degradation within 180 min under visible-light irradiation, yet HCN-II exhibited a superior apparent rate constant (k = 0.01156 min⁻¹) compared with CN-E. Radical trapping experiments demonstrated that O₂•⁻ and h⁺ were the primary reactive oxygen species involved, with OH• making a minor contribution. The enhanced performance of HCN-II is attributed to its higher surface area, improved light harvesting, and efficient charge separation derived from supramolecular assembly. These findings highlight the potential of engineered g-C₃N₄ nanostructures as efficient, metal-free photocatalysts for the degradation of recalcitrant organic pollutants in water treatment applications. Full article

Due to its sarcoma-derived origin and the associated carcinogenic risks, as well as its lack of tissue-specific extracellular matrix biochemical cues, the use of the injectable gel scaffold Matrigel is generally restricted to research applications. Therefore, the development of new fully synthetic injectable gel scaffolds that exhibit performance comparable to Matrigel is a high priority. In this study, we developed a novel fully synthetic injectable gel scaffold by combining a biodegradable PLGA-PEG-PLGA copolymer, clay nanoparticle LAPONITE®, and L-arginine-loaded metal–organic frameworks (NU-1000) at the nano level. An aqueous solution of the developed hybrid scaffold (PLGA-PEG-PLGA/LAPONITE®/L-Arg@NU-1000) exhibited rapid sol–gel transition at body temperature following simple injection and formed a continuous bulk-sized gel, demonstrating good injectability. Long-term sustained slow release of L-arginine from the resultant gels can be achieved because NU-1000 is a suitable reservoir for L-arginine. PLGA-PEG-PLGA/LAPONITE®/L-Arg@NU-1000 hybrid gels exhibited good compatibility with and promoted the growth of human skeletal muscle satellite cells. Importantly, in vivo experiments using skeletal muscle injury model mice demonstrated that the tissue regeneration efficiency of PLGA-PEG-PLGA/LAPONITE®/L-Arg@NU-1000 gels is higher than that of Matrigel. Specifically, we judged the higher tissue regeneration efficacy of our gels by histological analysis, including MYH3 immunofluorescent staining, H&E staining, and Masson’s trichrome staining. Taken together, these data suggest that novel hybrid hydrogels could serve as injectable hydrogel scaffolds for in vivo tissue engineering and ultimately replace Matrigel. Full article