Search Results (3,216)

Enzyme immobilisation within Metal–organic Frameworks (MOFs) provides a promising strategy for improving enzyme dispersion and local environment control, although the resulting performance depends strongly on the host materials, enzyme type and immobilisation conditions. Conventional in situ biomimetic mineralisation typically produces enzyme–MOF composites (enzyme@MOFs) with irregular morphologies, broad particle size distributions and aggregation, which can compromise catalytic performance and reproducibility. This study presents a flow-based microfluidic biomimetic mineralisation strategy for preparing horseradish peroxidase-encapsulated ZnBDC-NH₂ MOF composites. A flow-focusing microfluidic chip containing multiple rectangular baffle structures was designed to enhance transverse mixing, extend the effective residence time, and mitigate clogging during particle formation. Under the selected conditions, homogeneous HRP@ZnBDC-NH₂ particles with an average hydrodynamic diameter of 868.5 nm and a polydispersity index of 0.266 were obtained. The homogeneous HRP@ZnBDC-NH₂ showed an encapsulation efficiency of 56.17% and a loading content of 1.49%. Michaelis–Menten analysis gave a Km value of 52.49 μM for HRP@ZnBDC-NH₂, suggesting improved apparent substrate affinity compared with the corresponding bulk-synthesised sample. The results support the use of baffle-structured microfluidics as a controllable platform for enzyme@MOF synthesis, while further studies on enzyme leaching, reusability, long-term stability and extended chip operation are required to evaluate its operational robustness. Full article

Heavy metals are persistent environmental contaminants that accumulate in soils and vegetation, posing significant risks to ecological systems and human health. Pinus nigra needles are widely recognized as effective biomonitors for reflecting spatial and temporal variations in atmospheric heavy metal deposition. However, the complex, nonlinear interactions among multiple pollutants, environmental factors, and site-specific conditions limit the effectiveness of conventional statistical approaches in accurately modeling and predicting contamination patterns. This study investigated the spatial and seasonal distribution of heavy metals in soils and Pinus nigra needles across different environmental settings in Adıyaman, Türkiye, including urban traffic zones, an organized industrial area, a cement factory vicinity, and a clean reference site. Metal concentrations were determined using inductively coupled plasma mass spectrometry (ICP–MS) following standardized acid digestion procedures. To address the limitations of traditional methods and capture complex nonlinear relationships, advanced machine learning (ML) algorithms—multilayer perceptron, Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting—were employed to model elevation based on heavy metal concentrations. The dataset was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using R², RMSE, MAE, MAPE, and EVS. Among the models, XGBoost exhibited superior predictive performance. Excluding Cd, Cr, and Cu, it achieved R² = 0.9996 (RMSE = 0.068) in training and R² = 0.9526 (RMSE = 17.77) in testing. Including these metals further improved performance to R² = 0.9999 (RMSE = 0.054) for training and R² = 0.9890 (RMSE = 5.55) for testing. The results confirm that Pinus nigra needles are reliable bioindicators of heavy metal accumulation. More importantly, the integration of biomonitoring data with ML techniques provides a powerful framework for capturing complex environmental interactions and improving predictive accuracy, thereby supporting more effective environmental monitoring, risk assessment, and sustainable management strategies. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article

The accurate discrimination of structurally analogous neurotransmitters remains a formidable challenge due to their high structural similarity and overlapping chemical properties. To address the limitations of low specificity in single-probe sensors and the fabrication complexity of multi-component arrays, we developed a simplified fluorescence sensing array based on a single pyrene-functionalized MOF, In-TBAPy. This strategy leverages the distinctive monomer-to-excimer luminescence transition of In-TBAPy, triggered by the tunable π-π stacking of pyrene units within the crystalline framework. The results demonstrate that the array, integrated with Linear Discriminant Analysis (LDA) across four optimized emission channels, achieves a classification accuracy of 93.75% in identifying four highly similar neurotransmitters: serotonin (5-HT), dopamine (DA), adrenaline (A), and norepinephrine (NA). Notably, the sensing platform exhibits exceptional robustness in simulated physiological environments and complex multi-analyte mixtures, enabling reliable quantitative analysis: 0–100 μM for 5-HT and adrenaline (A), 0–40 μM for dopamine (DA), and 0–80 μM for norepinephrine. Mechanistic studies suggest that the differential quenching of monomer and excimer peaks stems from the synergistic effect of competitive absorption and host–guest interactions. This work effectively overcomes the cross-interference issues of traditional sensors and validates a high-efficiency solution for high-throughput neurotransmitter analysis using a single-material-based array strategy, significantly reducing operational costs and preparation time. Full article

Background: Copper is a trace element involved in key physiological and biochemical processes in humans, animals, and plants, and its homeostasis is tightly regulated. However, excessive exposure may cause adverse health and environmental effects. Copper-based plant protection products contribute to dietary exposure to copper and remain indispensable for the control of several crop diseases. Methods: This review summarizes the current state scientific and regulatory evidence on agricultural copper, identifies major knowledge gaps, and discusses strategies to support its sustainable use. A structured literature review was conducted using the PECO framework to define eligibility criteria, while key principles of the PRISMA 2020 statement were applied to improve the transparency of study identification and selection. Results: The available evidence confirms the dual role of copper as an essential micronutrient and a potentially toxic element, with adverse effects depending on exposure level, chemical form, bioavailability, and physiological status. Although copper remains an effective agricultural tool, long-term use may promote soil accumulation and environmental impacts. Current environmental risk assessment frameworks, originally developed for organic chemicals, do not fully account for the environmental behavior of inorganic metals. The review also examines copper occurrence, biological functions, agricultural uses, environmental fate, toxicity, and regulatory frameworks. Conclusions: Sustainable copper use requires balancing crop protection benefits with human health and environmental protection. Dietary exposure from authorized copper-based PPPs is generally considered negligible according to current EFSA PRIMo assessments, whereas occupational exposure and environmental accumulation remain important concerns. Future environmental risk assessment frameworks should better account for the environmental fate, natural background concentrations, bioavailability, and speciation of inorganic metals such as copper, thereby improving the scientific basis of regulatory decision-making. 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

The spiroindoline framework is a privileged scaffold in medicinal chemistry, appearing in natural products and in synthetic bioactive compounds, such as BAY 1214784, RO8994, and RK-287107, with reported activities ranging from antimitotic effects to kinase inhibition. This review covers the methods developed between 2020 and 2025 for constructing spiroindoline frameworks, organized first by the site of spirocyclization (C2 versus C3 of the indole) and then by catalyst class: second- and third-row transition metals, first-row transition metals and main-group Lewis acids, organocatalysis, and visible-light photoredox. For each method we discuss the reaction design, the accessible substrate scope, and mechanistic insights, with particular attention to how stereochemistry is controlled. We also highlight representative downstream transformations that demonstrate the synthetic utility of the produced spiroindolines. Progress over the past five years has been substantial, particularly in enantioselective methods that create a single stereocenter and in cascade designs that build complex polycyclic frameworks in a single operation. Asymmetric construction of multiple adjacent stereocenters, gram-scale demonstrations, and genuinely sustainable conditions remain less developed; these areas are priorities for future work. Full article

Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field’s evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010–2026 and retained 730 valid records from 1581 initial publications after screening. VOSviewer, CiteSpace, and R were used to analyze publication growth, collaboration networks, journal sources, and thematic evolution. Results show that annual output remained generally below 20 before 2019, then increased rapidly and reached approximately 280 publications in 2025, indicating accelerated integration of machine learning with adsorption simulation, material screening, and performance evaluation. The source distribution broadened from a limited set of chemistry and engineering journals to diverse venues, with recent high publication weights in Chemical Engineering Journal, Separation and Purification Technology, ACS Applied Materials & Interfaces, Microporous and Mesoporous Materials, and Journal of Materials Chemistry A. Collaboration analysis identified 10 compact author clusters, including groups associated with Randall Q. Snurr, Seda Keskin, Zhiwei Qiao, Qingyuan Yang, and Chongli Zhong, whereas the weak bridging links among clusters indicate that cross-community collaboration remains limited. Country and institutional analyses show that China, the United States, Canada, Iran, India, South Korea, and the United Kingdom are leading contributors, with Guangzhou University, Koç University, Northwestern University, the Chinese Academy of Sciences, Beijing University of Chemical Technology, and the United States Department of Energy occupying prominent positions. Keyword evolution reveals a shift from adsorption behavior and porous adsorbents toward data-guided material selection, high-throughput screening, deep learning, Bayesian optimization, and performance optimization, offering guidance for data-driven adsorbent discovery. Full article

Tribological systems involving rolling and sliding contacts generate coupled mechanical interactions that govern friction, wear, and surface degradation. These interactions produce multiaxial residual stresses that influence crack initiation, accelerate wear, and promote environmentally assisted damage. In corrosive environments, tribo-corrosion further intensifies material degradation through the combined action of mechanical wear and electrochemical reactions. Protective organic and metallic coatings are widely used to mitigate these effects; however, their performance depends on adhesion, stress evolution, and resistance to coupled mechanical and chemical degradation. Among the principal failure mechanisms, cathodic blistering is strongly influenced by diffusion, interfacial stresses, and tribological loading. This review therefore links cathodic blister evolution with coating degradation under combined tribological and corrosive conditions. The review critically examines the Khan–Nazir meso-mechanics Models I, II, and III, which integrate stress-assisted diffusion, residual stress development, mixed-mode fracture, and coating–substrate delamination. Recent developments have extended these models through substrate deformation, multilayer coating architectures, and electro-chemo-mechanical phase-field simulations. The models demonstrate how diffusion-induced and residual stresses interact with tribological loading to initiate and propagate interfacial defects. The analysis shows that blister evolution is primarily governed by elastic modulus mismatch and friction-induced stress fields, while stability criteria predict non-axisymmetric blister morphologies associated with buckling and delamination. Overall, this review highlights the significance of the Khan–Nazir models for understanding wear, friction, and coating durability in engineering systems. The unified framework provides valuable guidance for the design and optimisation of advanced multilayer protective coatings for marine, automotive, energy, and manufacturing applications operating under rolling/sliding contact and tribo-corrosion environments. Full article

This review is devoted to the occurrence, extraction, structural characterization and catalytic applications of vanadyl porphyrins present in heavy crude oils and petroleum residues. Vanadyl porphyrins represent the major vanadium-containing compounds in petroleum systems and play a dual role as both catalyst poisons during refining processes and valuable precursors for functional catalytic materials. Particular attention is devoted to recent advances in extraction technologies, including solvent extraction, ionic liquids, deep eutectic solvents, functionalized adsorbents and chelating agents. Process intensification approaches such as ultrasound- and microwave-assisted extraction, are also discussed as promising strategies for improving extraction efficiency and selectivity. Furthermore, recent developments in petroleum characterization using FTICR-MS, EPR, HYSCORE and LA-ICP-MS techniques are reviewed, providing insights into metalloporphyrin speciation, oxidation states, and distribution within complex petroleum matrices. Beyond their traditional role in catalyst deactivation, vanadyl porphyrins have emerged as attractive precursors for catalytic materials applied in oxidation reactions, photocatalysis, oxidative desulfurization, wastewater treatment and selective organic synthesis. The development of hybrid catalytic systems based on mesoporous silica, graphene oxide, carbon nanotubes, polymer matrices, and metal–organic frameworks has significantly improved catalyst stability, activity and recyclability. Current challenges related to the selective extraction, preservation of metalloporphyrin structure and catalytic performance evaluation are also discussed. Overall, this review provides an integrated perspective on the recovery, characterization and valorization of vanadyl porphyrins for sustainable petroleum upgrading and environmental applications. Full article

In this study, taking NdCl₃ and 2-amino-1,4-benzenedicarboxylic acid (H₂ATA) as raw materials, a novel lanthanide metal–organic framework, Nd(ATA), was synthesized by the coprecipitation method. After loading antibiotic levofloxacin (LEV), Nd(ATA) was combined with GelMA hydrogel to prepare a drug-loaded composite hydrogel, LEV@Nd(ATA)-Gel, which can emit near-infrared fluorescence under excitation at 808 nm and possesses improved mechanical properties compared to pure GelMA hydrogel. LEV@Nd(ATA)-Gel exhibited high bactericidal activity and low cytotoxicity, with cell viability increased by 35% compared to the control group. The release rate of the loaded LEV was found increasing with the pH decreasing from 7 to 3, and demonstrated a potential responsiveness to wound microenvironment. Furthermore, drug delivery studies revealed a significant correlation with the fluorescence intensity of the composite hydrogel and the drug release behavior, and the extent of drug release was quantitatively captured by an in vitro imaging technology. This study successfully integrated the drug release with fluorescent signal of carrier, providing a highly sensitive and visualizable strategy for the development of internal wound adhesive. Full article

Liquid metals, particularly gallium-based alloys, uniquely combine fluidic compliance with metallic conductivity, which makes them ideal candidates for biomimetic design. Rather than treating biomimicry as the mere imitation of biological forms, we argue that liquid metal biomimicry should be understood as the realization of biological strategies through the intrinsic physics of fluidity and interfacial dynamics. This review organizes existing research within a hierarchical framework that couples physical liquidity, interface biology analogy, and functional emergence to explain how adaptive behaviors naturally arise from dynamic liquid metal systems. We examine representative systems across morphological and functional dimensions and contend that their true significance lies not in replicating nature but in addressing problems that conventional rigid materials cannot solve. Looking forward, we identify several transformative directions that collectively chart a roadmap toward truly intelligent and autonomous bioinspired systems. By bridging the physics of fluidity with the principles of biological adaptability, liquid metal biomimicry holds transformative potential for soft robotics, wearable electronics, neuromorphic computing, and biomedical engineering. Full article

The conversion of CO₂ into value-added chemicals using photothermal catalysis is an attractive approach for achieving carbon neutrality. However, the limited visible-light absorption and rapid charge carrier recombination of CeO₂ hinder its photothermal catalytic performance. Herein, a series of In₂O₃-modified CeO₂ (In₂O₃-CeO₂) are fabricated from a Ce-BTC metal–organic framework (MOF) precursor and applied to photothermal CO₂ hydrogenation. This approach not only effectively modulates the pore structure but also generates abundant oxygen vacancies (O_v). As a result, CO₂ adsorption and activation, charge separation and photothermal catalytic performance over In₂O₃-CeO₂ are significantly enhanced. The 3% In₂O₃-CeO₂ (3% denotes amounts of In) composite catalyst exhibits the optimal catalytic performance with a CO yield of 92.35 mmol·g⁻¹·h⁻¹ and a remarkable 100% selectivity. A mechanistic study reveals that the Ov-rich interface facilitates electron transfer and promotes CO₂ conversion through a CO₂ → *COOH → *CO reaction pathway. This work provides an effective strategy for designing high-efficiency MOF-derived composites to achieve carbon utilization. Full article

Industrial wastewater containing refractory organic compounds, heavy metals, and emerging contaminants poses a significant challenge to conventional treatment methods due to their high chemical stability and toxicity. This review systematically summarizes recent advances in Fenton-based advanced oxidation processes (AOPs) for industrial wastewater treatment, with a particular focus on the paradigm shift from homogeneous to heterogeneous catalytic systems. Homogeneous Fenton processes, which rely on Fe²⁺/H₂O₂ reactions, exhibit rapid reaction kinetics but are severely limited by a narrow operational pH range (2–4) and the generation of substantial iron sludge. In contrast, heterogeneous Fenton systems employing immobilized or supported catalysts—such as iron-loaded zeolites, metal–organic frameworks, and carbon-based composites—broaden the applicable pH range to near-neutral conditions (4–8), enable catalyst recovery and reuse over multiple cycles, and enhance process sustainability by reducing iron leaching and sludge production. Integration with external energy inputs—such as photo, electricity, or ultrasound—can further promote radical generation and mass transfer, improving degradation efficiency while reducing chemical consumption. Practical applications in treating wastewater from textile, pharmaceutical, and electroplating industries have demonstrated effective contaminant removal and enhanced biodegradability. However, most current research remains at the laboratory scale, with long-term catalyst stability, operational costs, and scalability representing major barriers to large-scale implementation. Future research should focus on developing stable and regenerable catalysts, advancing pilot-scale studies of integrated systems, and conducting long-term evaluations under real wastewater conditions to promote the development of efficient, low-carbon, and sustainable solutions for industrial wastewater treatment. Full article

The quasar Eigenvector-1/Main Sequence (E1/MS) provides a physically motivated empirical framework to organize the spectroscopic diversity of type 1 active galactic nuclei (AGN). In its optical plane, the full width at half maximum of H β and the Fe II strength ratio R FeII define a sequence that is primarily driven by Eddington ratio, with important secondary roles played by black-hole mass, orientation, spectral energy distribution, and chemical enrichment. The E1/MS framework is therefore well suited to identifying highly accreting and possibly super-Eddington (SE) sources, usually associated with the extreme Population A (xA) spectral types. In this study, we discuss why E1/MS is a useful tool to search for SE accretors among jetted AGN and, conversely, to place γ -ray-detected AGN in the broader context of quasar phenomenology. We summarize two complementary results: (1) some candidate SE accretors show radio properties such as high brightness temperature non-thermal cores or radio lobes consistent with jet activity and (2) a subset of low-redshift γ -ray narrow-line Seyfert 1 galaxies exhibit optical spectra consistent with xA or borderline-xA classification. We also expand the discussion of recent developments in E1/MS studies, including metallicity trends, the spectral energy distribution of xA quasars, and the role of highly accreting quasars as discovery tools for extreme accretion states, as probes of quasars at the reionization epoch, and as possible cosmological probes. Full article