Search Results (864)

To explore an alternative detection approach for the pesticide imidacloprid (IMI), this study repurposed the quinoline-benzimidazole fluorescent probe DQBM-B—previously developed for Co²⁺ recognition—and investigated its detection performance and interaction mechanism toward IMI in the aggregated state. The optimal working conditions of the probe were determined by optimizing key detection parameters, and the sensing performance and matrix compatibility were evaluated through selectivity tests and proof-of-concept spiked cucumber extract analysis. The DQBM-B aggregates interact with IMI synergistically through intermolecular hydrogen bonding and π–π stacking, which enrich IMI at the aggregate surface to create a local enrichment layer. The observed fluorescence quenching arises from the synergistic contribution of static quenching (due to ground-state complex formation) and the inner filter effect (IFE). Under the optimal conditions, the system exhibited a detection limit of 0.75 μmol L⁻¹ for IMI with favorable anti-interference ability. The matrix effect evaluation in cucumber extract demonstrated good recovery and precision, demonstrating the feasibility of the aggregation-regulated IFE strategy in complex food matrices. This study expands the application scope of the DQBM-B probe from metal-ion sensing to pesticide detection and provides a metal-free, aggregation-regulated strategy for the fluorescence detection of neonicotinoid pesticides. Full article

Biomass-derived nanoengineered carbon materials have emerged as key platforms in environmental technologies due to their high surface area, electrical conductivity, chemical stability, and sustainable synthetic route starting from renewable feedstock. This broad family comprises dimensionally nanoscale materials, such as carbon dots, carbon nanofibers, and graphene-based structures, as well as biochars, hydrochars, activated carbons, and related porous carbonaceous materials whose pore architecture, surface chemistry, or defects are deliberately engineered at the nanometer scale. Beyond their traditional role as passive supports, these materials can actively regulate adsorption phenomena, charge transport, and catalytic microenvironments through precise control of heteroatom doping, graphitic domains, and hierarchical porosity. Among current environmental priorities, carbon dioxide (CO₂) management represents one of the most pressing challenges. Biomass-derived nanocarbons offer tunable adsorption sites for selective CO₂ capture while simultaneously serving as active matrices for catalytic conversion. Tailored doped-carbon frameworks can stabilize key reaction intermediates, suppress competing pathways such as hydrogen evolution, and promote selective transformation into fuels and high-value chemicals. In addition, these materials are excellent hosts for atomically dispersed metals, dual-site catalysts, and semiconductor hybrids used in electrochemical and photocatalytic CO₂ reduction. By combining renewable sourcing with nanoscale control of reactivity, carbon materials create a bridge between environmental remediation and carbon valorization. This review critically examines recent progress in biomass-derived nanoengineered carbon materials for integrated CO₂ capture and conversion, with emphasis on structure-property-performance relationships, mechanistic roles, scalability, and sustainability. Particular attention is also devoted to catalytic conversion and electrochemical CO₂ sensing, where carbon-based and hybrid interfaces enable the transduction of CO₂ recognition into measurable electrical responses. These materials represent a promising yet underexplored pathway toward circular carbon management and the development of next-generation low-carbon chemical technologies. Full article

Nanocomposite-structured sensing interfaces were developed on fibrous substrates for chemiresistive detection of volatile organic compounds (VOCs) by integrating graphene (GE), cellulose derivatives hydroxyethylcellulose (HEC) and carboxymethylcellulose (CMC) and molecularly linked gold nanoparticles into composition-programmable thin films. Raman and infrared spectroscopy confirm that graphene incorporation occurs through physical integration without chemical modification of the polymer matrix, preserving cellulose integrity while enabling graphene loading to govern electrical percolation and charge-transport pathways. Systematic variation in nanocomposite composition reveals clear design rules linking interfacial polarity to VOC class sensitivity: hydrophilic GE/CMC and amphiphilic GE/HEC interfaces exhibit enhanced responses to polar and hydrogen-bonding VOCs, whereas hydrophobic gold thiolate assemblies preferentially respond to nonpolar aromatic and aliphatic VOCs. Incorporation of ligand-functionalized gold nanoparticles introduces an additional tunability dimension, modulating both VOC affinity and sensor stability through combined electronic and surface-chemical effects. Sensor arrays constructed from complementary nanocomposite interfaces achieve reliable VOC discrimination, as demonstrated by sensitivity patterns, spider-chart analysis, and principal component analysis, with effective separations retained even in reduced-sensor configurations. Across multiple nanocomposite architectures, enhanced response sensitivity is observed at low VOC concentrations, highlighting the role of interfacial adsorption dynamics and underscoring the potential of paper-based nanocomposite chemiresistive platforms for sub-ppm VOC detection. Full article

Drought and salinity are major abiotic stresses that severely constrain plant growth and agricultural productivity. Histidine kinases (HKs), as key components of the plant two-component system (TCS), play crucial roles in environmental signal perception and adaptive responses. In this study, we functionally characterized a sweet potato (Ipomoea batatas L.) HK gene, IbHK1a, and investigated its role in drought and salt stress tolerance. Expression analysis revealed that IbHK1a is predominantly expressed in root tissues, particularly in storage and fibrous roots, indicating its potential involvement in stress sensing and adaptation. Subcellular localization demonstrated that the IbHK1a protein is localized to the plasma membrane, suggesting a role in external signal perception. To elucidate its biological function, IbHK1a was heterologously overexpressed in Arabidopsis thaliana. Transgenic plants exhibited significantly enhanced tolerance to drought and salt stress, as evidenced by higher seed germination rates, improved primary root growth, reduced leaf wilting, and increased survival rates compared with wild-type (WT) plants. Physiological analyses showed that IbHK1a overexpression led to increased activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), accompanied by reduced accumulation of reactive oxygen species (ROS) such as hydrogen peroxide (H₂O₂) and malondialdehyde (MDA). Consistently, leaf histochemical staining confirmed lower ROS accumulation in transgenic plants under stress conditions. In sweet potato, overexpression of IbHK1a in transgenic hairy roots enhanced tolerance to drought and salinity, whereas RNA interference lines displayed increased sensitivity, further confirming its positive regulatory role. Additionally, protein interaction analysis indicated that IbHK1a interacts with Arabidopsis histidine phosphotransferase proteins (AHPs), suggesting its involvement in conserved TCS-mediated phosphorelay signaling pathways. Functional complementation analysis demonstrated that IbHK1a partially rescues the stress-sensitive phenotype of the AHK1 mutant, indicating functional conservation with Arabidopsis AHK1. Collectively, these findings demonstrate that IbHK1a positively regulates drought and salt stress tolerance by enhancing antioxidant defense and ROS homeostasis. Its interaction with AHPs and partial complementation of the ahk1 mutant further support its involvement in the conserved TCS phosphorelay pathway. These results establish IbHK1a as an important component of abiotic stress responses and a potential genetic target for improving drought and salinity tolerance in sweet potato. Full article

Hydrogen (H₂) detection under practical operating conditions requires sensing materials that simultaneously provide accessible reaction sites, efficient gas diffusion pathways, and fast interfacial charge transfer. Here, a hierarchical star-sphere ZnCo₂O₄ (ZC) architecture was integrated with graphene oxide (GO) and Pt supported on graphitized carbon (Pt/C) to develop hybrid chemiresistive sensing layers for low-temperature hydrogen detection. The synthesized ZC-based material exhibited a hierarchical morphology consisting of porous microspheres and star-shaped assemblies, providing a multiscale framework for gas access and surface reactions. By varying the GO content from 0.1 to 1 wt% at a fixed Pt/C loading, the ZC-0.5G composite achieved the most balanced structure, with well-distributed GO sheets, preserved star–sphere morphology, the highest specific surface area (53.6 m²/g), and the largest pore volume (0.09 cm³/g). The optimized sensor gave responses of 12.96%, 19.20%, 22.87%, and 26.43% for 500, 4000, 8000 and 10,000 ppm H₂ concentrations, respectively, with measurable response down to 50 ppm. The highest sensing performance was achieved at 50 °C and 60% relative humidity (RH), where the hierarchical oxide framework, GO-assisted interfacial pathways, and Pt catalytic sites acted in concert. The sensor also showed repeatable cyclic behavior and preferential response to H₂ compared to methanol, isopropanol, ethanol, acetone, and dimethylformamide. The improved sensing performance is attributed to the synergistic combination of the hierarchical ZC framework, GO-assisted interfacial pathways, and Pt-assisted catalytic activation, which together facilitate gas diffusion, surface reactions, and resistance modulation. Full article

Biochar is a porous carbonaceous material synthesized through the pyrolysis of diverse biomass resources, including agricultural and forestry residues as well as livestock manure. It possesses superior characteristics such as a large specific surface area, adjustable pore architecture, abundant surface functional groups, and favorable electrical conductivity. With the increasingly severe global energy shortage and environmental pollution problems in recent years, biochar has emerged as a green, low-cost functional material with distinct application superiority in multiple key research directions, including energy storage and conversion, chemical catalysis, environmental restoration, and signal sensing and detection. This study comprehensively summarizes the latest research advances of biochar in the aforementioned application fields, focusing on innovative achievements in photocatalytic and electrocatalytic hydrogen generation, supercapacitors and electrochemical energy storage systems, persulfate activation technology, carbon dioxide capture, remediation of heavy metal and organic contaminants, volatile organic compound (VOC) adsorption, as well as electrochemical sensing devices. Existing research results demonstrate that modification strategies including metal and non-metal doping, surface oxidation treatment, and compounding with semiconductors or metal oxide materials can effectively improve the catalytic activity and functional performance of biochar. Furthermore, this paper prospects the future interdisciplinary development trends of biochar, analyzes the existing research gaps in mechanism exploration, structural optimization design, and industrial large-scale preparation, and provides theoretical and practical references for the further popularization and application of biochar in sustainable energy development and environmental governance fields. Full article

The increasing adoption of hydrogen as an energy carrier requires advanced monitoring solutions for transport infrastructures, where intelligent sensing and data-driven analysis can play a key role in improving safety and operational efficiency. However, anomaly detection in hydrogen transport networks remains challenging due to the limited availability of operational data and the complexity of transient behaviors associated with these systems. This work investigates a weakly-supervised anomaly detection framework for hydrogen transport networks based on high-fidelity simulation and data-driven analysis. The proposed methodology combines temporal deep learning architectures and unsupervised representation learning models with an operational threshold calibration strategy based on the trade-off between false positives and false negatives. The proposed framework is validated using a high-fidelity simulation environment that reproduces normal and anomalous operating conditions, including leaks, compressor malfunctions, and delayed activation events. The framework is evaluated through comparative experiments involving different anomaly detection architectures, robustness analysis under measurement noise, and leave-one-topology-out generalization tests. Results demonstrate that the proposed approach can effectively identify abnormal behaviors while maintaining robustness against degraded signal quality and previously unseen operating configurations. The obtained results highlight the effectiveness of the proposed methodology as a framework for developing and validating intelligent monitoring strategies for hydrogen transport infrastructures. Full article

Smart chemical sensors have emerged as promising tools for real-time monitoring of food spoilage in both fermented and non-fermented products. By detecting key spoilage indicators—including biogenic amines, ammonia, hydrogen sulfide, methane, pH variations, and microbial volatile organic compounds (MVOCs)—these systems enable rapid, on-site assessment of food quality, offering a viable alternative to conventional, time-consuming laboratory analyses. Recent advances encompass diverse sensing mechanisms, including chemiresistive platforms based on conducting polymers and MEMS (Microelectromechanical Systems); optical/colorimetric systems using dyes, metal–organic frameworks, and porphyrins; and electrochemical and biosensing approaches employing enzymes, antibodies, aptamers, and whole-cell recognition elements. These sensors demonstrate high sensitivity (ppb–ppm range), enabling early detection of spoilage before sensory perception or microbiological threshold exceedance. Their applicability has been validated across a wide range of food matrices, including meat, fish, dairy products, vegetables, beverages, and fermented foods. Despite significant progress, key challenges persist, including signal drift, limited specificity, susceptibility to environmental factors such as humidity and temperature, and interference from complex food matrices. Furthermore, integration into intelligent packaging requires the development of flexible, food-safe, and regulatory-compliant materials. Emerging approaches that combine sensor arrays with machine learning and MVOC pattern recognition are enhancing predictive accuracy and enabling food classification across commodity types. Overall, smart chemical sensing technologies are rapidly transitioning from laboratory prototypes to practical applications in intelligent packaging and wireless monitoring systems, with ongoing research focused on improving robustness, standardization, and scalability for commercial deployment. This article provides an overview of the topic, drawing on the available bibliography from the last five years and the most-cited scientific databases. Full article

Metal–Organic Frameworks (MOFs) have undergone remarkable development in recent decades, transforming them into one of the most dynamic classes of emerging composite materials. These crystalline, porous coordination networks, built from metal ions or metal clusters interconnected by organic linkers, form architectures with tunable porosity, large specific surface area, and chemical functionality. Due to their remarkable stability and customizable functionalities, MOFs have attracted significant attention in recent years as promising materials for different applications. In 2025, Susumu Kitagawa, Omar Yaghi, and Richard Robson were awarded the Nobel Prize in Chemistry for pioneering the development of MOF crystalline materials with spacious internal cavities that can store, filter or catalyze molecules. This review systematically consolidates the recent literature (since 2020) on MOF-based systems, covering state-of-the-art performance, synthesis advantages and limitations, and the influence of reaction parameters on morphology, structure, and luminescent properties. The rapid yearly increase in MOF-related publications, continuing strongly into 2026, reflects the growing global interest and highlights the rising importance of their design and applications. This trend motivates the central focus of this paper, which, in a single work, emphasizes the integrated use of MOFs in luminescent sensing, biosensing, the removal of heavy metals, microplastics, and organic dyes in water treatment, and hydrogen storage. Finally, the challenges, conclusions and future perspectives of MOF-based materials will be highlighted with the aim of providing guidelines for their further development and additional applications. Full article

Multi-horizon forecasting of nonlinear dynamic time series with exogenous inputs is challenging when the target variable exhibits strong temporal persistence and the exogenous variables provide horizon-dependent corrective information. Direct forecasting models must learn both the carry-forward behavior of the target and the nonlinear deviations caused by changes in the process inputs. This study proposes a residual exogenous–autoregressive gated forecasting framework for nonlinear dynamic prediction. The proposed model decomposes the forecasting operator into a persistence component and a learnable residual correction term. Historical target dynamics and exogenous input dynamics are encoded using two dedicated CNN-LSTM branches, and their latent representations are combined through a sample-dependent sigmoid gating mechanism. The final prediction is obtained by adding the learned correction to the most recent target observation. The framework is evaluated on a benchmark sulfur recovery unit dataset for multi-horizon hydrogen sulfide H₂S concentration forecasting using a leakage-aware nested blocked hyperparameter selection and evaluation protocol. Three forecasting horizons are considered: one-step, five-step, and ten-step ahead prediction. The proposed method achieved the lowest RMSE at the one-step and five-step horizons and remained highly competitive at the ten-step horizon, where its RMSE was nearly identical to the best PatchTST baseline. Across the three horizons, the proposed model obtained RMSE values of $0.0096\pm 0.0020$, $0.0436\pm 0.0097$, and $0.0521\pm 0.0138$, corresponding to RMSE reductions over the persistence baseline of 39.7%, 10.0%, and 13.7%, respectively. The model also maintained a compact parameter count and sub-millisecond inference latency, supporting its feasibility for online soft-sensing applications. Regression, time-series, error distribution, Taylor diagram, and SHAP analyses show that the residual gated formulation is particularly effective for short- and medium-horizon forecasting, while longer-horizon prediction remains more difficult because of increasing temporal uncertainty. The SHAP results indicate that historical H₂S dominates short-horizon prediction, whereas airflow-related variables become more influential at the longer horizon. The results demonstrate that the proposed framework provides an interpretable and computationally compact learning approach for residual forecasting in persistent nonlinear dynamic systems. Full article

Flexible piezoresistive sensors are highly desirable for wearable health monitoring, yet balancing ultrahigh sensitivity and wide pressure detection range is a major bottleneck restricting their applications in electronic skin and soft robots. This work constructs a hierarchical piezoresistive sensor through a simple three-step fabrication: electrospinning PVDF/PAN fiber networks, polydopamine (PDA) surface modification, and in situ polypyrrole (PPy) polymerization for conductive sensing layers. As a dual-function interlayer, PDA forms hydrogen and covalent bonds with PPy to yield uniform, firm conductive coatings. The link between PPy morphology and sensing performance is clarified by regulating polymerization parameters. At a pyrrole concentration of 3 g/L, the optimized sensor achieves a high sensitivity of 220.88 kPa⁻¹ (0–10 kPa) and stable linear signals up to 1 MPa, with superior cycling durability over 5000 cycles and good biocompatibility. This scalable fabrication resolves the sensitivity–range tradeoff, promising wearable medical monitoring and human–machine interaction devices. Full article

The detection of organic carbon in wastewater is essential for process monitoring and regulatory assessment. Yet conventional chemical oxygen demand (COD) and total organic carbon (TOC) methods remain reagent-dependent, slow, and unsuitable for inline operation. Photoelectrochemical (PEC) sensing based on TiO₂ offers a reagent-free alternative, but its response to wastewater-relevant dissolved organic matter (DOM) and real effluent matrices is still poorly understood. In this study, a TiO₂-based PEC system was systematically evaluated using four representative model compounds—glucose, potassium hydrogen phthalate, L-tryptophan, and urea—covering major fractions typically present in municipal wastewater. For the first time, representative wastewater-associated organic compound classes, conductivity effects, and the transferability of the PEC response to real wastewater effluent were systematically investigated. The photocurrent response showed distinct, highly linear concentration–signal relationships for each substance, suggesting a dominant contribution of surface-associated electronic effects. Conductivity variations across a relevant range had no measurable influence on sensitivity or photocurrent magnitude, indicating that the PEC response is not governed by bulk ionic transport but primarily is an interfacial process at the site of TiO₂. When applied to real wastewater effluent, the sensor exhibited an excellent linear correlation with dilution level (R² = 0.9954), demonstrating a linear response within a defined matrix and an LOD of 1.12 mg L⁻¹ COD. For the investigated model compounds, LOD values ranged from 1.06 to 3.00 mg L⁻¹ COD, while a linear response was maintained up to approximately 80–100 mg L⁻¹ COD. These findings establish TiO₂-based PEC sensing as a promising platform for the reagent-free, online monitoring of organic loads in wastewater treatment. Full article

The hydrogen temperature-programmed reduction (H₂-TPR) method was used to analyze the sensing properties of nanostructured indium oxide for hydrogen detection. Commercial and mechanically activated In₂O₃ samples were selected for investigation. Mechanical activation leads to the generation of surface defects and an increase in specific surface area, which enhances sensitivity to H₂ and lowers the sensor operating temperature. An approach is proposed that allows a qualitative and quantitative relationship to be established between the H₂-TPR profiles of the oxides and the sensor response. This relationship is based on a model of electron transfer across a potential barrier at grain boundaries, formed with the participation of adsorbed oxygen. The temperature dependence of the sensor response is found to be determined by the concentration of negatively charged oxygen on the surface of the nanoparticles, which, in turn, depends on temperature. Using the sensor sensitive layer based on indium oxide as an example, a correlation is established between the parameters of the TPR profiles and the sensor response. Full article

This study investigates the electrochemical properties of polymer-based microelectrode arrays (pMEAs) and their performance in measuring oxygen (O₂) and hydrogen peroxide (H₂O₂). Morphological characterization by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) revealed a uniform, fine-grained platinum surface with nanoscale roughness, consistent with the Ti/Pt/Au/Pt multilayer stack architecture. The electrochemical behavior of the pMEAs was assessed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which demonstrated favorable responses for both O₂ reduction and H₂O₂ oxidation, together with low impedance (41.1 kΩ at 1 kHz). For O₂ detection, amperometric measurements at −0.6 V vs. Ag/AgCl indicated a sensitivity of −0.25 ± 0.04 nA μM⁻¹ and a detection limit of 5.4 ± 1.4 nM. For H₂O₂ detection, application of +0.7 V vs. Ag/AgCl resulted in a sensitivity of 88.13 ± 7.61 nA mM⁻¹ and a detection limit of 41.9 ± 5.6 nM. Selectivity evaluation showed effective interferent exclusion following m-phenylenediamine electrodeposition, without compromising analytical performance. Overall, these findings indicate the suitability of pMEAs for real-time, in vivo monitoring of O₂ and H₂O₂ in brain tissue with high spatial and temporal resolution, supporting applications in oxidative stress research and neurometabolic sensing. Full article

A Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ high-entropy alloy film was designed as a Cr- and Al-free metallic sensing layer for thin-film strain gauges in high-pressure hydrogen environments. CALPHAD calculations predicted a BCC/B2-type phase field, while XRD, EBSD and GIXRD results supported a BCC-type structure without direct confirmation of long-range B2 ordering. Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ films deposited on Si reference substrates at 150 and 300 W retained broad BCC-type diffraction features. The 300 W film showed a more continuous cross-sectional morphology, good metallic conductivity and a comparable nanomechanical response with slightly higher hardness. Device-level tests were then performed using Cr/AlN/Fe₅Cu₅V₃₀Ti₃₀Nb₃₀ TFSGs on 316L stainless-steel substrates. The devices exhibited average absolute apparent zero shifts of 16.08 με in 12 MPa N₂ and 17.79 με in 12 MPa H₂, with an additional H₂-associated apparent response of only 1.71 με. Static tensile tests in 12 MPa H₂ confirmed a linear strain response with a gauge factor of 1.72 ± 0.01. Full article