Search Results (13,592)

Electronic skins (E-skins) are the integration of intelligent wearable sensors that can collect human physiological, motion, or environmental parameters in real-time through flexible, sensitive materials. The performance of E-skins depends on the selection of materials to a large extent. Hydrogel materials are an excellent candidate for E-skin preparation due to their tissue-like softness and biocompatibility. However, their low electrical conductivity, weak mechanical strength, and environmental instability seriously hinder high-fidelity signal acquisition and reliable operation in practical applications. To overcome these bottlenecks, conductive composite hydrogels have emerged as a promising alternative material. The unique properties of conductive composite hydrogels, such as high stretchability, self-healing ability, and adjustable electrical conductivity, address the relevant issues of traditional hydrogels in wearable applications. This review focuses on conductive composite hydrogels for wearable E-skins. Firstly, the types, characteristics, and preparation strategies of hydrogel matrix materials are introduced. Subsequently, the performance regulation mechanisms of key conductive fillers on composite hydrogels are discussed. Then, the application progress in electrophysiological signal monitoring, human–machine interaction, and human motion monitoring is reviewed. Finally, the current challenges and future development directions of hydrogel-based E-skins are prospected, aiming to provide comprehensive material and fabrication references for the practical application of composite hydrogel in electronic skins. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

In this study, we discovered a new diameter-dependent carbon nanotube (CNT) hydrogel composed exclusively of CNTs and tannic acid (TA). Accordingly, we first examined the relationship between the concentrations of CNTs and TA, as well as the CNT diameter, and whether gelation occurred. As a result, we found that when the TA concentration was fixed at 0.15 wt%, the threshold CNT concentration required for gelation was 0.05 wt%, which was lower than the values reported for previously known CNT hydrogels. We also determined that a TA to CNT weight ratio of 2–3 is critical for gelation. Furthermore, we found that subjecting the CNT dispersion to hydrothermal treatment at 160 °C, followed by freezing and ambient drying, produced a CNT aerogel that retained its 3D structure. Then, we evaluated the thermoelectric properties (electrical conductivity and Seebeck coefficient) of the resulting CNT hydrogel and aerogel under a temperature gradient for application. Both materials exhibited stable and reproducible electromotive force, and the measured Seebeck coefficients were comparable to those of conventional CNT-based thermoelectric materials. These findings demonstrate that 3D thermoelectric materials can be readily fabricated from CNT dispersions via simple processes and highlight the potential of these materials for future applications in energy-harvesting devices. Full article

This study addresses the challenge of simultaneously improving the electrical conductivity and strength of aluminum alloys. We innovatively combine powder metallurgy with melt stirring casting to fabricate graphite flake-added aluminum matrix composites through secondary remelting, electromagnetic stirring, and extruding. The influence of graphite flake content gradient (0–3.0 wt.%) on the mechanical properties and electrical conductivity was systematically investigated. Our results demonstrate that the composite with 0.2 wt.% graphite flakes (sample GM02) exhibits optimal comprehensive performance: tensile strength reaches 100.9 MPa (a 124% increase over pure Al), and electrical conductivity reaches 67.1% IACS (a 9.6% increase). Microstructural analysis reveals that low-content graphite flakes effectively suppressed electron scattering by forming semi-coherent interfaces. However, when graphite flake content exceeds 0.5 wt.%, a significant decrease in conductivity and plasticity (elongation below 10%) occurs due to increased Al₄C₃ phase formation, enhanced grain boundary scattering caused by grain refinement, and porosity defects induced by graphite flake agglomeration. This study provides a novel approach for the industrial production of high-performance, lightweight conductive components. Full article

The structure, chemical composition, thermal stability, and abuse responses of cathode materials are critical to the safety and economy of lithium-ion batteries (LIBs). This review systematically summarizes advances in research on how cathode materials influence LIB thermal runaway (TR) behavior. It analyzes the oxygen release from cathodes in TR mechanisms and the hazards of such oxygen generation during TR, expounds on how differences in cathode structure, chemical composition, and thermal stability affect TR behavior, and summarizes the thermal characteristics of LIBs with different cathodes under mechanical, electrical, and thermal abuse. Results indicate that oxygen released from cathode decomposition during TR oxidizes electrolytes, releasing substantial heat and gas and causing more severe TR hazards. Structural instability of cathodes leads to accelerated release of lattice oxygen, speeding up TR initiation. Chemical composition regulates thermal stability, phase transition pathways, and gas generation rates during TR, while elemental ratios affect the ease of TR triggering. Cathodes with poor thermal stability have lower thermal decomposition onset temperatures, making TR more likely to occur and intensifying reaction severity. All three abuse types trigger inherent risks of cathodes, inducing TR and significantly increasing its occurrence probability. Differences in intrinsic properties further extend to the system level, also influencing thermal runaway propagation and fire dynamics at the module level. Future research focusing on the intrinsic properties of cathodes and external abuse is of great significance for addressing LIB TR behavior. Full article

The restoration of “Heitutan” degraded grasslands on the Qinghai-Tibetan Plateau was hindered by suboptimal grass species mixtures, leading to low vegetation productivity, impaired soil nutrient cycling, and microbial functional degradation. Based on a 22-year controlled field experiment, this study systematically elucidated the regulatory mechanisms of different artificial grass mixtures on vegetation community characteristics, soil physicochemical properties, and bacterial community structure and function. The results demonstrated that mixed-sowing treatments significantly improved soil conditions and enhanced aboveground biomass. The HC treatment (Elymus nutans Griseb. + Poa crymophila Keng ex L. Liu cv. ‘Qinghai’ + Festuca sinensis Keng ex S. L. Lu cv. ‘Qinghai’) achieved aboveground biomass of 1580.0 and 1645.0 g·m⁻², representing 66.14% and 60.91% increases, respectively, compared to the HA monoculture (E. nutans). Concurrently, this treatment increased soil organic matter content by 52.3% and 48.4%, total nitrogen by 59.4% and 69.2%, while reducing electrical conductivity by 48.99% and 51.72%, with optimal pH stabilization (7.34–7.38). These findings confirmed that optimized grass mixtures effectively enhance soil physicochemical properties and carbon–nitrogen retention. Microbiome analysis revealed that the HE treatment (E. nutans + P. crymophila + F. sinensis + Poa poophagorum Bor. + Festuca kryloviana Reverd. cv. ‘Huanhu’) exhibited superior α-diversity indices (OTU, Shannon, Ace, Chao1, Pielou) with increases of 9.36%, 4.20%, 15.0%, 1.76%, and 13.4%, respectively, over HA, accompanied by optimal community evenness (lowest Simpson index). Core bacterial phyla included Pseudomonadota (22.7–29.9%), Acidobacteriota (21.5–23.6%), and Actinomycetota (13.6–16.0%), with significant suppression of pathogenic bacteria. Co-occurrence network analysis identified specialized functional modules, with HC and HD treatments (E. nutans + P. crymophila + F. sinensis + P. poophagorum) forming a “nitrogen transformation–antibiotic secretion” network (57.3% positive connections). Structural equation modeling (SEM) revealed that mixed sowing had the strongest direct effect on bacterial diversity (β = 0.76), surpassing indirect effects via soil (β = 0.37) and vegetation (β = 0.11). Redundancy analysis (RDA) identified vegetation cover (24.7% explained variance) and soil pH (20.0%) as key drivers of bacterial community assembly. Principal component analysis (PCA) confirmed HC and HD treatments as the most effective restoration strategies. This study elucidated a tripartite “vegetation–soil–microorganism” restoration mechanism, demonstrating that intermediate-diversity mixtures (3–4 species) optimize ecosystem recovery through niche complementarity, pathogen suppression, and enhanced nutrient cycling. These findings provided a scientific basis for species selection in alpine grassland restoration. Full article

This study examines the contrast in the nonlinear dynamics of Thrinax radiata Lodd. ex Schult. & Schult. f. Seed germplasm explored by optical and electrical signals. By integrating chaotic attractors for the modulation of the optical and electrical measurements, the research ensures high sensitivity monitoring of seed germplasm dynamics. Reflectance measurements and electrical responses were analyzed across different laser pulse energies using Newton–Leipnik and Rössler chaotic attractors for signal characterization. The optical attractor captured laser-induced changes in reflectance, highlighting nonlinear thermal effects, while the electrical attractor, through a custom-designed circuit, revealed electromagnetic interactions within the seed. Results showed that increasing laser energy amplified voltage magnitudes in both systems, demonstrating their sensitivity to energy inputs and distinct energy-dependent chaotic patterns. Fractional calculus, specifically the Caputo fractional derivative, was applied for modeling temperature distribution within the seeds during irradiation. Simulations revealed heat transfer about 1 °C in central regions, closely correlating with observed changes in chaotic attractor morphology. This interdisciplinary approach emphasizes the unique strengths of each method: optical attractors effectively analyze photoinduced thermal effects, while electrical attractors offer complementary insights into bioelectrical properties. Together, these techniques provide a realistic framework for studying seed germplasm dynamics, advancing knowledge of their responses to external perturbations. The findings pave the way for future applications and highlight the potential of chaos theory for early detection of structural and bioelectrical changes induced by external energy inputs, thereby contributing to sample protection. Our results provide quantitative dynamical descriptors of laser-evoked seed responses that establish a tractable framework for future studies linking these metrics to physiological outcomes. Full article

In this study, the prediction of four radiological risk parameters of thermal waters in Türkiye (dose contribution (D_E) from radon release in thermal water to air for workers and visitors, the annual effective dose from radon ingestion (D_ing) and the annual effective dose to the stomach from radon ingestion (D_sto)) from three physicochemical properties of thermal waters (electrical conductivity (EC), pH and temperature (T)) was investigated using multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). To achieve this, two separate MLPANN and RBFANN models were constructed using data from the literature. The MLPANN and RBFANN models were verified using performance metrics (relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), and ratio of RMSE to data standard deviation (RSR)). The comparison of performance metrics shows that MLPANN models achieved approximately 54% lower error metrics than RBF models. The performance of the developed models was further examined using rank analysis, Taylor and Scaled Percentage Error (SPE) plots. Rank analysis and Taylor and SPE graphs showed that MLPANN models predicted the values of four radiological risk parameters of thermal waters more correctly than RBFANN models. This study demonstrates that MLPANNs significantly outperformed RBFANNs in predicting the radiological risks of thermal waters in Türkiye. Full article

Understanding the intricate relationship between composition, processing conditions, and material properties is essential for optimizing Cu-based alloys. Machine learning offers a powerful tool for decoding these complex interactions, enabling more efficient alloy design. This work introduces a comprehensive machine learning framework aimed at accurately predicting key properties such as hardness and electrical conductivity of low-alloyed Cu-based alloys. By integrating various input parameters, including chemical composition and thermo-mechanical processing parameters, the study develops and validates multiple machine learning models, including Multi-Layer Perceptron with Production-Aware Deep Architecture (MLP-PADA), Deep Feedforward Network with Multi-Regularization Framework (DFF-MRF), Feedforward Network with Self-Adaptive Optimization (FFN-SAO), and Feedforward Network with Materials Mapping (FFN-TMM). On a held-out test set, DFF-MRF achieved the best generalization (R²_test = 0.9066; RMSE_test = 5.3644), followed by MLP-PADA (R²_test = 0.8953; RMSE_test = 5.7080) and FFN-TMM (R²_test = 0.8914; RMSE_test = 5.8126), with FFN-SAO slightly lower (R²_test = 0.8709). Additionally, a computational performance analysis was conducted to evaluate inference time, memory usage, energy consumption, and batch scalability across all models. Feature importance analysis was conducted, revealing that aging temperature, Cr, and aging duration were the most influential factors for hardness. In contrast, aging duration, aging temperature, solution treatment temperature, and Cu played key roles in electrical conductivity. The results demonstrate the effectiveness of these advanced machine learning models in predicting critical material properties, offering insightful advancements for materials science research. This study introduces the first controlled, statistically validated, multi-model benchmark that integrates composition and thermo-mechanical processing with deployment-grade profiling for property prediction of low-alloyed Cu alloys. Full article

The thickness of the LPCVD-Si₃N₄ gate dielectric layer significantly influences the electron transport properties of AlGaN/GaN metal–insulator–semiconductor high-electron-mobility transistors (MIS-HEMTs), but the mechanism by which it affects polarization Coulomb field (PCF) scattering remains largely unexplored. In this study, AlGaN/GaN MIS-HEMTs with LPCVD-Si₃N₄ gate dielectric thicknesses of 0 nm, 5 nm, and 20 nm were fabricated, and the influence of LPCVD-Si₃N₄ thickness on PCF scattering was systematically investigated. Through electrical measurements and theoretical calculations, the relationship between LPCVD-Si₃N₄ gate dielectric layer thickness, additional polarization charge (∆ρ), two-dimensional electron gas (2DEG) density, and 2DEG mobility was analyzed. The results show that increasing the LPCVD-Si₃N₄ thickness reduces the vertical electric field in the AlGaN barrier, weakening the inverse piezoelectric effect (IPE) and reducing ∆ρ. Further analysis reveals that the ∆ρ exhibits a non-monotonic dependence on negative gate voltage, initially increasing and subsequently decreasing, due to the competition between strain accumulation and stress relaxation. Meanwhile, the 2DEG mobility limited by PCF (μ_PCF) decreases monotonically with increasing negative gate voltage, mainly due to the progressive weakening of the 2DEG screening effect. The research results reveal the physical mechanism by which LPCVD-Si₃N₄ thickness regulates PCF scattering, providing theoretical guidance for optimizing gate dielectric parameters and enhancing the performance of AlGaN/GaN MIS-HEMTs. Full article

This study examines the effects of reduced nitrogen (N) application on rice straw N depolymerization in coastal saline paddy soil to establish a scientific basis for optimizing N application strategies during straw incorporation in coastal paddy systems. A 360-day field straw bag burial experiment was conducted using four N application levels: N0 (control, without N fertilizer), N1 (225 kg N/ha), N2 (300 kg N/ha), and N3 (375 kg N/ha). The results indicated that applying 300 kg N/ha significantly (p < 0.05) increased dissolved organic N (DON) content, apr and chiA gene copies, and the activities of alkaline protease, chitinase, leucine aminopeptidase, and N-acetylglucosaminidase. In addition, the application of 300 kg N/ha enhanced the synergistic effects of alkaline protein- and chitin-degrading microbial communities. Pseudomonas, Brevundimonas, Sorangium, Cohnella, and Thermosporothrix were identified as keystone taxa predominant in straw N depolymerization. Straw N depolymerization occurred by two primary pathways: direct regulation of enzyme activity by straw properties of total carbon and electrical conductivity, and indirect influence on N hydrolase activity and DON production through modified microbial community structures. The findings suggest that an application rate of 300 kg N/ha is optimal for promoting straw N depolymerization in coastal saline paddy fields. Full article

Poly(3-thienylboronic acid) (PThBA) has recently been suggested as a conducting polymer with affinity for saccharides. In this study, thin films of this compound were deposited onto gold electrodes. The system obtained was studied as a possible chemical sensor. The measurements were performed by impedance spectroscopy using potassium ferro/ferricyanide as a redox mediator. The thickness of the polymer and the deposition of the adhesive sublayer were optimized to achieve a compromise between the blocking of defects in the polymer layer and the unnecessary increase in the internal resistance of this conductometric sensor. A comparative study of the influence of fructose, glucose, and sorbitol on transversal polymer resistance was conducted. The binding constants for these saccharides were extracted from the concentration dependencies of sensor conductance. Among them, sorbitol showed the highest affinity with a binding constant up to ~15,000 L·mol⁻¹, followed by fructose (~8700 L·mol⁻¹) and glucose (~4500 L·mol⁻¹). In order to exclude the contribution of the analyte tautomers on the obtained binding constants, measurements of ethylene glycol were also performed. The effects of pH and the redox state of PThBA on its affinity properties were studied, revealing higher affinities at alkaline pH and in oxidized state of the chemosensitive polymer. The developed system has the capacity to be applied in chemical sensors and virtual sensor arrays with electrical affinity control. Full article

Beta-gallium oxide (β-Ga₂O₃) is a semiconductor with an ultra-wide bandgap, high optical transparency, and excellent electrical properties, which can be finely tuned for a wide range of electronic devices. This study optimized the process conditions for fabricating β-Ga₂O₃ thin films with desired electrical characteristics. β-Ga₂O₃ films were deposited on (100) Si substrates via RF magnetron sputtering with varying O₂ flow rates and post-annealed at temperatures ranging from 600 °C to 800 °C. The structural and electrical properties of the films were analyzed using X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), and Hall effect measurements. The XRD results confirmed the formation of nanocrystalline β-Ga₂O₃, with variations in peak intensities and shifts observed based on O₂ flow rates. The films exhibited carrier concentrations exceeding 5 × 10²² cm⁻³, mobilities ranging from 50 to 115 cm²/Vs, and resistivity around 1 × 10⁻⁶ Ω⋅cm. This study demonstrates that the electrical properties of β-Ga₂O₃ thin films can be modulated during the deposition and post-annealing processes. The ability to control these properties underscores the potential of β-Ga₂O₃ for advanced applications in high-performance high-power devices and optoelectronic devices such as deep ultraviolet photodetectors. Full article

The persistent conflict between strength and electrical conductivity in copper-based materials presents a fundamental limitation for next-generation high-performance applications. Graphene, with its unique two-dimensional architecture and exceptional intrinsic characteristics, has become a promising reinforcement phase for copper matrices. This comprehensive review synthesizes recent advancements in graphene–copper composites (CGCs), focusing particularly on structural design innovations and scalable manufacturing approaches such as powder metallurgy, molecular-level mixing, electrochemical deposition, and chemical vapor deposition. The analysis examines pathways for optimizing key properties—including mechanical strength, thermal conduction, and electrical performance—while investigating the fundamental reinforcement mechanisms and charge/heat transport phenomena. Special consideration is given to how graphene morphology, concentration, structural quality, interfacial chemistry, and processing conditions collectively determine composite behavior. Significant emphasis is placed on interface engineering strategies, graphene alignment, consolidation control, and defect management to minimize electron and phonon scattering while improving stress transfer efficiency. The review concludes by proposing research directions to resolve the strength–conductivity paradox and broaden practical implementation domains, thereby offering both methodological frameworks and theoretical foundations to support the industrial adoption of high-performance CGCs. Full article

Nanoparticle superlattices—periodic assemblies of uniformly spaced nanocrystals—bridge the nanoscale precision of individual particles with emergent collective properties akin to those of bulk materials. Recent advances demonstrate that multivalent ions and charged polymers can guide the co-assembly of nanoparticles, imparting electrostatic gating and enabling semiconductor-like behavior. However, the specific roles of anion geometry, valency, and charge density in mediating ion transport remain unclear. Here, we employ coarse-grained molecular dynamics simulations to investigate how applied electric fields (0–0.40 V/nm) modulate ionic conductivity and spatial distribution in trimethylammonium-functionalized gold nanoparticle superlattices assembled with four phosphate anions of distinct geometries and charges. Our results reveal that linear anions outperform ring-shaped analogues in conductivity due to higher charge densities and weaker interfacial binding. Notably, charge density exerts a greater influence on ion mobility than size alone. Under strong fields, anions accumulate at nanoparticle interfaces, where interfacial adsorption and steric constraints suppress transport. In contrast, local migration is governed by geometrical confinement and field strength. Analyses of transition probability and residence time further indicate that the rigidity and delocalized charge of cyclic anions act as mobility barriers. These findings provide mechanistic insights into the structure–function relationship governing ion transport in superlattices, offering guidance for designing next-generation ion conductors, electrochemical sensors, and energy storage materials through anion engineering. Full article