Search Results (1,783)

Developing sensitive and reversible CO sensors requires precise control of material–analyte interactions. Using DFT, we investigate CO sensing on bimetallic (Fe, Pt) anchored on N-doped graphene (TM₂–N₄–C), focusing on active-site density effects. Three densities are considered: low (12.7 Å), medium (8.5 Å), and high (4.2 Å). FePt–N₄–C band gaps exhibit non-monotonic tuning, approaching metallicity at high density. CO chemisorbs on Fe sites, but physisorbs on Pt sites. FePt exhibits stronger synergistic adsorption than homonuclear counterparts. While adsorption generally strengthens with density, spin-polarized calculations qualitatively reorder this trend via spin delocalization. High temperatures drastically improve recovery; low-density FePt–N₄–C reaches 65 s at 498 K. Three design principles emerge: low-density heteronuclear systems for reversible sensing, medium-density high-spin states for ultra-sensitive capture, and high-density configurations for work function sensors. This work establishes active site density as a key electronic and kinetic knob for graphene-based CO sensors. Full article

Hydrogels and nanocomposite−enhanced hydrogels, owing to their high−water content, excellent biocompatibility, and mechanical flexibility, have demonstrated broad application prospects in tissue engineering, drug delivery, and flexible electronics. With the continuous advancement of computational power, molecular dynamics (MD) simulations have increasingly become an important tool for characterizing nanocomposite materials and hydrogel systems. This approach enables the capture of structural evolution at the atomic/molecular scale and provides mechanistic insights into deformation behaviors and interaction mechanisms under external stimuli such as mechanical force, temperature, and electric fields. This review is organized around the central framework of “structural construction–interfacial regulation−responsive behavior–dynamic evolution”, and systematically summarizes the recent progress in the application of molecular dynamics and multiscale simulation methods to hydrogels and nanocomposite hydrogels. The systems discussed mainly include synthetic polymer-based hydrogels, natural polymer−based hydrogels, peptide/protein−based hydrogels, and nanocomposite hydrogels. Particular emphasis is placed on modeling strategies and force−field selection principles for describing atomic interactions in various nanocomposite hydrogel systems. In addition, the important applications of multiscale simulation strategies in elucidating the interfacial behavior of hydrogels and the mechanisms underlying their dynamic responses under nonequilibrium conditions are also discussed. Finally, future development trends are outlined, including multiscale coupled simulations, closed−loop correction between experiments and simulations, and data−driven modeling strategies for the precise design and performance prediction of complex hydrogel systems. Full article

The common carp (Cyprinus carpio) kidney uniquely integrates excretory nephrons, renal hematopoietic tissue, and hormonally active thyroid follicles, positioning it as a candidate “multipurpose biomarker organ” for pollutants like perfluorooctanoic acid (PFOA), a prototype long-chain PFAS and persistent organic pollutant exhibiting nephrotoxic, immunotoxic, and thyroid-disrupting effects. Building on prior histological, ultrastructural, and morphometric analyses from carp exposed to waterborne PFOA (0, 200 ng L⁻¹, 2 mg L⁻¹ for 56 days), a hierarchical multipurpose index comprising nephrotoxic, immunotoxic, and thyrotoxic subindices was developed from z-scored light-, electron-microscopy, and morphometric features, enabling cross-scale integration; proximal tubule vesiculations and effete rodlet cells (RCs) were newly quantified from archival electron micrographs. The subindices captured PFOA-induced glomerular hyperfiltration with proximal protein reabsorption and collecting duct RCs recruitment (nephrotoxic); hematopoietic tissue RCs recruitment, clustering, and exocytosis (immunotoxic); and increased thyroid follicle abundance/vesiculation, cross-sectional area, and perimeter (thyrotoxic). Quantification of previously only qualitatively assessed features provided statistical validation, while radar plot integration rendered results more intuitively evident—particularly highlighting the non-monotonic thyroid response—condensing organ-level complexity into a coherent framework supporting carp kidney as a translational One Health model for multi-endpoint waterborne pollutant assessment. Full article

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article

Early intervention is pivotal for mitigating the progression of Alzheimer’s disease (AD). This study presents an electrochemical immunosensor targeting synaptic vesicle glycoprotein 2A (SV2A) to facilitate early AD diagnosis. A sensing interface was engineered using a nanocomposite of graphene oxide (GO) and 3-carboxyl polypyrrole (3-COOH-PPy). Leveraging the synergistic effects between the large specific surface area of GO and the superior conductivity of 3-COOH-PPy, the composite established an efficient electron transport network. This architecture provided abundant active sites for capture antibody immobilization while significantly enhancing interfacial electron transfer kinetics. Coupling this interface with an enzyme-mediated signal amplification strategy based on the horseradish peroxidase (HRP)-catalyzed TMB/H₂O₂ system, the immunosensor achieved high sensitivity. It exhibited a wide linear range of 2 ng/mL to 16 μg/mL with a low limit of detection (LOD) of 0.15 ng/mL. Furthermore, successful detection in C57 mouse serum samples validated the method’s reliability and potential for clinical application. In conclusion, this immunosensor offers a sensitive and robust platform for the early diagnosis of AD. Full article

Continuous monitoring of pathological motor features is vital for post-stroke rehabilitation but remains challenged by power reliance and low sensitivity of wearable sensors. Here, we develop a high-sensitivity, self-powered breathable nanogenerator (BN-TENG) utilizing fish-scale-derived biological hydroxyapatite/carbon (Bio-HAp/C) fillers within electrospun polyvinylidene fluoride (PVDF) nanofibers. The Bio-HAp/C enhances electron-trapping capability, while a high-resilience ethylene-vinyl acetate (EVA) spacer optimizes contact-separation dynamics. The BN-TENG achieves a superior sensitivity of 16.28 V·N⁻¹ and remarkable stability over 10,000 cycles. By implementing a multi-node sensing strategy, the sensor successfully captures complex hemiplegic patterns, including compensatory shoulder hiking, distal muscle spasticity, and postural asymmetry. By resolving subtle micro-vibrations missed by traditional electronics, this work provides a sustainable, autonomous interface for characterizing pathological motor features and assessing rehabilitation progress in hemiplegic patients. Full article

Flexible piezoelectric materials demonstrate broad application potential in wearable health monitoring, human–machine interaction, and biosensing. However, the piezoelectric response of pure PVDF-TrFE is limited and insufficient to meet the requirements for highly sensitive sensing. In this study, ZnO/PVDF-TrFE composite films with varying ZnO doping contents (3–11 wt%) were fabricated and systematically characterized in terms of their structural, thermal, and electrical properties. The results indicate that ZnO significantly promotes the formation of the polar β-phase in PVDF-TrFE, with the maximum β-phase content (F_β = 24.76%) and optimal piezoelectric performance achieved at 9 wt% ZnO doping. Devices based on this optimal composition exhibited stable ultrasonic transmission and reception capabilities under high-frequency pulse excitation, enabling sensitive detection of minor static pressure variations (e.g., contact pressure) through changes in ultrasonic echo signals, thereby realizing wearable conformity monitoring. Moreover, a sensor designed with a three-channel flexible substrate successfully captured human wrist pulse signals with high accuracy, demonstrating the practical utility and reliability of the device in flexible bio-electronic sensing applications. Full article

A novel sensitive and selective electrochemiluminescence (ECL) sensor using nitrogen-doped graphyne as the platform was proposed for kanamycin (KAN) detection. First, nitrogen-doped graphyne nanomaterial (1N-GY) with high conductivity was synthesized using a high-energy ball milling method. Compared with ordinary graphyne, the addition of nitrogen atoms can improve the conductivity of the material and reduce the electronic migration energy barrier. Then it was used as a substrate material of the ECL sensor, not only increasing the conductivity of the biosensor but also improving the sensitivity of the ECL sensor by providing more immobilization space for the luminescent probe of Nafion-coated mesoporous silica adsorbed Ru(bpy)₃²⁺ (mSiO₂@Nafion@Ru(bpy)₃²⁺). On this basis, mSiO₂@Nafion@Ru(bpy)₃²⁺ functionalized DNA probes were used as luminescent and capture probes to specifically recognize different concentrations of KAN to produce ECL signals. Under optimal conditions, the proposed ECL sensor exhibited good linearity (10⁻¹²–10⁻⁶ M KAN) and a low detection limit of 1.08 pM. The prepared biosensor with good stability and selectivity successfully detected KAN in honey and milk samples, with spiked recovery rates ranging from 98% to 111.79%. This method not only expands the application of 1N-GY as a novel graphitic material in ECL biosensors but also provides an effective way to check antibiotics in dairy products. Full article

Cr-based composite coatings with superior wear resistance are in growing demand for high-performance applications in the automotive, aerospace, and general manufacturing sectors. In this study, an Al10Cu alloy produced via powder metallurgy was coated with a chromium/nanodiamond (Cr/ND) composite layer using an electrodeposition process to enhance its tribological performance. The coatings were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The resulting Cr/ND layer exhibited a uniform thickness of 73.5–76.2 μm and markedly improved surface hardness (809.4 HV), representing a 15-fold increase over the uncoated alloy (53.6 HV). Pin-on-disk tribological testing under dry sliding conditions showed complete elimination of detectable mass loss (0.00 mg vs. 0.55 mg for uncoated) within the measurement system resolution, indicating excellent resistance to both abrasive and adhesive wear. XRD analysis revealed the formation of a hexagonal close-packed Cr₂H phase with incorporated nanodiamond particles. To capture and predict the temporal evolution of the friction coefficient, a customized dual-layer long short-term memory neural network—optimized with a look-back window of 3 timesteps and ReLU-activated dense layers—was implemented. The model achieved superior predictive performance on the coated system, with validation and test R² values of 0.9973 and 0.9965, respectively, demonstrating enhanced modeling accuracy for surface-engineered materials. These findings demonstrate a significant advancement in wear protection for aluminum alloys and introduce a robust data-driven approach for real-time friction prediction in engineered surfaces. Full article

The widespread use of plastics has inevitably led to the accumulation of persistent plastic debris in aquatic systems, where gradual fragmentation generates microplastics (MPs) that threaten ecological and biological health. Their small size, chemical stability, and resistance to degradation make effective removal particularly challenging. In this work, a composite adsorbent was fabricated through the in situ solvothermal growth of Materials of Institute Lavoisier 100 (Iron) (MIL-100(Fe)) onto coconut shell-derived activated carbon (CSAC), yielding a monolithic material denoted as CSAC@MIL-100(Fe). The integration of porous C with a metal–organic framework created a hierarchically structured adsorbent rich in accessible binding sites. The composite achieved a maximum polystyrene (PS) removal efficiency of 97.4% and maintained 91.44% efficiency after seven regeneration cycles. Stable adsorption performance was observed across a broad pH range. Structural and chemical analyses (scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS)) combined with adsorption modeling revealed heterogeneous multilayer adsorption behavior consistent with the Freundlich isotherm and pseudo-second-order kinetics. π–π interactions, electrostatic attraction, and coordination effects jointly governed PS capture. The Langmuir maximum adsorption capacity reached 746.27 mg/g. These findings demonstrate a practical and recyclable strategy for efficient MP remediation in aquatic environments. Full article

Prolonged water injection in unconsolidated sandstone reservoirs can induce pore rearrangement and modify flow pathways, thereby affecting reservoir performance. However, quantitative characterization of pore evolution in both temporal and spatial dimensions remains limited. This study investigates the mechanisms of pore-structure evolution during extended injection through a series of multi-scale experiments. Scanning electron microscopy and X-ray diffraction analyses were employed to compare mineral composition and microstructural characteristics before and after injection, while in situ nuclear magnetic resonance (NMR) monitoring captured the dynamic evolution process, enabling pore-size classification from T₂ spectra and fractal assessment of structural complexity. Segmented NMR measurements at different distances further resolved spatial heterogeneity. The results show that prolonged water injection reduced permeability by 10.4–32.1%, whereas porosity exhibited only minor variation, indicating that the decline in flow capacity is primarily controlled by pore–throat structural adjustment rather than pore volume loss. Mineralogical redistribution and fine-particle migration decreased the median pore radius by 21.5–51.8% and the micropore fractal dimension by 23.8–76.5%, with stronger responses observed at higher permeabilities, while meso- and macropore fractal dimensions remained nearly unchanged, indicating preferential modification of micropores with preservation of the main connected flow framework. Consistently, NMR responses reveal pronounced spatial heterogeneity along the flow direction. The NMR signal changes at the injection end were 11.2–18.4% and 7.7–21.7% during the early and intermediate stages, respectively, both exceeding those at the distal end (2.9–12.4% and 1.9–17.1%). These results indicate a downstream-attenuating structural modification gradient. The findings provide new insights into pore-structure evolution during prolonged water injection and offer a scientific basis for optimizing water-injection strategies in unconsolidated sandstone reservoirs. Full article

Calculation of heat transfer in granular materials is an important task for many applications, from thermal management in electronics to exploring celestial soils. Usually, an effective thermal-conductivity model is employed to predict heat flux in unstructured granular media, such as a packed bed. However, a more advanced approach, the discrete element method (DEM), can capture the complex effects of mechanical loading and material mixtures on thermal transport coefficients, which traditional models struggle with. Pivotal for this approach is knowing the heat transfer coefficient between two adjacent particles. Currently, in most DEM-capable software, only particles in direct surface contact are considered to have non-zero heat conduction. We propose considering particles that are close to each other but don’t have a contact area with a non-zero surface area. We perform numerical modeling of the conductive heat transfer coefficient between equal spherical particles separated by media, assuming the fluid’s thermal conductivity is at least an order of magnitude lower. We use numerical solutions of differential equations to account for both thermal resistance within particles and through the gap between them. We found a simple generalized correlation for the heat transfer coefficient between particles and a general formula for the angular distribution of heat flux density across the particle surface. By employing a non-dimensional approach, the obtained formulas are constructed using non-dimensional parameters: the ratio of the particle’s thermal conductivity to that of the medium, and the ratio of the gap width between particles to their radius. The resulting formula is simple and convenient for DEM heat transfer calculations in packed and fluidized beds. Full article

The urgent need to mitigate carbon dioxide (CO₂) emissions from fossil-fuel-based electricity generation has driven research into advanced materials for post-combustion carbon capture. This paper presents a modified solvothermal technique to synthesize zinc (Zn) and magnesium (Mg) based MOF-74 suitable for CO₂ capture from coal-fired power plants. The materials were synthesized through a solvothermal method using N,N-dimethylformamide (DMF) as the primary solvent, and subsequently characterized using Brunauer–Emmett–Teller (BET) surface area analysis, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), and thermogravimetric analysis (TGA). Both MOFs contained oxygen-containing functional groups and were thermally stable up to 430 °C and 600 °C respectively, making them ideal for carbon capture. The low-pressure N₂-BET surface areas were 55 m²/g and 24.73 m²/g. In conclusion, the Zn material had a mesoporous structure, making it more favorable for carbon capture. It was found that prolonged synthesis time weakened the MOF structure. Future work should experimentally evaluate CO₂ capture from coal-derived flue gas using Zn/Mg-MOF-74 materials, investigating adsorption behavior and kinetics through isotherm and kinetic models, while also assessing the effect of varying Zn: Mg ratios under optimized synthesis conditions. Full article

Rapid and accurate recognition of radar active jamming is a prerequisite for cognitive electronic countermeasures. However, under complex electromagnetic environments with scarce training samples, existing deep learning models are prone to capturing spurious correlations induced by environmental confounders, resulting in notable performance degradation. To address this causal confounding issue, we propose a physics-guided variational causal intervention network (PG-VCIN). First, we reconstruct a structured causal model of jamming signal generation, decoupling observations into robust physical statistical features and sensitive time–frequency image representations. Physical priors are then leveraged to perform dynamic precision-weighted modulation of visual feature extraction, enforcing physical consistency at the representation learning stage. Second, we formulate deconfounding within an active inference framework and introduce a variational information bottleneck to optimize mutual information, thereby filtering out high-complexity redundant information attributable to confounders while preserving the essential causal semantics. Finally, we numerically approximate the causal effect by imposing dual intervention constraints in the latent space, including intra-class invariance and confounder invariance. Experiments on a semi-physical simulation dataset demonstrate that the proposed method achieves substantially higher recognition accuracy than several representative few-shot baselines in extremely low-sample regimes, validating the effectiveness of integrating physical mechanisms with causal inference. Full article

Metal-free electrodes are essential to promote electrochemical reactions, the core of sustainable energy resources. In search of better carbon-based electrode materials, we have explored several spatial arrangements of boron (B) within proximity in the graphene lattice, as evident in recent experimental observations. Multi-boron substitution enriches sites by tuning electronic structure and strengthens binding of key intermediates of oxygen reduction, oxygen evolution, and hydrogen evolution reactions facilitating electrocatalytic performance. Our optimal B-doped site shows near thermo-neutral H adsorption ($\Delta G_{H^{*}}$∼$\pm 0.4\mathrm{eV}$), consistent with experiments. The overpotentials are highly sensitive to the dopant motifs and the spread among configurations shows that experimentally accessible multi-B doping can serve as a practical active site engineering knob to achieve optimized multi-functional performance. In parallel, we find that specific multi-B configurations selectively capture and pre-activate NO_x (NO/NO₂) under ambient conditions while retaining weak affinity for NH₃. These sites also interact with SO₂ and related hazardous species, enabling selective air filtration and targeted NO_x control within the electrocatalytic scope of this study. Full article