Search Results (2,267)

Escherichia coli (E. coli) is a microorganism commonly found in water and food matrices, and its rapid and accurate detection is crucial for maintaining public health and ensuring food safety. However, traditional molecularly imprinted polymer (MIP) sensors often face challenges such as tedious template removal and prolonged sensing times. This study develops a label-free bacterial molecularly imprinted sensor that utilizes the synergistic effect of polypyrrole (PPy) and multi-walled carbon nanotubes (MWCNTs) to achieve highly sensitive detection of E. coli. Based on the large specific surface area and superior conductivity of MWCNTs, as well as the favorable electrochemical polymerization properties of PPy, a PPy/MWCNTs composite film was fabricated via a one-step electropolymerization process. The prepared sensor exhibited excellent kinetic characteristics, with a template removal time of only 15 min, and could be regenerated and used for subsequent detection within 30 min. Under optimized conditions, the biosensor showed a satisfactory linear response over the concentration range of 10²–10⁸ CFU/mL, with a low detection limit of 65 CFU/mL (3σ/S). Furthermore, recovery experiments conducted in tap water and lemon juice samples yielded satisfactory recoveries ranging from 87.1% to 114.8%, demonstrating the reliability and practical applicability of the proposed sensor for bacterial detection in real samples. This sensor offers advantages such as simple preparation, low material cost, and high sensitivity, providing a reliable and practical analytical platform for the rapid and reliable detection of bacteria. Full article

Electrocatalytic seawater splitting is an ideal strategy for the large-scale production of green hydrogen. Compared to scarce freshwater, oceanic seawater electrolysis represents a game-changer for the hydrogen economy. Herein, we report a cost-effective one-step synthesis of binder-free, self-supported 3D nickel–manganese–cobalt (NiMnCo) coatings on titanium (Ti) substrates and evaluated their electrocatalytic performance for the hydrogen evolution reactions (HERs) in alkaline media (1.0 M KOH), simulated seawater (SSW, 1.0 M KOH + 0.5 M NaCl) and alkaline natural seawater (ASW, 1.0 M KOH + natural seawater). These ternary coatings were electrodeposited on Ti substrates using an electrochemical deposition method via a dynamic hydrogen bubble template (DHBT) technique. The optimized ternary NiMnCo/Ti-2 electrocatalyst exhibited an enhanced HER activity in both alkaline and seawater media, achieving an ultra-low overpotential of 29, 59 and 66 mV to reach the benchmark current density of 10 mA cm⁻² in SSW, ASW and 1.0 M KOH, respectively. This efficient 3D ternary NiMnCo/Ti-2 electrocatalyst demonstrated stable long-term performance at a constant potential of −0.23 V (vs. RHE) and a constant current density of 10 mA cm⁻² for 50 h without any significant degradation. Furthermore, it exhibited long-term stability in alkaline electrolyte and simulated seawater during multi-step chronopotentiometric testing at variable current densities from 20 mA cm⁻² to 100 mA cm⁻² for 18 h. This superior performance can be attributed to its unique intermetallic structure and multi-component composition, which provides good Cl⁻ resistance, electrochemical stability and synergistic effects among its constituents. Therefore, the optimized NiMnCo/Ti-2 electrocatalyst is a promising candidate for practical seawater electrolysis aiming at green hydrogen production. Full article

This paper presents a parametric modeling and aerodynamic matching optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Firstly, based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Secondly, numerical simulations are conducted to analyze the internal flow field and evaluate the performance of the initial design of this compressor, revealing performance deficits such as significant vortex-induced losses and a large outlet circumferential flow angle (−12.138°). Thirdly, an aerodynamic optimization framework integrating a Kriging surrogate model and a Genetic Algorithm (GA) is applied to the second-stage stator, targeting at the aerodynamic matching optimization under multiple operating conditions. The optimization objectives include maximizing the overall polytropic efficiency of compressor and the static pressure ratio of second-stage stator, as well as minimizing the total pressure loss coefficient and the outlet circumferential flow angle of second-stage stator. The results demonstrate that the optimized design achieves a 2.17% improvement in the overall polytropic efficiency and a 12.01% improvement in the static pressure recovery coefficient at the design condition, along with a notable reduction in the outlet circumferential flow angle to 0.663°. Under multi-condition operation, the optimized stator exhibits enhanced performance stability. The overall polytropic efficiency is improved by 2.06% and the static pressure recovery coefficient is improved by 23.31% at the low-flow condition, confirming the effectiveness of the employed parametric modeling and sequential optimization approach. Full article

To address the inaccurate scheduling of electric–hydrogen integrated stations (EHISs) caused by the limited accuracy of conventional mechanistic models for proton exchange membrane (PEM) electrolyzers, this study proposes a deep reinforcement learning (DRL)-based scheduling strategy incorporating a data-driven electrolyzer model. First, a deep XGBoost model is developed to characterize the hydrogen production behavior of the PEM electrolyzer, thereby replacing the traditional mechanistic model and reducing prediction errors. Second, the EHIS scheduling problem is formulated as a constrained Markov decision process (CMDP) that explicitly considers user demand and carbon emission constraints. Third, an improved deep Q-network (DQN) algorithm integrating Lagrangian relaxation and the template policy-based reinforcement learning (TPRL) method is designed to solve the scheduling problem, which enhances convergence speed and generalization performance under similar operating scenarios. The simulation results demonstrate that the proposed method can effectively alleviate the decision-making risks introduced by model inaccuracies and significantly improve the operational profitability of the station while satisfying user demand and carbon emission constraints. Full article

Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich behavioral data from diverse reading behaviors. Eye movement data from 59 participants were recorded while they read two manga works on a screen. A comprehensive set of gaze features was extracted and evaluated using five machine learning classifiers, among which Random Forest (RF) consistently achieved the best performance. Under constrained experimental conditions, the RF classifier achieved a Rank-1 identification rate of 95.0% and an equal error rate (EER) of 1.9%. Furthermore, this study systematically investigated two critical challenges for practical deployment: stimulus dependency and template aging. Cross-stimulus evaluation revealed substantial performance degradation when training and testing used different manga works, and template aging analysis over an approximately 90-day interval demonstrated notable declines in identification accuracy. These results provide preliminary evidence supporting the potential of natural reading behaviors for biometric continuous authentication systems while highlighting the need for further research into cross-stimulus generalization and temporal stability. Full article

Si₃N₄@MgSiN₂ composite powder with a core–shell structure was successfully synthesized via the in situ reaction between Mg and α-Si₃N₄ using a NaCl–KCl mixed molten salt in this study. The effects of process parameters, including the molten salt system, reaction temperature, and Mg/Si₃N₄ mass ratio, on the morphology, phase composition, and microstructure of the coating layer were investigated. The results indicate that the reaction follows a “template growth” mechanism. Mg-containing species dissolve in the molten salt, diffuse to the surface of Si₃N₄ particles, and react with α-Si₃N₄, resulting in a relatively uniform MgSiN₂ layer at 1300 °C. The yield of MgSiN₂ layer exhibits a linear positive correlation with the Mg/Si₃N₄ mass ratio, enabling controllable microstructural regulation through adjustment of the starting materials composition. The core–shell powder forms a liquid phase at a relatively low temperature (approximately 1350 °C), demonstrating excellent sintering activity. This work provides a new material foundation for the fabrication of silicon nitride ceramics with high thermal conductivity. Full article

Superhydrophobic surfaces are typically achieved through the synergistic integration of appropriate nanostructures and low-surface-energy chemical compositions. This study presents a novel and facile method for constructing a superhydrophobic hierarchical structure directly on a pristine selective laser melting (SLM) titanium surface. The intrinsic partially melted Ti particles, which are inherent to the SLM fabrication process, were strategically utilized as a natural microscale template for the in situ growth of TiO₂ nanotubes via electrochemical anodization. Three distinct micro/nano-topographies were successfully fabricated, integrating the spherical microparticles with either conventional TiO₂ nanotube arrays or separated nanotube arrays. The results demonstrate that the resulting superhydrophobic behavior can be effectively regulated by two key factors: the liquid–solid contact mode at the microscale and the strength of capillary action within the nanostructures. Notably, these characteristics can be tailored by controlling the nanotube diameter and intertubular spacing. These findings contribute to a deeper understanding of the role of micro–nano hierarchical structures in engineering superhydrophobic surfaces, thereby opening new avenues for advanced applications. Full article

Evaluating emotional understanding in Large Language Models (LLMs) is challenging because assessments are subjective, ambiguous, multidimensional, and sensitive to controllable generation parameters. We developed a unified mathematical framework for characterizing LLM “emotional individuality” that integrates softmax sampling–temperature control (the decoding-time temperature parameter exposed by the API and typically used to modulate output randomness during token generation), fuzzy set theory with Shannon-type fuzzy entropy, and persona-based cognitive diversity analysis. We evaluated 36 API-accessible LLMs from seven major vendors on Japanese literary texts, using four personas each assigned a sampling temperature ($T\in \{0.1,0.4,0.7,0.9\}$), yielding 4227/4320 trial responses (97.8% coverage), of which 4067/4227 contained valid numeric emotion scores (96.2%). Temperature controllability varied approximately 25-fold (${\kappa }_M\in [0.039,0.982]$) with both positive and negative temperature–variance relationships across models. Because each sampling temperature is deterministically assigned to a persona in our design, ${\kappa }_M$ should be interpreted as an operational temperature–variance association across persona conditions rather than an isolated causal temperature effect. The model-level mean fuzzy entropy ranged from approximately 0.40 to 0.66, and the numerical stability consistency scores ranged from approximately 0.548 to 0.780. We also observed text-dependent structure, including genre-specific variation in the Interest–Sadness relationship. For practitioners, the framework is most directly useful as a benchmark-design and model-screening template for structured emotion-scoring tasks; its empirical conclusions remain limited to the present Japanese literary, text-only setting. Full article

The rheological behavior of chitosan–vanillin crosslinked olive oil-in-water emulsions (Φ = 0.52) was investigated to identify formulation and processing conditions suitable for designing oleogel precursors. The effects of homogenization conditions, reaction temperature, chitosan concentration, vanillin-to-chitosan molar ratio, and non-ionic surfactants were systematically evaluated. Surfactant-free emulsions exhibited a structured, gel-like response and non-thixotropic shear-thinning flow, which was well described by the Herschel–Bulkley model within the investigated shear-rate range. Optimal homogenization (4 min, ≥9500 rpm) refined the microstructure without compromising stability. Increasing the reaction temperature to 55 °C, the chitosan concentration to ~0.9% (w/w), and the vanillin-to-chitosan molar ratio to 0.7 maximized yield stress, consistency, and thermal robustness, consistent with enhanced network formation. In contrast, Tween^® surfactants produced divergent responses, increasing small-amplitude oscillatory stiffness while markedly reducing resistance under steady shear, likely due to surfactant-driven interfacial displacement. Among the tested surfactants, Tween^® 20 provided the highest thermal stability. Overall, these results define processing and formulation windows to obtain surfactant-free, structured emulsions with improved structuring performance, supporting their use as effective templates for olive oil oleogel development. Full article

Statistical inference in experimental neuroscience is routinely detached from the experimental record: analytic choices are reported in prose summaries that do not expose the code, assumptions, or decision pathways that produced the results. This detachment limits reproducibility and impairs peer review. Here, we describe the Companion Statistical Report (CSR), a structured, versioned document format designed to accompany empirical neuroscience manuscripts as peer-reviewed as a peer-reviewed resource. The CSR integrates data provenance, preprocessing decisions, exploratory analyses, model specifications, assumption diagnostics, inference with effect sizes, and sensitivity analyses into a single executable document, authored in Quarto 1.8 and supporting both R and Python workflows. We provide an open template hosted at on GitHub that implements this format with institutional branding, parameterization, and version tracking. The template was developed by the Bertrand Russell Research Excellence Group (NEC) at the School of Medicine, Rio de Janeiro State University. By making analytic choices auditable and reproducible by design, CSRs are designed to reduce the gap between what neuroscience experiments measure and what published statistics claim, offering a tractable and immediately implementable step toward greater transparency. Full article

Composite ZnO/Fe nanostructured thin films are synthesized via physical vapor deposition using radio frequency magnetron sputtering in conventional, as well as in glancing angle deposition (GLAD) geometries. ZnO is employed as a compact nanocolumnar template to direct Fe growth in bilayer and multilayer architectures. Morphological analysis reveals well-defined ZnO/Fe interfaces for normal deposition geometry, with diminished interface clarity and reduced layer thickness in GLAD samples. Crystallographic characterization indicates clear ZnO-{002} and α-Fe-{110} texture. Magnetostatic characterization investigates the effects of morphology on coercivity and domain nucleation. GLAD-deposited Fe films exhibit clear in-plane magnetic anisotropy, with remanence to saturation magnetization (M_REM/M_SAT) equal to 1 for the easy axis and equal to 0.24 for the hard axis, consistent with inclined nanocolumn morphology. Our findings show that deposition geometry, rather the ZnO template, mostly affects the morphology of Fe films. The above, highlight the potential of engineered ZnO/Fe nanocomposites for magnetic, spintronic, and magnetoplasmonic applications, by tuning morphology and interface quality through deposition parameters. Full article

Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction method that combines ontology modeling with large language models (LLMs). First, an ontology for water conservancy facility safety is constructed, encompassing four core elements: agencies and personnel, engineering equipment, risks and hidden dangers, and systems and processes. Subsequently, a KG-LLM-GraphRAG architecture is designed, which optimizes the knowledge extraction effectiveness of LLM through ontology-constrained prompt templates and utilizes the Neo4j graph database for knowledge storage and multi-hop reasoning. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches in entity-relationship extraction tasks. The resulting KG supports hazard identification, emergency decision-making, and knowledge reuse, offering an efficient tool for organizing and reasoning in water conservancy safety management, strongly propelling the digital transformation of the water conservancy industry. Full article

Modern web application development using frameworks such as React often requires writing a significant amount of initial code before reaching the stage where development becomes engaging. To address this, we developed Lunor, a domain-specific language that can be used in the early phases of front-end development by allowing developers to describe web interfaces in a clear, human-readable syntax that incorporates Markdown for defining the content of a web application. The proposed solution integrates three key components: the Lunor language definition, a template-based code generation, and a Visual Studio Code (VS Code) extension built on the Language Server Protocol, forming a comprehensive environment for efficient web development. Lunor enables rapid prototyping and the creation of simple, yet fully functional web applications, while the generated code remains compatible with standard web technologies for further expansion. Lunor demonstrates that domain-specific languages can simplify front-end web development effectively and integrate seamlessly into the modern web development process. Full article

Conventional high-temperature calcination for activating MFI zeolite membranes is energy-intensive and prone to inducing defects. Here, we demonstrate that a rapid ozonation treatment at 200 °C for only 1 h effectively decomposes organic templates while preserving membrane integrity. The resulting membrane exhibits H₂/CH₄ and H₂/N₂ ideal selectivities of 10.3 and 6.5, respectively, at room temperature, with C₃H₈ and SF₆ permeances below the detection limit. These results confirm a dense, defect-minimized architecture and good molecular sieving performance of the zeolite membrane. In contrast, extending ozonation to 48 h leads to defect formation and a marked reduction in selectivity. For H₂/CH₄ mixture separation, the membrane achieves a selectivity of 23.8 at 100 °C, which is highly competitive among reported MFI membranes. In isopropanol dehydration, it achieves a water flux of 2.3 kg·m⁻²·h⁻¹ and a separation factor of 3278 at 70 °C with a 10 wt% water feed, while maintaining >99.5 wt% water content in the permeate over a broad operating temperature range (30–70 °C). This work establishes rapid ozonation as a scalable, energy-efficient activation method for high-performance MFI zeolite membranes in both gas and liquid separations. Full article

In this work, a flexible and sustainable radar-absorbing material (RAM) based on porous carbon derived from raw Kraft black liquor was developed. The porous carbon filler was synthesized through a simple, eco-friendly one-pot polymerization route, thereby avoiding lignin extraction, purification, and chemical activation steps. Macroporosity was introduced by using poly(methyl methacrylate) microspheres as a hard template, yielding a lightweight carbon material with a foam-like morphology, low density, and high porosity. The carbon filler was incorporated into a silicone rubber matrix at different loadings (5–25 wt.%) to produce flexible composites. The structural, morphological, and textural properties of porous carbon were investigated by SEM, EDX, Raman spectroscopy, nitrogen adsorption, and mercury porosimetry. The electromagnetic properties of composites were measured in the X-band (8.2–12.4 GHz) using a vector network analyzer. The mechanical behavior was evaluated through Young’s modulus. The results show that increasing filler content enhances dielectric losses and attenuation capability. Among all composites, the sample containing 20 wt.% of porous carbon exhibited the best electromagnetic performance, achieving a reflection loss of −42.3 dB at 10.97 GHz with a thickness of 2.43 mm, corresponding to an absorption efficiency of 99.99%. This performance is attributed to a favorable combination of impedance matching and quarter-wavelength cancellation effects. The developed sustainable, lightweight, and flexible composites demonstrate potential as low-cost RAM for aerospace and electromagnetic interference mitigation applications. Full article