Bioinspired Multifunctional Eutectogels for Skin-Like Flexible Strain Sensors with Potential Application in Deep-Learning Handwriting Recognition.

Polymerizable deep eutectic solvents (PDES) have recently emerged as a class of solvent-free ionically conductive elastomers and are considered among the most feasible candidates for next-generation ionotronic devices. However, the fundamental challenge persists in synergistically combining high mechanical strength, robust adhesion, reliable self-healing capacity, and effective antimicrobial performance within a unified material system capable of fulfilling the rigorous operational demands of next-generation ionotronic devices across multifunctional applications. Inspired by the hierarchical structure of spider silk, HCAG eutectogels composed of acrylic acid (AA), 2-hydroxyethyl acrylate (HEA), and choline chloride (ChCl) were successfully synthesized via a one-step photopolymerization method.

Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.

Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.

The Synthesis and Photoluminescence Properties of Novel Sr(AlO)(WO): Eu Green-Emitting Phosphors for White LEDs.

Phosphors, as the crucial material of phosphor-converted white light-emitting diodes (pc-WLEDs), have played an essential role in improving luminescent efficiency and regulating color rendering index (CRI). Hence, we have successfully synthesized a novel Eu doped Sr(AlO)(WO) (SAWO) green phosphor for the first time using the solid-state reaction, as well as systematically investigated its phase and crystal structure, luminescent properties, and thermal stability. The SAWO:x mol%Eu (0.

Electrochemical detection of cortisol in sweat for sports performance analysis using graphene oxide-silicon carbide nanocomposites.

An advanced electrochemical immunosensor platform was designed for the precise quantification of cortisol. The sensor design integrates graphene oxide-silicon carbide (GO-SiC) nanocomposites onto a glassy carbon electrode (GCE). Denatured bovine serum albumin (d-BSA) and an anti-cortisol antibody were immobilized on the GO-SiC/GCE surface as part of the immunosensor's design.

Polypyrrole-Modified Triple-Responsive Hydrogel Dressing Based on Bacterial Cellulose and Quaternary Ammonium Chitosan and Its Synergistic Antibacterial Mechanism.

Smart antibacterial materials are gaining attention for their potential in controlled drug release and efficient treatment. This study constructed a composite hydrogel based on oxidized bacterial cellulose (OBC) and quaternary ammonium chitosan (QAC). This hydrogel was loaded with the natural antibacterial drug berberine (Ber) and utilized the abundant functional groups (such as amino and carboxyl groups) on OBC and QAC to achieve pH responsiveness, achieving on-demand drug release in the alkaline microenvironment of simulated chronic wounds.

Research on Laser Cladding Single-Pass Continuous Carbon Fiber-Reinforced Aluminum Matrix Composite Process Based on Abaqus.

This study addresses the critical challenges of interfacial stress mismatch, fiber degradation, and unstable clad geometry in manufacturing continuous carbon fiber-reinforced aluminum composites (Cf/Al) via laser cladding, driven by rapid thermal gradients. A dual-ellipsoid heat source-based thermoelastic-plastic finite element model was developed in Abaqus, integrating phase-dependent material properties and latent heat effects to simulate multi-physics interactions during single-track deposition, resolving transient temperature fields peaking at 1265 °C, and residual stresses across uncoated and Ni-coated fiber configurations. The work identifies an optimal parameter window characterized by laser power ranging from 700 to 800 W, scan speed of 2 mm/s, and spot radius of 3 mm that minimizes thermal distortion below 5% through gradient-controlled energy delivery, while quantitatively demonstrating nickel interlayers' dual protective role in achieving 42% reduction in fiber degradation at 1200 °C compared to uncoated systems and enhancing interfacial load transfer efficiency by 34.

Functional foods and exercise in breast cancer: epigenetic modulation, chemotherapy tolerance, and fatigue management.

Background: Breast cancer (BC) is the most commonly diagnosed malignancy among women worldwide. There is increasing interest in the role of modifiable lifestyle factors, particularly nutrition and physical activity, in influencing cancer risk, progression, and treatment response.

Objective: This review explores how functional foods and exercise can modulate BC through molecular and epigenetic mechanisms and evaluates their potential as adjunctive strategies in prevention and therapy.

Polyurethane Elastomers with Mechanochromic and Self-Healing Functions for Strain Sensor.

Polyurethane is formed by the reaction of a polyol and an isocyanate by covalent bonds, which have been rapidly developing due to their good processability and mechanical properties. However, polyurethane is subject to micro-damage due to external stresses during service, which can lead to rapid performance failure. Currently, the nondestructive assessment methods generally require complex and expensive equipment.

A multifunctional terahertz device based on vanadium dioxide metamaterials that switches between ultra-broadband absorption and ultra-high- narrowband absorption.

Terahertz (THz) absorbers with ultra-broadband and ultra-narrowband absorption capabilities are crucial for integrated and efficient terahertz modulation. This study proposes a dual-mode tunable terahertz absorber based on the phase transition characteristics of vanadium dioxide (VO), enabling dynamic switching between narrowband and broadband absorption through its insulating-to-metallic transition. In the insulating state, the excitation of quasi-bound states in the continuum (Q-BIC) resonance geometric parameter modulation of silicon pillars is investigated, with its physical mechanism elucidated impedance matching theory and multipole analysis.

Intrinsic polarization mechanism of CCTO based on optimized Skanavi model with displacement fields modification.

To elucidate the microscopic origin of the giant dielectric properties in perovskite calcium copper titanate (CCTO), this study proposes a novel optimization of the Skanavi model by incorporating ionic polarization displacement fields. The optical dielectric constant is calculated using the Shannon ionic radius, which accounts for coordination number and electron spin state effects, while the static dielectric response is analyzed through the introduced displacement field. By evaluating the effective electric field ratios of Ca, Cu, Ti, and O ions under varying supercell sizes (1 × 1 × 1 to 19 × 19 × 19), we observe that Ca and Ti exhibit robust lattice stability, whereas Cu polarization direction reverses and O polarization amplitude diminishes significantly.

Smart Polymers and Adaptive Systems in Pilot Suit Engineering: Toward Autonomous, Responsive, and Wearable Flight Technologies.

Next-generation pilot suits are evolving into intelligent, adaptive platforms that integrate advanced polymeric materials, smart textiles, and on-body artificial intelligence. High-performance polymers have advanced in mechanical strength, thermal regulation, and environmental resilience, with fabrication methods like electrospinning, weaving, and 3D/4D printing enabling structural versatility and sensor integration. In particular, functional nanomaterials and hierarchical nanostructures contribute critical properties such as conductivity, flexibility, and responsiveness, forming the foundation for miniaturized sensing and integrated electronics.

Pressure-Activated Breathable Liquid-Metal Electrodes for Long-Term Sleep Electroencephalogram Monitoring.

Sleep electroencephalography monitoring has become an effective strategy for health evaluation. However, conventional gel electrodes suffer from inherent limitations such as moisture loss and poor breathability, compromising long-term signal stability and wear comfort. Here, we present a facile strategy for fabricating breathable, biocompatible liquid-metal electrodes with enhanced interfaces.

Defining Human Thermoregulation Limits: A Critical Evaluation of Predictive Models using Healthy Young Adults.

The core temperature inflection point (CTIP) method (also known as humidity-ramp protocol) and biophysical modeling are widely used to determine human thermoregulation limits, yet their validity under prolonged heat exposure remains unverified. This study evaluated their predictive accuracy by exposing 36 healthy young adults (20 males & 16 females) to five counterbalanced 8-hour indoor heat trials in a controlled chamber (36°C/74.5%RH, 40°C/55.

Emotion recognition of CNN bidirectional long short-term memory with center and Softmax loss function (CNN-BiLSTM-CS) EEG -based.

EEG signal is being widely used in the field of emotion recognition, which currently suffers from the difficulty of obtaining highly distinguishable features. We propose CNN-BiLSTM-CS for emotion recognition EEG-based, which is to address the shortcomings of the traditional LSTM unidirectional propagation and Softmax supervised model in feature extraction. The method firstly employs BiLSTM to CNN, which can bilaterally obtain emotion feature information, and then introduces Center and Softmax to form a joint loss function to minimize the intra-class distance and maximize the inter-class distance, which can improve the recognition ability.

Bionic Multilevel Composite Structures Design in Ag-CuO Contacts for Synergistically Enhancing Erosion and Impact Resistance.

The synergistic enhancement effect among multilevel composite structures is essential for improving the overall performance of contacts. Notably, natural bamboo demonstrates exceptional mechanical properties through the coupling effect of its structure. By reforming the microstructure of bamboo, an Ag-CuO contact, consisting of the Ag-rich regions, the CuO chain-like skeleton regions, and the CuO barrier layers, was inversely designed and controllably fabricated.

An innovative peptide toxicity prediction model based on multi-scale convolutional neural network and residual connection.

Motivation: Peptide toxicity is a critical concern in the development of peptide-based therapeutics, as toxic peptides can lead to severe side effects, including organ damage, immune reactions, and cytotoxicity. Predicting peptide toxicity accurately is essential to ensure the safety and efficacy of these drugs.

Results: In this study, we propose a novel model, ToxMSRC, to predict peptide toxicity using a combination of the continuous bag of words (CBOW) method from word2vec, synthetic minority over-sampling technique (SMOTE), multi-scale convolutional neural networks (CNN), and bidirectional long short-term memory (BiLSTM).

Multi level synergistic regulation of phosphorus slow-release performance through LDH modified biochar-based slow-release fertilizer.

Addressing global phosphorus resource scarcity and low utilization efficiency of traditional phosphorus fertilizers, this study presents an innovative strategy for modifying biochar-based slow-release phosphorus fertilizer (BSPF) with Mg-Al layered double hydroxides (LDH-BSPF), to enhance phosphorus slow-release performance through multi-level synergistic regulation. A novel slow-release system was fabricated via co-pyrolysis technology integrating biomass, phosphorus sources, and LDH. The effects of Mg/Al molar ratios (2:1-5:1) and pyrolysis temperatures (400-600 °C) on material structural properties were systematically investigated.

Durable Superhydrophobic Composite Coating Based on Hydrangea-like SiO Nanoparticles with Excellent Performance in Anticorrosion, Drag Reduction, and Antifouling.

Superhydrophobic coatings possess distinct wettability characteristics and hold significant potential in metal corrosion protection and underwater drag reduction. However, their practical application is often hindered by poor durability arising from the fragility of their micro/nanostructured surface roughness. In this study, a durable superhydrophobic coating featuring a hierarchical, hydrangea-like micro/nanostructure was successfully fabricated on an aluminum alloy substrate via a simple one-step cold-spraying technique.

Highly Efficient Tribocatalysis of Superhard SiC for Water Purification.

Mechanical friction offers a frequent approach for sustainable energy harvesting, as it can be captured and transformed into electricity by means of the triboelectric phenomenon. Theoretically, this electricity may subsequently be employed to drive electrochemical water purification processes. Herein, the experimental results confirm that the SiC particles effectively trigger the tribocatalytic decomposition of Rhodamine B (RhB).

Node Flexibility Unlocks Structural Adaptability and Guest Versatility of Anionocages.

Due to their exceptional nodal flexibility, anionocages are promising host molecules capable of mimicking the dynamic self-assembly and host-guest chemistry of proteins. However, their application has been limited by the challenges in constructing large internal cavities. Here, we present an effective strategy to overcome this limitation by enhancing node flexibility to improve both structural adaptability and guest encapsulation versatility.

Hierarchically Engineered Flame-Retardant Triboelectric Yarn Exhibiting Robust Mechanical Performance and Humidity-Boosted Electrical Output for Firefighting Applications.

As urbanization progresses and building complexity grows, fire safety gains prominence, driving up demand for intelligent, high-performance fire-fighting gear. This study develops a super-intelligent fireproof yarn (SIFY) with a coaxial structure via hierarchical textile assembly. The middle waterproof and flame-retardant layer and the outer high-strength flame-retardant poly(p-phenylene benzobisoxazole) fibers endow SIFY with top-notch flame-retardant performance (limiting oxygen index of 50.

Regulation of Microstructure and Absorption Properties of MXene Materials: Theoretical and Experimental.

This study systematically investigates the modulation mechanism of transition metal elements (Ti, Nb, Ta, V) on the microwave absorption performance of MXenes (TiCT, TiNbCTx, TiTaCT, TiVCT, NbCT, VCT). Using multiscale characterization techniques, the microstructure, elemental distribution, and surface chemical states of these materials are comprehensively analyzed. Integrated electromagnetic parameter measurements and theoretical calculations elucidate the physical mechanisms underlying their distinct microwave absorption behaviors.

In-situ loading of ag nanoparticles and sodium cefoperazone onto bacterial cellulose-based composite antimicrobial films for promoting wound healing.

This study focuses on bacterial cellulose (BC), a material with a three-dimensional network structure, excellent biocompatibility, and superior mechanical properties, to address its inherent limitations of insufficient antibacterial activity and poor rehydration capability. Silver nanoparticles (AgNPs) were synthesized in situ on the BC surface via a hydrothermal method, followed by immersion in polyethylene glycol (PEG) and cefoperazone sodium (CEFNa) solutions to prepare BC/Ag/PEG/CEFNa composite films. The BC films and composites were comprehensively characterized using techniques such as XRD, SEM, and FTIR spectroscopy.

Adaptive normalizing flows for solving Fokker-Planck equation.

The Fokker-Planck (FP) equation governs the probabilistic response of diffusion processes driven by stochastic differential equations (SDEs). Gaussian mixture models and deep learning solvers are two state-of-the-art methods for solving the FP equation. Although mixture models mostly depend on empirical sampling strategies and predefined Gaussian components, deep learning techniques suffer from inherent interpretability deficits and require excessively large training samples.