Search Results (3,031)

Intratumoral injections of macromolecules, such as biologics and immunotherapeutics, show promise in overcoming dose-limiting side effects associated with systemic injections and improve treatment efficacy. However, the retention of injectates in the tumor microenvironment is a major underappreciated challenge. High interstitial pressures and dense tumor architectures create shear forces that rapidly expel low-viscosity solutions post-injection. Injectable hydrogels may address these concerns by providing a viscoelastic delivery vehicle that shields loaded therapies from rapid expulsion from the tumor. A chitosan–glycerol hydrogel was thus developed and characterized with the goal of improving the injection retention of loaded therapeutics. The gelation parameters and mechanical properties of the hydrogel were explored to reveal a shear-thinning gel that is injectable through a 27-gauge needle. Biocompatibility studies demonstrated that the chitosan–glycerol hydrogel was nontoxic. Retention studies revealed significant improvements in the retention of model therapeutics when formulated with the chitosan–glycerol hydrogel compared to less-viscous solutions. Finally, release studies showed that there was a sustained release of model therapeutics of various molecular sizes from the hydrogel. Overall, the chitosan–glycerol hydrogel demonstrated injectability, enhanced retention, biocompatibility, and sustained release of macromolecules, indicating its potential for future clinical use in intratumoral macromolecule delivery. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

The cross-sectional dimensions of structural elements in a structure are design elements that need to be carefully designed and are related to the stiffness of the structure. Various optimization processes are applied to determine the optimum cross-sectional dimensions of beams or columns in structures. By repeating the optimization processes for multiple load scenarios, it is possible to create a data set that shows the optimum design section properties. However, this step means repeating the same processes to produce the optimum cross-sectional dimensions. Artificial intelligence technology offers a short-cut solution to this by providing the opportunity to train itself with previously generated optimum cross-sectional dimensions and infer new cross-sectional dimensions. By processing the data, the artificial neural network can generate models that predict the cross-section for a new structural element. In this study, an optimization process is applied to a simple tubular column and an I-section beam, and the results are compiled to create a data set that presents the optimum section dimensions as a class. The harmony search (HS) algorithm, which is a metaheuristic method, was used in optimization. An artificial neural network (ANN) was created to predict the cross-sectional dimensions of the sample structural elements. The neural architecture search (NAS) method, which incorporates many metaheuristic algorithms designed to search for the best artificial neural network architecture, was applied. In this method, the best values of various parameters of the neural network, such as activation function, number of layers, and neurons, are searched for in the model with a tool called HyperNetExplorer. Model metrics were calculated to evaluate the prediction success of the developed model. An effective neural network architecture for column and beam elements is obtained. Full article

Macrocyclic organic nanostructures have emerged as crucial components of functional supramolecular materials owing to their unique structural and chemical features, such as their distinctive “infinite” cyclic topology and tunable topology-dependent properties, attracting significant recent attention. However, the controlled synthesis of macrocyclic compounds with well-defined compositions and geometries remains a formidable challenge. On-surface synthesis, capable of constructing nanostructures with atomic precision on various substrates, has become a frontier technique for exploring novel macrocyclic architectures. This review summarizes the recent advances in the on-surface synthesis of macrocycles. It focuses on analyzing the synthetic mechanisms and conformational characterization of macrocycles formed through diverse bonding interactions, including both covalent and non-covalent linkages. This review elucidates the intricate interplay between the thermodynamic and kinetic factors governing macrocyclic structure formation across these bonding types and clarifies the critical influence of the reaction temperature and external conditions on the cyclization efficiency. Ultimately, this study offers design strategies for the precise on-surface synthesis of larger and more flexible macrocyclic compounds. Full article

A modular strategy for the molecular design of silicone-based antifoaming agents was developed by precisely controlling the architecture of poly (methylhydrosiloxane) (PMHS). Sixteen PMHS variants were synthesized by systematically varying the siloxane chain length (L1–L4), backbone composition (D₃T₁ vs. D₃₀T₁), and terminal group chemistry (H- vs. M-type). These structural modifications resulted in a broad range of Si-H functionalities, which were quantitatively analyzed and correlated with defoaming performance. The PMHS matrices were integrated with high-viscosity PDMS, a nonionic surfactant, and covalently grafted fumed silica—which was chemically matched to each PMHS backbone—to construct formulation-specific defoaming systems with enhanced interfacial compatibility and colloidal stability. Comprehensive physicochemical characterization via FT-IR, ¹H NMR, GPC, TGA, and surface tension analysis revealed a nonmonotonic relationship between Si-H content and defoaming efficiency. Formulations containing 0.1–0.3 wt% Si-H achieved peak performance, with suppression efficiencies up to 96.6% and surface tensions as low as 18.9 mN/m. Deviations from this optimal range impaired performance due to interfacial over-reactivity or reduced mobility. Furthermore, thermal stability and molecular weight distribution were found to be governed by repeat unit architecture and terminal group selection. Compared with conventional EO/PO-modified commercial defoamers, the PMHS-based systems exhibited markedly improved suppression durability and formulation stability in high-viscosity environments. These results establish a predictive structure–property framework for tailoring antifoaming agents and highlight PMHS-based formulations as advanced foam suppressors with improved functionality. This study provides actionable design criteria for high-performance silicone materials with strong potential for application in thermally and mechanically demanding environments such as coating, bioprocessing, and polymer manufacturing. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

Based on molecular dynamics simulation, we conducted a comprehensive study on the tensile behaviors and properties of the $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy with varying ${\gamma }^{\prime }$(Ni₃Al) phase volume fractions ($V\left({\gamma }^{\prime }\right))$ under high-temperature, high-strain-rate service environments. Our investigation revealed that the tensile behavior of the superalloy depends critically on the $V\left({\gamma }^{\prime }\right)$. When the $V\left({\gamma }^{\prime }\right)$ increased from 13.5 to 67%, the system’s tensile strength exhibited a non-monotonic response, peaking at $\mathrm{V}\left({\gamma }^{\prime }\right)$ = 40.3% before progressively decreasing. Conversely, the maximum uniform plastic strain decreased linearly and significantly when $V\left({\gamma }^{\prime }\right)$ increased. These results establish an atomistically informed framework that elucidates the composition–microstructure–property relationships in $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloys, specifically addressing how $V\left({\gamma }^{\prime }\right)$ governs variations in deformation mechanisms and mechanical performance. Furthermore, this work provides quantitative design paradigm for optimizing ${\gamma }^{\prime }$(Ni₃Al) precipitate architecture and compositional tuning in the Ni-based $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy. Full article

The Kekeya oilfield is located at the southwestern edge of the Tarim Basin, in the southern margin of the Yecheng depression, at the western end of the second structural belt of the northern foothills of the Kunlun Mountains. It is one of the important oil and gas fields in western China, with significant oil and gas resource potential in the X4-X5 section of the Xihepu Formation. This study focuses on the edge of the alluvial fan depositional system, employing various techniques, including core data and well logging data, to precisely characterize the sand body architecture and comprehensively analyze the reservoir architecture in the study area. First, the regional geological background of the area is analyzed, clarifying the sedimentary environment and evolutionary process of the Xihepu Formation. Based on the sedimentary environment and microfacies classification, the sedimentary features of the region are revealed. On this basis, using reservoir architecture element analysis, the interfaces of the reservoir architecture are finely subdivided. The spatial distribution characteristics of the planar architecture are discussed, and the spatial distribution and internal architecture of individual sand body units are analyzed. The study focuses on the spatial combination of microfacies units along the profile and their internal distribution patterns. Additionally, a quantitative analysis of the sizes of various types of sand bodies is conducted, constructing the sedimentary model for the region and revealing the control mechanisms of different sedimentary architectures on reservoir properties and oil and gas accumulation patterns. This study pioneers a quantitative model for alluvial fan fringe in gentle-slope basins, featuring the following: (1) lobe width-thickness ratios (avg. 128), (2) four base-level-sensitive boundary markers, and (3) a retrogradational stacking mechanism. The findings directly inform reservoir development in analogous arid-climate systems. This research not only provides a scientific basis for the exploration and development of the Kekeya oilfield but also serves as an important reference for reservoir architecture studies in similar geological contexts. Full article

With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and resistance to attacks. Advances in fractional calculus and memristive technologies offer new avenues for enhancing security through more complex and tunable dynamics. However, the practical deployment of high-dimensional fractional-order memristive chaotic systems in hardware remains underexplored. This study addresses this gap by presenting a secure image transmission system implemented on a field-programmable gate array (FPGA) using a universal high-dimensional memristive chaotic topology with arbitrary-order dynamics. The design leverages four- and five-dimensional hyperchaotic oscillators, analyzed through bifurcation diagrams and Lyapunov exponents. To enable efficient hardware realization, the chaotic dynamics are approximated using the explicit fractional-order Runge–Kutta (EFORK) method with the Caputo fractional derivative, implemented in VHDL. Deployed on the Xilinx Artix-7 AC701 platform, synchronized master–slave chaotic generators drive a multi-stage stream cipher. This encryption process supports both RGB and grayscale images. Evaluation shows strong cryptographic properties: correlation of $-6.1081\times {10}^{-5}$, entropy of 7.9991, NPCR of 99.9776%, UACI of 33.4154%, and a key space of $2^{1344}$, confirming high security and robustness. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

The demand for anion exchange membranes (AEMs) is growing due to their applications in water electrolysis, CO₂ reduction conversion and fuel cells, as well as water treatment, driven by the increasing energy demand and the need for a sustainable future. However, current AEMs still face challenges, such as insufficient permeability and stability in strongly acidic or alkaline media, which limit their durability and the sustainability of membrane fabrication. In this study, polyvinyl alcohol (PVA) and chitosan (CS) biopolymers are selected for membrane preparation. Zinc oxide (ZnO) and porous organic polymer (POP) nanoparticles are also introduced within the PVA-CS polymer blends to make mixed-matrix membranes (MMMs) with increased OH⁻ transport sites. The membranes are characterized based on typical properties for AEM applications, such as thickness, water uptake, KOH uptake, Cl⁻ and OH⁻ permeability and ion exchange capacity (IEC). The OH⁻ transport of the PVA-CS blend is increased by at least 94.2% compared with commercial membranes. The incorporation of non-porous ZnO and porous POP nanoparticles into the polymer blend does not compromise the OH⁻ transport properties. On the contrary, ZnO nanoparticles enhance the membrane’s water retention capacity, provide basic surface sites that facilitate hydroxide ion conduction and reinforce the mechanical and thermal stability. In parallel, POPs introduce a highly porous architecture that increases the internal surface area and promotes the formation of continuous hydrated pathways, essential to efficient OH⁻ mobility. Furthermore, the presence of POPs also contributes to reinforcing the mechanical integrity of the membrane. Thus, PVA-CS bio-based membranes are a promising alternative to conventional ion exchange membranes for various applications. Full article

Organoids and microphysiological systems (MPSs) have emerged as physiologically relevant platforms that recapitulate key structural and functional features of human organs, tissues, and microenvironments. As one of the essential components that define the success of these systems, hydrogels play the central role of providing a three-dimensional, biomimetic scaffold that supports cell viability, spatial organization, and dynamic signaling. Natural polymer-based hydrogels, derived from materials such as collagen, gelatin, hyaluronic acid, and alginate, offer favorable properties including biocompatibility, degradability, and an extracellular matrix-like architecture. This review presents recent advances in the design and application of such hydrogels, focusing on crosslinking strategies (physical, chemical, and hybrid), the viscoelastic characteristics, and stimuli-responsive behaviors. The influence of these materials on cellular processes, such as stemness maintenance, differentiation, and morphogenesis, is critically examined. Furthermore, the applications of organoid culture and dynamic MPS platforms are discussed, highlighting their roles in morphogen delivery, barrier formation, and vascularization. Current challenges and future perspectives toward achieving standardized, scalable, and translational hydrogel systems are also addressed. Full article