Search Results (3,242)

Food packaging serves a pivotal role in daily life, facilitating the efficient transportation of food and extending its shelf life. Petroleum-derived plastic packaging is extensively employed; however, its non-biodegradable nature poses significant environmental pollution and ecological degradation. Natural polymers (e.g., proteins such as gelatin and corn gluten protein; polysaccharides including pectin, chitosan, starch, cellulose, and alginate) and synthetic polymers (e.g., polyvinyl alcohol, polylactic acid, and polyhydroxyalkanoates) can be utilized to fabricate food packaging films, thereby achieving green and eco-friendly objectives. Nevertheless, the inferior mechanical strength and inadequate antibacterial activity of biopolymer-based packaging have restricted their practical applications. In recent years, nanomaterials (e.g., nanoparticles, nanotubes, nanofibers, and nanosheets) have been employed to enhance the performance of food packaging, emerging as a research hotspot. Notably, nanoparticles possess unique properties, including a high specific surface area, excellent dispersibility, and multifunctionality, which enables them to be easily incorporated into film matrices. Owing to their unique chemical structures, nanoparticles form strong interactions with film matrices, leading to a denser spatial structure. This not only markedly enhances the mechanical strength of the films, but also simultaneously improves their antibacterial and antioxidant capabilities. This review classifies and summarizes common nanomaterials based on their chemical compositions, providing a theoretical foundation and technical reference for the future development and application of nanomaterials in the field of bio-based active food packaging. Full article

The growth of lithium dendrites and the associated “dead lithium” issue significantly impair the performance and cycle life of lithium metal batteries. This study utilizes a phase-field model under constant-current discharge conditions to simulate the dissolution process of lithium dendrites. The results demonstrate that the non-uniform dissolution of lithium dendrites is a primary cause of their stripping and subsequent dead lithium formation. Specifically, a high charging voltage and a high reaction rate constant aggravate dendrite growth and dead lithium accumulation. Although a high discharging voltage accelerates dendrite dissolution, it readily induces stripping at the dendrite roots, generating more dead lithium. In contrast, increasing the temperature, enhancing the interface mobility, adjusting the anisotropy strength to a moderate level, and constructing semi-circular initial nuclei can effectively mitigate dead lithium by promoting a more uniform dissolution process. This research provides a theoretical foundation for optimizing battery operational parameters and electrode designs to improve capacity and safety. Full article

Comprehensive studies of hydrogen embrittlement in high-strength austenitic alloys under cryogenic conditions are scarce, leaving the combined effect of hydrogen charging and extreme temperatures largely unexplored. Given the demands of cryogenic applications such as hydrogen storage and transport, understanding material behavior under these conditions is crucial. Here, we present the first systematic study of hydrogen’s effect at liquid helium temperature (4.2 K) on the mechanical properties of precipitation hardened austenitic alloys, specifically the nickel-based Alloy 718 and austenitic stainless steel A286. Both materials were subjected to pressurized hydrogen charging at 473 K followed by slow strain rate tensile testing at room temperature and at 4.2 K. Hydrogen charging caused significant ductility loss at room temperature in both alloys. In contrast, testing at 4.2 K resulted in increased strength and no evidence of hydrogen embrittlement. Notably, materials pre-charged with hydrogen and tested at 4.2 K exhibited higher stress drop amplitudes and increased strain accumulation during serration events, suggesting persistent hydrogen–dislocation interactions and possible enhanced dislocation pinning by obstacles such as Lomer–Cottrell locks. These results indicate that while hydrogen influences plasticity mechanisms at cryogenic temperatures, embrittlement is suppressed, providing new insight into the safe development of austenitic alloys in cryogenic hydrogen environments. Full article

Fiber-reinforced polymer (FRP) composites, particularly glass fiber-reinforced polymer (GFRP), are increasingly utilized in civil engineering due to their high strength-to-weight ratio, corrosion resistance, and environmental sustainability compared to steel. Shear connectors in FRP–concrete hybrid beams are critical for effective load transfer, yet their behavior under static loads remains underexplored. This study aims to investigate the shear strength, stiffness, and failure modes of GFRP, CFRP, AFRP, and stainless-steel shear connectors in FRP–concrete hybrid beams through a comprehensive parametric analysis, addressing gaps in material optimization, bolt configuration, and design guidelines. A validated finite element model in Abaqus was employed to simulate push-out tests based on experimental data. The parameters analyzed included shear connector material (GFRP, CFRP, AFRP, and stainless steel), bolt diameter (16–30 mm), number of bolts (1–6), longitudinal spacing (60–120 mm), embedment length (40–70 mm), and concrete compressive strength (30–70 MPa). Shear load–slip (P-S) curves, ultimate shear load (P), secant stiffness (K₁), and failure modes were evaluated. CFRP bolts exhibited the highest shear capacity, 26.50% greater than stainless steel, with failure dominated by flange bearing, like AFRP and stainless steel, while GFRP bolts failed by shear failure of bolt shanks. Shear capacity increased by 90.60%, with bolt diameter from 16 mm to 30 mm, shifting failure from bolt shank to concrete splitting. Multi-bolt configurations reduced per-bolt shear capacity by up to 15.00% due to uneven load distribution. Larger bolt spacing improved per-bolt shear capacity by 9.48% from 60 mm (3d) to 120 mm (6d). However, in beams, larger spacing reduced the total number of bolts, decreasing overall shear resistance and the degree of shear connection. Higher embedment lengths (he/d ≥ 3.0) mitigated pry-out failure, with shear capacity increasing by 33.59% from 40 mm to 70 mm embedment. Increasing concrete strength from 30 MPa to 70 MPa enhanced shear capacity by 22.07%, shifting the failure mode from concrete splitting to bolt shank shear. The study highlights the critical influence of bolt material, diameter, number, spacing, embedment length, and concrete strength on shear behavior. These findings support the development of FRP-specific design models, enhancing the reliability and sustainability of FRP–concrete hybrid systems. Full article

This study examines the influence of pyrolysis temperature on the physicochemical characteristics of malt-derived biochar (BC) and its effect on the performance of cement mortars. Malt biomass, a by-product of the brewing industry, was subjected to pyrolysis at 300 °C and 500 °C, followed by high-energy ball milling to produce nanoscale biochar. Characterization using FTIR, Raman spectroscopy, XRD, BET, SEM, and XRF revealed that BC500 possessed higher graphitic ordering, larger specific surface area (110 m₂/g), and smaller pore size compared to BC300, which exhibited greater hydrophobicity. Incorporation of BC500 into cement mortars at 0.25–1.0 wt.%, with and without superplasticizer, resulted in up to a 20.6% increase in compressive strength and a significant reduction in water absorption. These enhancements are attributed to the internal curing effect of biochar, its refined pore structure, and improved interfacial bonding with hydration products. The findings demonstrate that optimized malt biochar serves as a sustainable additive that improves the mechanical performance and durability of cementitious materials while advancing circular economy principles through the valorization of industrial malt waste and the mitigation of the environmental impact of cement production. Full article

To address the problem of corrosion, glass fiber-reinforced polymer (GFRP) bars have been introduced as a viable alternative to conventional steel reinforcement in concrete structures. While extensive research has been conducted on the flexural behavior of RC beams reinforced with steel and GFRP bars over both normal-term and long-term periods, studies focusing on fresh concrete beams are almost non-existent. Consequently, this research investigates the impact of steel and GFRP longitudinal reinforcement, as well as the influence of varying concrete compressive strengths, on the flexural behavior of RC beams. The study employs 3-point bending experiments and machine learning (ML) predictive analyses. Specifically, the short-term (fresh) concrete reinforcement compatibility and the effects of steel and GFRP bar reinforcements on beam flexural behavior were examined across three concrete compressive strength categories: low (C25), moderate (C35), and high (C50). A notable contribution of this research is the application of different ML regression models, utilizing Python’s library, for deflection prediction of RC beams. The failure mechanisms of the beams under static loading conditions were analyzed, revealing that composite bar RC beams failed through flexural cracking and demonstrated ductile behavior, whereas steel bar RC beams exhibited brittle failure characterized by shear cracks and sudden failure modes. The ML regression models successfully predicted the deflection values of RC beams under ultimate loads, achieving an average accuracy of 91.3%, which was deemed highly satisfactory. Among the 18 beams tested, the highest ultimate load was obtained for the SC50-1 beam at 87.46 kN. In contrast, while the steel-reinforced beams exhibited higher load-bearing capacities, it was observed that the GFRP-reinforced beams showed greater deflection and ductility, particularly in beams with low and medium concrete strengths. Based on these findings, it is recommended that the Gradient Boosting Regressor, an AI regression model, be utilized to guide researchers in evaluating the load-carrying and bending capacity of structural beam elements. Full article

This paper proposes a multi-objective optimization method that integrates deep neural networks (DNN) with gray relational analysis (GRA) to optimize lay-up configurations for carbon fiber-reinforced plastic (CFRP) automotive components. Specifically, a lab-scale CFRP B-pillar structure was investigated to simultaneously maximize structural strength and failure safety. A DNN surrogate model was trained using finite element simulations of 2000 random stacking sequences to achieve high predictive accuracy. The trained model was then used to evaluate all possible lay-up combinations to derive Pareto optimal solutions. Gray relational analysis was subsequently employed to select the final optimal configurations based on designer preferences. The selected lay-up designs demonstrated improvements in both strength and failure safety. To validate the proposed framework, laboratory-scale CFRP B-pillar was fabricated using a prepreg compression molding process and subjected to bending tests. The experimental results confirmed an error below 5% and failure trends consistent with the simulation results, thereby validating the reliability of the proposed method. The proposed DNN-GRA approach enables efficient multi-objective optimization with reduced computational effort and flexibility in reflecting different engineering priorities. Full article

Marine oil spill incidents are one of the major global marine pollution issues, which pose significant threats to ocean ecosystems. However, traditional monitoring methods often suffer from time delays, high costs, and limited real-time capability, making them inadequate for timely and large-scale oil spill detection. With the development of remote sensing (RS) technology and artificial intelligence (AI) methods, as well as the increasing frequency of marine oil spill accidents, plenty of AI-based methods using RS imagery have been proposed for more efficient and accurate oil spill monitoring. This review presents a comprehensive and systematic overview of recent progress in marine oil spill analysis using RS imagery, emphasizing the integration of AI methods across three key tasks: detection, classification, and thickness estimation. Specifically, we first introduce the main types of RS data and discuss the significance of publicly available datasets, which can facilitate method validation and model comparison. Second, we briefly review the application of RS imagery from different sensors in oil spill detection, highlighting the strengths of various spectral and polarimetric methods. Third, we summarize advances in oil spill classification, including AI-based methods that enable differentiation between mineral oil, biogenic films, and various emulsified oils. Fourth, we discuss emerging techniques for oil spill thickness estimation. Finally, we analyze the challenges of existing methods and future directions, including the need for real-time monitoring, the integration of multi-source RS data, and the development of robust models that can generalize across different environmental conditions. This review adopts a comprehensive perspective from both AI methods and RS technology, provides a systematic overview of recent advancements, identifies critical gaps in current methodologies, and serves as a valuable reference for researchers and practitioners working on oil spill monitoring. Full article

Acrylic solid surface composites made of poly (methyl methacrylate) (PMMA) and aluminum trihydrate, Al(OH)₃ (ATH) are widely used in furniture and interior applications. However, independent brand comparative data, especially on density-normalized (“specific”) properties, remain limited. This study quantifies the flexural response of 11 commercial sheets (6, 8, and 12 mm, including one translucent) under ISO 178 three-point bending and evaluates the effects of heating and cooling relevant to thermoforming. The density is concentrated in the range 1680–1748 kg/m³ (weighted mean of 1712 kg/m³). The flexural strength ranged between 51 and 79 MPa, divided into three groups—high (76–79 MPa), medium (63–67 MPa), and low (51–56 MPa) levels, while the modulus ranged between 7700 and 9400 MPa with a narrow dispersion. The strength showed no significant correlation with density, while the modulus increased with density, indicating that stiffness is composition-dominated, while strength is influenced by factors related to microstructural defects/particle boundaries. Heating at 160 °C and subsequent cooling have a significant influence on flexural strength and strain. Flexural strength increased by an average of approximately 7%, and flexural strain increased by approximately 12%, while the modulus remained virtually unchanged (within ±0.5%); additionally, shock cooling did not bring any benefits. The density-normalized parameters (σ/ρ, E/ρ) reflected these trends, allowing for a more accurate comparison when limited by mass or deformation. Overall, the results are broadly consistent with manufacturers’ declarations and demonstrate that thermoforming-relevant heating at 160 °C, followed by cooling, can be used not only to improve formability but also to modestly increase flexural strength and strain without compromising stiffness. Full article

As the most commercially developed metal additive process, laser powder bed fusion (LPBF) is vital to advancing several industry sectors, enabling high-precision part production across aerospace, biomedical, and manufacturing industries. Al 7075 alloy offers low density and high-specific strength yet faces LPBF challenges such as hot cracking and porosity due to rapid solidification, thermal gradients, and a wide freezing range. To address these challenges, this study proposes an integrated computational and experimental framework to enhance the LPBF processability of Al 7xxx alloys by compositional modification. Using the Calculation of Phase Diagram approach, printable Al 7xxx compositions were designed by adding grain refiners (V and/or Ti) and a eutectic solidification enhancer (Mg) to Al 7075 alloy to enable grain refinement and eutectic solidification. Subsequent LPBF experiments and characterization tests, such as metallography (scanning electron microscopy), energy-dispersive X-ray spectroscopy, X-ray diffraction, and X-ray micro-computed tomography, confirmed the production of refined microstructures with reduced defects. This study contributes to existing approaches for producing high-quality Al 7xxx alloy parts without significant compositional deviations using an integrated computational and experimental approach. Finally, aligning with the Materials Genome Initiative, this study contributes to the development and industrial adoption of advanced materials. Full article

High-strength concrete (HSC) is widely used in coastal regions, but its durability and structural safety is threatened by chloride ingress in marine environments. This study investigates the effects of different curing methods, normal, steam, and high-temperature autoclave on the chloride resistance of HSC using the electric flux test. A critical chloride concentration of 4.5% was identified, and accelerated deterioration tests were conducted to evaluate mechanical properties development (compressive strength, elastic modulus, toughness, specific toughness) under the various curing conditions. Additionally, the development of hydration products and microstructural characteristics were analyzed to elucidate the mechanisms underlying the observed differences. The results indicate that steam and autoclave curing enhance cement hydration and the initial mechanical properties of HSC but also increase permeability and susceptibility to chloride ion penetration compared to normal curing. Chloride penetration was found to be most severe at moderate chloride concentrations (~4.5%), while higher concentrations resulted in reduced ion migration. Although intensive curing under elevated temperature and pressure improves early strength and stiffness, it accelerates mechanical degradation under chloride exposure, highlighting a trade-off between short-term performance and long-term durability. Full article

Localized ¹H magnetic resonance spectroscopy (MRS) is a powerful tool in pre-clinical and clinical neurological research, offering non-invasive insight into neurochemical composition in localized brain regions. Zebrafish (Danio rerio) are increasingly being utilized as models in neurological disorder research, providing valuable insights into disease mechanisms. However, the small size of the zebrafish brain and limited MRS sensitivity at low magnetic fields hinder comprehensive neurochemical analysis of localized brain regions. Here, we investigate the potential of ultra-high-field (UHF) MR systems, particularly 28.2 T, for this purpose. This present study pioneers the application of localized ¹H spectroscopy in zebrafish brain at 28.2 T. Point resolved spectroscopy (PRESS) sequence parameters were optimized to reduce the impact of chemical shift displacement error and to enable molecular level information from distinct brain regions. Optimized parameters included gradient strength, excitation frequency, echo time, and voxel volume specifically targeting the 0–4.5 ppm chemical shift regions. Exceptionally high-resolution cerebral metabolite spectra were successfully acquired from localized regions of the zebrafish brain in voxels as small as 125 nL, allowing for the identification and quantification of major brain metabolites with remarkable spectral clarity, including lactate, myo-inositol, creatine, alanine, glutamate, glutamine, choline (phosphocholine + glycerol-phospho-choline), taurine, aspartate, N-acetylaspartyl-glutamate (NAAG), N-acetylaspartate (NAA), and γ-aminobutyric acid (GABA). The unprecedented spatial resolution achieved in a small model organism enabled detailed comparisons of the neurochemical composition across distinct zebrafish brain regions, including the forebrain, midbrain, and hindbrain. This level of precision opens exciting new opportunities to investigate how specific diseases in zebrafish models influence the neurochemical composition of specific brain areas. Full article

This study investigates the valorization of municipal solid waste incineration (MSWI) residues—specifically bottom ash with slag (BA + S) and fly ash (FA)—through alkaline activation in geopolymer and cementitious systems. The research demonstrates that alkali activation significantly improves mechanical properties, with compressive strengths up to 45.9 MPa for cement mortars and 33.2 MPa for geopolymers. A key innovation includes the quantification of hydrogen gas release during activation, with up to 72.5 dm³/kg H₂ from BA + S, offering insights into binder design and potential green hydrogen recovery. Environmental leachability assessments confirmed that activated BA + S immobilizes heavy metals effectively, although FA showed higher barium and lead leaching. Morphological analysis (SEM, granulometry) revealed microstructural changes enhancing reactivity. Additionally, a practical swelling test is proposed for early detection of expansion risk. The findings contribute to the development of sustainable, high-performance binders from waste, with implications for circular economy and energy valorization strategies. Full article

Over the past few decades, Fc fragment-conjugated proteins, such as Protein A, have been extensively utilized across a range of applications, including antibody purification, site-specific immobilization of antibodies, and the development of biosensing platforms. In this study, building upon our group prior research, we designed and screened an affinity DNA functional ligand (A-DNAFL) and experimentally validated its binding affinity (K_D = 6.59 × 10⁻⁸) toward mouse IgG antibodies, whose binding performance was comparable to that of protein A. Systematic evaluations were performed to assess the binding efficiency under varying pH levels and ionic strength conditions. Optimal antibody immobilization was achieved in PBST-B buffer under physiological pH 7.2–7.4 and containing approximately 154 mM Na⁺ and 4 mM K⁺. Two competitive binding assays confirmed that the A-DNAFL binds to the Fc fragment of murine IgG antibody. Furthermore, molecular docking simulations were employed to investigate the interaction mode, revealing key residues involved in binding as well as the contributions of hydrogen bonding and hydrophobic interactions to complex stabilization. Leveraging these insights, A-DNAFL was utilized as a tool for oriented immobilization of antibodies on the sensing interface, enabling the construction of an immunosensor for ricin detection. Following optimization of immobilization parameters, the biosensor exhibited a detection limit of 30.5 ng/mL with the linear regression equation is lg(Response) = 0.329 lg(C_ricin) − 2.027 (N = 9, R = 0.938, p < 0.001)—representing a 64-fold improvement compared to conventional protein A-based methods. The system demonstrated robust resistance to nonspecific interference. Sensing interface reusability was also evaluated, showing only 8.55% signal reduction after two regeneration cycles, indicating that glycine effectively elutes bound antibodies while preserving sensor activity. In summary, the A-DNAFL presented in this study represents a novel antibody-directed immobilization material that serves as a promising alternative to protein A. It offers several advantages, including high modifiability, low production cost, and a relatively small molecular weight. These features collectively contribute to its broad application potential in biosensing, antibody purification, and other areas of life science research. Full article

Background/Objectives: This exploratory systematic review aims to analyze the influence of isolated muscle flexibility training on the reduction of chronic pain symptoms in older adults aged 65 years or more. Articles were selected from the Web of Science, PubMed, and Scopus databases, using the EndNote software for reference management. The selection process followed the PICOS framework and the PRISMA 2020 guidelines, and the review protocol was registered in the PROSPERO database. Methods: The inclusion criteria comprised randomized controlled trials with participants aged 65 or older, evaluating the effect of flexibility training as a standalone intervention on chronic pain, and published in English or Portuguese. Studies were excluded if they involved multimodal training, did not specify participants’ ages, evaluated only acute or postoperative pain, or were not peer-reviewed articles. Results: From an initial pool of 1390 articles, only three met all criteria and were included in the final analysis. These studies—conducted in China (n = 2) and the United States (n = 1)—showed moderate methodological quality (PEDro score = 7/10). Two trials applied Proprioceptive Neuromuscular Facilitation (PNF) in participants with knee osteoarthritis, while the third compared a flexibility-based program to combined strength and aerobic training in a healthy elderly population. All studies reported significant reductions in chronic pain symptoms following flexibility training interventions. Conclusions: The reviewed evidence suggests that muscle flexibility training, particularly using techniques like PNF, may be a promising therapeutic strategy to mitigate specific chronic pain-related symptoms in older adults, particularly reductions in joint stiffness, movement discomfort, and pain intensity associated with osteoarthritis. However, the limited number of high-quality trials and heterogeneity in protocols and pain assessment tools highlight the need for further research. Full article