Search Results (769)

Carboxymethyl hydroxypropyl cellulose (CMHPC) combines the advantages of both carboxymethyl and hydroxypropyl substitutions, exhibiting superior solubility, viscosity characteristics, and enhanced salt tolerance compared to carboxymethyl cellulose (CMC). This study presents an optimized synthesis route for CMHPC through homogeneous hydroxypropylation of CMC under alkaline conditions. The effects of key reaction parameters, including propylene oxide amount and reaction time, on the structure and resulting properties were systematically investigated. The resulting CMHPC were comprehensively characterized using FTIR, solid state ¹³C NMR, and scanning electron microscopy (SEM), etc., confirming the successful hydroxypropyl group incorporation and morphological changes. In our findings, the suitable concentrations for NaOH and CMC were 5% and 4%, respectively, which could balance the yield and solution fluidity. CMHPC exhibited a much faster dissolution speed (3–5 min) than that of CMC (>30 min), indicating markedly enhanced hydrophilicity and solubility. Moreover, CMHPC also exhibited improved salt and acidity tolerance due to the steric hindrance of hydroxypropyl groups. CMHPC was also used to modify recycled coarse aggregate (RCA), and the results indicated that CMHPC could enhance the surface compactness and structural integrity of RCA. Moreover, CMHPC effectively improved the water resistance of RCA by constructing a physical barrier and optimizing the pore structure of the aggregate. This research provides valuable insights into the fabrication of modified cellulose ethers in homogeneous systems and offers a practical pathway for producing high-value cellulose derivatives with tailored properties, particularly for potential construction applications. Full article

The study systematizes the current state of knowledge on the morphological diversity of dispersed-phase particles in the most widely used lubricating greases, encompassing their shape, size, surface structure, and overall geometry. The extensive discussion of the diversity of grease thickener particles is supplemented with their microscopic images. Particular emphasis is placed on the influence of thickener particle morphology, the degree of their aggregation, and interparticle interactions on the rheological, mechanical, and tribological properties of grease formulations. The paper reviews recent advances in investigations of grease microstructure, with special emphasis on imaging techniques—ranging from dark-field imaging, through scanning electron microscopy, to atomic force microscopy—together with a discussion of their advantages and limitations in the assessment of particle morphology. A significant part of the work is devoted to rheological studies, which enable an indirect evaluation of the structural state of grease by analyzing its response to shear and deformation, thereby allowing inferences to be drawn about the micro- and mesostructure of lubricating greases. The historical development of rheological research on lubricating greases is also presented—from simple flow models, through the introduction of the concepts of viscoelasticity and structural rheology, to modern experimental and modeling approaches—highlighting the close relationships between rheological properties and thickener structure, manufacturing processes, composition, and in-service behavior of lubricating greases, particularly in tribological applications. It is indicated that contemporary studies confirm the feasibility of tailoring the microstructure of grease thickeners to specific lubrication conditions, as their characteristics fundamentally determine the rheological and tribological properties of the entire system. Full article

The electrochemical reduction of CO₂ to ethanol represents a sustainable alternative to recycle CO₂ into a value-added product, yet achieving high selectivity and efficiency remains a challenge. This work explores Cu-based catalysts supported on SiO₂ and ZrO₂, with and without ZnO doping, for ethanol production in a continuous flow-cell system. Gas diffusion electrodes are fabricated using commercial catalysts with varying Cu loadings (5–10%) and ZnO contents (2–3.5%). Comprehensive characterization by XPS confirms the presence of Cu²⁺ and Zn²⁺ species, while SEM reveals that ZnO incorporation improves surface uniformity and aggregate distribution compared to undoped samples. Electrochemical tests demonstrate that 10% Cu on SiO₂ achieves a Faradaic efficiency of 96% for ethanol at −3 mA cm⁻², outperforming both doped catalysts and previously reported materials. However, efficiency declines at higher current densities, indicating a trade-off between selectivity and productivity. ZnO doping enhances C₂⁺ product formation but does not surpass the undoped catalyst in ethanol selectivity. These results underline the importance of catalyst composition, support interactions, and operating conditions, and point to further optimization of electrode architecture and cell configuration to sustain high ethanol yields under industrially relevant conditions. Full article

In an effort to reduce global dependence on fossil-based polymers and advance toward a more sustainable materials industry, research over recent decades has increasingly focused on the development of bio-based polymers and broadening their potential applications. Within this context, the present study investigates nanocomposites based on poly(butylene succinate-co-butylene adipate) (PBSA), reinforced with two types of nanofillers: silicon dioxide nanoparticles (SiO₂ NPs) and cellulose nanofibrils (CNFs). The main objective of this work is to examine how the morphology, geometry, and chemical nature of the nanofillers influence the thermal, mechanical, and barrier properties of PBSA, as well as its biodegradability. For each nanofiller, three formulations were prepared, containing 1, 2, and 5 wt% of filler, respectively. Scanning electron microscopy (SEM) analysis confirmed good dispersion and minimal aggregation in the SiO₂-based systems, whereas marked aggregation was observed in the CNF-based samples. Thermal analysis indicated that the intrinsic thermal properties of neat PBSA were largely preserved. Mechanical testing revealed improvements in both the elastic modulus and elongation at break for most nanocomposite samples. In particular, CNFs provided the most consistent reinforcing effect, with enhancements of approximately 40% in the elastic modulus (495.4 vs. 356.4 GPa in neat PBSA) and 52% in elongation at the break (185.1 vs. 122.0% in neat PBSA) with 5 wt% loading. Additionally, the incorporation of nanofillers did not alter the surface hydrophilicity, but it did improve the oxygen barrier performance and enhanced disintegration under composting conditions. Overall, these findings demonstrate the promising potential of PBSA-based nanocomposites for sustainable rigid packaging applications. Full article

The vibration compaction behavior of fully graded fresh concrete differs fundamentally from that of conventional two-graded concrete. Based on measured vibration responses of an internal vibrator and sinking-ball tests, an energy transfer model for fully graded concrete was established by incorporating the effects of aggregate-specific surface area, paste–aggregate ratio, dynamic damping, and natural frequency, and the spatiotemporal attenuation of vibration energy in fresh concrete was systematically analyzed. Experimental results indicate that fully graded concrete exhibits a higher energy absorption capacity during the early stage of vibration, with a maximum energy absorption rate of 423 W and a peak energy transfer efficiency of 76.3%, both of which are significantly higher than those of two-graded concrete at the same slump. However, as a dense aggregate skeleton rapidly forms, the energy absorption efficiency of fully graded concrete decreases more rapidly during the middle and later stages of vibration, showing a characteristic pattern of “high initial absorption followed by rapid attenuation.” Through segregation assessment and porosity analysis, a safe vibration energy range for fully graded concrete was quantitatively determined, with lower and upper energy thresholds of 159.7 J·kg⁻¹ and 538.5 J·kg⁻¹, respectively. In addition, the experiments identified recommended vibration durations of 30–65 s and effective vibration influence radii of 22–85 mm for fully graded concrete under different slump conditions. These findings provide a quantitative basis for the control of vibration parameters and energy-oriented construction of fully graded concrete. Full article

This study examines how the Alkali–Silica Reaction (ASR) modifies chloride transport and chloride-induced corrosion (CIC) in reinforced concrete beams. Non-reactive and reactive concrete beams were cast with blue metal and dacite aggregates and subjected to a two-stage exposure: (i) alkali-rich immersion at 38 °C to induce ASR, and (ii) impressed-current CIC and NT BUILD 492 chloride migration testing. Microstructural changes were characterized using SEM–EDS and TGA. The reactive specimens developed extensive surface cracking, but after one year of ASR exposure, exhibited 47–53% lower non-steady-state migration coefficients (Dnssm: 7.03–8.02 × 10⁻¹² m²/s) than the non-reactive beam (15.09 × 10⁻¹² m²/s). After two years, Dnssm was reduced by approximately 37–56% (4.78–6.93 vs. 10.92 × 10⁻¹² m²/s). Crack mapping confirmed higher crack density and width in reactive beams, while SEM–EDS and TGA evidenced Ca depletion and the formation of C–(N,K)–S–H gels, which fill cracks and refine the pore structure. Electrical resistance monitoring showed earlier corrosion initiation in ASR-damaged beams but less pronounced resistance loss during the propagation phase. Overall, the results indicate that ASR can initially accelerate corrosion initiation through microcracking and reduced resistivity, but long-term gel deposition can partially seal transport paths and lower chloride migration under the specific conditions of this study. Full article

Accurate characterization of seafloor sediment properties is critical for marine engineering design, resource assessment, and environmental management. Sidescan sonar offers efficient wide-area mapping capabilities, yet establishing robust quantitative relationships between acoustic backscatter intensity and sediment texture remains challenging, particularly in heterogeneous coastal environments. This study investigates the correlation between sidescan sonar backscatter intensity and sediment grain size parameters in waters southwest of Hainan Island, China. High-resolution acoustic data (450 kHz) were acquired alongside surface sediment samples from 18 stations spanning diverse sediment types. Backscatter intensity, represented by grayscale values, was systematically compared with grain size distributions and individual size fractions. Results reveal that mean grain size shows no meaningful correlation with backscatter intensity; however, fine sand fraction content (0.075–0.25 mm) exhibits a strong negative linear relationship (R² = 0.87 under optimal conditions). Distribution-level analysis demonstrates that backscatter variability mirrors sediment textural complexity, with coarse sediments producing broad, elevated intensity distributions and fine sediments yielding narrow, suppressed distributions. Inter-survey variability highlights the sensitivity of absolute intensity values to environmental conditions during acquisition. Spatial distribution analysis reveals that sediment grain size follows a systematic NE-SW gradient controlled by hydrodynamic energy, with notable local anomalies controlled by reef structures (producing coarse bioclastic sediment) and topographic sheltering (maintaining fine-grained deposits in shallow areas). These findings provide a quantitative basis for fraction-specific acoustic classification approaches while emphasizing the importance of multi-scale analysis incorporating both regional hydrodynamic trends and local morphological controls. The established relationship between fine sand abundance and acoustic response enables semi-quantitative sediment prediction from remotely sensed data, supporting improved seafloor mapping protocols for offshore infrastructure siting, aggregate resource evaluation, and coastal zone management in morphologically complex environments. Full article

Various methods for classifying and evaluating the shape, size, and surface texture of sand particles are examined, highlighting their impact on concrete mixture properties. This study emphasizes the role of particle morphology in determining concrete workability and segregation, particularly in glass-fiber-reinforced (GRC) thin-layer concrete for building facade panels. The effects of different aggregate types on concrete workability and segregation are analyzed, showing that aggregates with spherical particles and a lower elongation index improve mixture consistency and reduce segregation. Three types of fine aggregates were used (instead of quartz sand in the mixtures, natural sand and granite screenings were chosen, which would be a sustainable alternative to quartz sand), and thin-layer glass-fiber-reinforced concrete using aggregates of different shapes was characterized by layering the mixture. The workability and segregation of fine-grained fiberglass-reinforced concrete mixtures depend on the aggregate particles’ shape. Up to 50% of quartz sand can be replaced with granite siftings or natural sand, as measured by the segregation index, as calculated according to the method proposed in this paper. Increasing the amount of natural sand from 10% to 50% also increases the segregation index from 1.9 to 2.6, and when using granite sifting aggregates, it rises from 2.6 to 3.5. Aggregates with spherical particles are more suitable for this thin-layer GRC concrete, if we examine the consistency parameters of fresh concrete and the possibilities of working with it in real production conditions. Full article

Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%. Full article

Salt permeation erosion is a key factor leading to the deterioration of service performance and shortening the lifespan of asphalt pavement in salt-rich areas. In this environment, the combined action of water and salt accelerates the decline in the asphalt–aggregate interface, leading to distress, such as raveling and loosening, which severely limit pavement durability. The authors systematically reviewed the research progress on asphalt–aggregate adhesion in a saline corrosion environment and discussed the complex mechanisms of adhesion degradation driven by intrinsic factors, including aggregate chemical properties, surface morphology, asphalt components, and polarity, as well as environmental factors, such as moisture, salt, and temperature. We also summarized multi-scale evaluation methods, including conventional macroscopic tests and molecular dynamics simulations, and revealed the damage evolution patterns caused by the coupled effects of water, salt, heat, and mechanical forces. Based on this, the effectiveness of technical approaches, such as asphalt modification and aggregate modification, is explored. Addressing the current insufficiency in research on asphalt adhesion under complex conditions in salt-rich areas, this study highlights the necessity for further research on mechanisms of multi-environment interactions, composite salt erosion simulation, development of novel anti-salt erosion materials, and intelligent monitoring and early warning, aiming to provide a theoretical basis and technical support for the weather-resistant design and long-term service of asphalt pavement in salt-rich regions. Full article

Background/Objectives: The treatment of bacterial infections is complicated by emerging antibiotic resistance. This paper identifies a novel approach with a nanoparticle that targets bacterial surface charge and is responsive to the nutrient environment (i.e., glucose) and presence of metabolically active bystander species (i.e., amylase secretion) within microbial communities. Methods: Thus, metabolically responsive composite nanoparticles (440 ± 58 nm) were fabricated via electrohydrodynamic jetting of a cationic starch polymer incorporating 5–7 nm copper nanoparticles (0.3 wt%). Starch was selected as the base polymer, as it is a common carbon source for amylase-producing bacterial communities, in particular under glucose-limited growth conditions. Results: The resulting positively charged particles effectively associated with Gram-positive Staphylococcus aureus, forming co-aggregates with bacterial cells and exhibiting antibacterial activity tenfold greater than free copper nanoparticles. In co-cultures of S. aureus and the amylase-producing bystander species, Bacillus subtilis, enzymatic degradation of the copper–starch nanoparticles increased antibacterial activity against S. aureus by 44%. Conclusions: This work highlights the potential for metabolically regulated particles as a novel paradigm for selective, narrow-spectrum antibacterial therapies that exploit ecological interactions within microbial communities. Full article

Accurate representation of structural geometry, physical properties, and boundary conditions remains a major challenge in the finite element (FE) modeling of jacket structures. To address these difficulties, this study proposes a multi-source data synchronous updating framework for FE models based on the Genetic Aggregated Response Surface (GARS) and the Non-dominated Sorting Genetic Algorithm III (NSGA-III). First, vibration and strain tests were simultaneously conducted on an indoor jacket platform structure to obtain its natural frequencies and local dynamic strain responses. The measured data were processed to extract the first three natural frequencies and dynamic strain time histories at two critical locations, which served as reference data for model updating. An initial FE model of the jacket platform structure was then established, and sensitivity analysis was performed to identify the parameters requiring updating. Based on the simulation results, GARS was employed to construct response surface models describing the relationship between structural responses (natural frequencies and local strains) and the parameters to be updated, replacing FE analyses during optimization. Finally, NSGA-III was utilized to achieve synchronous updating of the FE model using multi-source data, and the updated geometric parameters were experimentally validated. The results demonstrate that errors in the first three natural frequencies of the FE model were reduced from 3.44%, −7.31%, and 5.88% to −0.02%, −0.43%, and 0.08%, respectively. Strain errors in the local region decreased from 12.96% and 10.33% to 1.4% and 2.1%. The corrected geometric parameters showed errors less than 1.85% when compared with actual measurements. These findings verify the accuracy and applicability of the proposed method for updating jacket platform FE models, providing an effective reference for model updating of in-service offshore structures. Full article

The hydrophobic region of semaglutide makes it prone to aggregation in aqueous solution, which leads to serious interception in microfiltration. The influences of pH and low concentrations of salts (NaCl, CH₃COONa, Na₂SO₄ and (NH₄)₂SO₄) on the particle size and zeta potential of semaglutide aggregates were studied in this work. The results showed pH could change the zeta potential on the semaglutide surface, but the impact on semaglutide dispersion was limited. When salts were introduced into aqueous solution, NaCl had a more significant dispersion effect on semaglutide than other salts. Under pH 2.5 or pH 8.0 conditions, the addition of 0.01 mol/L NaCl reduced the average particle size of semaglutide aggregates to below 70 nm. The permeability of semaglutide in microfiltration increased from 60% to 86% under optimized conditions with the PES membrane (0.22 μm), and the adsorption loss also reduced 40%. In addition, this study compared the HPLC detection precision of semaglutide samples prefiltered with different microfiltration filters. Some semaglutide was intercepted by various microfiltration filters, resulting in serious detection errors. When semaglutide was dissolved in the aqueous solution containing 0.01 mol/L NaCl with pH 2.5, the detection error was controlled within 1%. Full article

This review examines data-driven road traffic safety modeling, aiming to provide a comprehensive overview of the state-of-the-art and persistent research gaps. The study is structured around data sources, influencing factors, reactive and proactive modeling approaches, and key challenges. Data sources, including crashes, trajectories, traffic, roadway geometry, and environmental data, are first reviewed in the context of reactive and proactive safety analysis. To address the substantial heterogeneity across studies, a vote-counting strategy is adopted to aggregate directional evidence reported in the literature. The synthesis indicates that traffic demand variables exhibit consistently positive associations with crash occurrence, while speed-related effects are strongly context-dependent. Road geometry and surface conditions have largely consistent directional impacts on safety outcomes. From a methodological perspective, reactive approaches remain dominant, while proactive approaches exhibit potential for early risk identification but remain insufficiently validated due to data quality constraints. In addition, empirical evidence on conflict–crash relationships is still limited. Notably, model performance varies substantially across safety tasks, with algorithm effectiveness primarily driven by data structure, outcome definition, and aggregation level, rather than by the intrinsic superiority of any single approach. Overall, this review highlights challenges related to data integration, spatio-temporal modeling, interpretability, and transferability, and provides practical guidance for model selection in operational road safety analysis. Full article

Edge Intelligence (EI) empowers Mobile Crowdsensing (MCS) with real-time, distributed processing capabilities, but these advancements exacerbate long-standing privacy challenges. The strict requirements for low-latency computation on heterogeneous, resource-constrained edge nodes often conflict with the significant overhead imposed by traditional privacy-preserving techniques. Furthermore, distributed data flows and dynamic network conditions expand the attack surface, complicating risk containment. However, existing surveys do not examine privacy-preserving data aggregation through the lens of EI-specific constraints, a gap that this work aims to address. To this end, this paper systematically reviews recent privacy-preserving aggregation mechanisms from an EI-oriented perspective that accounts for real-time constraints, energy limitations, and decentralized cooperation. The survey examines emerging attack models and defense strategies associated with distributed collaboration and evaluates their implications for aggregation security in EI environments. Existing methods are categorized and assessed according to MCS system architecture and lifecycles, revealing limitations in applicability, scalability, and suitability under EI constraints. By integrating current techniques with experimental findings, this paper identifies open challenges and outlines promising directions for enhancing privacy protection in EI-driven MCS, offering both conceptual and analytical insights and practical guidance for future system design. Full article