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The surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non‐homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro‐ and micro‐scale awareness sub‐modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro‐ and micro‐texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R2= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non‐contact alternative method for skid resistance analysis. [ABSTRACT FROM AUTHOR]

In the present study, the effect of elastomer ratios in solution-based mixtures on the formation of the phase structure and properties of compositions based on butyl rubber, chlorobutyl rubber, and ethylene-propylene-diene rubber was investigated. This work determined the influence of the elastomer ratios in uncured blends and their impact on the patterns of phase structure formation in multicomponent polymer systems, alterations in molecular mobility, and their influence on ozone resistance. Attenuated total reflectance – Fourier transform infrared spectroscopy was employed to analyze the chemical structure of the polymer surfaces. The influence of blend ratio (mass percentage ratios of 100/0, 80/20, 60/40, 50/50, 40/60, 20/80, and 0/100) on the phase structure and surface ozone oxidation was studied. The green strengths of the blends of ethylene-propylene-diene rubber and butyl/chlorobutyl rubbers were studied over a wide range of blend ratios. [ABSTRACT FROM AUTHOR]

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Interpenetrating polymer networks (IPNs) possessing a wide damping temperature range are commonly employed as damping materials that are applied in numerous fields. But IPNs will gradually appear to fatigue microcracks and fracture when long‐term in the energy absorption and dissipation. So, the ability for IPNs to recover from damage is necessary that can considerably prolong their service life. Based on polyurethane elastomer and cross‐linked epoxy, several innovative self‐healing IPNs are designed and prepared in this research. The IPNs containing dynamic disulfide bond exhibit favorable damping and self‐healing properties. In detail, the loss factor of IPN‐80/20 is beyond 0.3 at temperatures between −0.8 and 65.5 °C that covers the operating temperature range at room temperature. What is more, The PU/EP IPNs also have excellent shape memory properties, self‐healing ability, and thermal stability. [ABSTRACT FROM AUTHOR]

This research delved into the influence of mesoporous silica's surface charge density on the adsorption of Cu 2+ . The synthesis of mesoporous silica employed the hydrothermal method, with pore size controlled by varying the length of trimethylammonium bromide (C n TAB, n = 12, 14, 16) chains. Gas adsorption techniques and transmission electron microscopy characterized the mesoporous silica structure. Surface charge densities of the mesoporous silica were determined through potentiometric titration, while surface hydroxyl densities were assessed using the thermogravimetric method. Subsequently, batch adsorption experiments were conducted to study the adsorption of Cu 2+ in mesoporous silica, and the process was comprehensively analyzed using Atomic absorption spectrometry (AAS), Fourier transform infrared (FTIR), and L3 edge X-ray absorption near edge structure (XANES). The research findings suggest a positive correlation between the pore size of mesoporous silica, its surface charge density, and the adsorption capacity for Cu 2+ . More specifically, as the pore size increases within the 3-4.1 nm range, the surface charge density and the adsorption capacity for Cu 2+ also increase. Our findings provide valuable insights into the relationship between the physicochemical properties of mesoporous silica and the adsorption behavior of Cu 2+ , offering potential applications in areas such as environmental remediation and catalysis., (© 2024. The Author(s).)