Zhen Yao

Oxygen inhibition is conventionally regarded as undesirable in UV photopolymerization because it induces incomplete curing and is commonly minimized during fabrication. Herein, oxygen-inhibition-induced partial curing was exploited as a structural engineering tool for transparent polymer coatings. The partially cured soft layer enables controlled penetration of a nanoparticle-reinforced hard-layer precursor, forming a gradient layer with continuously varying composition across the thickness. This penetration creates a gradient interface, defined as a spatially continuous transition from the rigid hard layer to the compliant soft layer, unlike conventional bilayer coatings. The gradual transition reduces the mechanical mismatch promoting continuous stress transfer and dissipation during deformation. Therefore, the gradient architecture combines mechanical robustness, bending capability, wear resistance, and optical transparency. The optimized coating (15-LED-60) exhibits a hardness of 811 MPa, inward bending radius below 1 mm, outward bending radius of 5 mm, transmittance of 91.7%, and withstands over 4500 abrasion cycles at 103 kPa. the penetration depth and interfacial gradient can be regulated by SiO₂ content and ethanol-mediated penetration, enabling control over interfacial structure and mechanical response. This work demonstrates that oxygen inhibition can serve as a controllable tool for interfacial gradient engineering, offer an interface design strategy for mechanically robust transparent polymer coatings.

Polypropylene alloys exhibit a complex multiscale structure, posing challenges for traditional analytical methods in uncovering the relationships between structural and mechanical properties. To address this, a novel machine learning framework that integrates graphical modeling with regression techniques is proposed to enhance interpretability and predictive accuracy. Specifically, a Bayesian network is constructed to elucidate the dependencies among structural variables, guiding feature selection in the regression stage, and a hierarchical XGBoost regression model based on this network (BNXGB) is developed to predict impact strength (IS) and flexural modulus (FM). Compared to a conventional XGBoost model (XGB), BNXGB demonstrates superior performance, achieving higher R ² score for both IS and FM. More importantly, BNXGB handles missing input features by leveraging the network to impute them from parent nodes, thereby maintaining robust performance under incomplete data. Even under the extreme condition when all features except the four root nodes are missing, BNXGB still preserves reasonable R ² scores for both IS and FM, whereas XGB's performance drops sharply. These results demonstrate the robustness and practical applicability of the BNXGB framework for polymer property prediction under missing data, potentially reducing the need for exhaustive material characterization.

Emulsified asphalt is extensively utilized in a variety of applications. During the preparation process, the emulsifier diffuses from the aqueous phase and adsorbs to the surface of asphalt particles, forming an adsorption film that protects the asphalt particles and stabilizes the emulsion. In this study, a quartz crystal microbalance with dissipation monitoring and molecular dynamics simulations were employed to investigate the adsorption behavior of modified styrene-maleic anhydride emulsifiers on the asphalt surface. The effects of the emulsifier molecular structure on diffusion kinetics and film mechanical properties were examined. The study revealed that the diffusion kinetics of the emulsifier are influenced by molecular weight and hydrophobicity. In addition, emulsifier groups and their spatial arrangement significantly influence adsorption conformation, thereby affecting film rigidity. Based on the findings, a quantitative model was developed to characterize the rigidity of the adsorption film. Furthermore, an adsorption efficiency index was proposed, defined by the reduction in interaction energy between the emulsifier and asphalt before and after the emulsifier transfers from the asphalt interior to the asphalt-water interface. This index enables evaluation of the structural rationality of emulsifier molecules and provides guidance for the rational design of emulsifiers in asphalt emulsions.

Hard and bendable polymer films are critically important in a wide range of applications. It is generally accepted that for polymers, increased cross-linking density leads to enhanced hardness, but often at the expense of bendability. Here, by selecting different UV monomers, thermal post-treatment, and addition of LiCl, a series of polymer films with varied cross-linking densities and bonding architectures were prepared through rapid UV curing. Comparative analysis reveals that, unlike other cross-linking, the introduction of hydrogen bonding effectively enhances surface hardness without deteriorating bending properties in this system. Compared to trimethylolpropane triacrylate, pentaerythritol triacrylate has a hydroxyl group to form hydrogen bonds, exhibiting higher hardness but no decrease in bendability. Upon thermal treatment of 4-acryloylmorpholine, the hardness variation correlates with hydrogen bonding, while the bending radius remains unchanged. The introduction of LiCl leads to the formation of coordination bonds in the polymer, resulting in increased hardness but a reduced bending performance. Contrary to the general understanding, the hardness and bending properties of the polymers were decoupled by introducing hydrogen bonding. This work provides a theoretical basis for the development of advanced functional polymeric materials with an optimized mechanical performance.

Silicone rubber foam is recognized for its heat resistance and lightweight properties, making it suitable for a variety of applications. However, the relationship between the formulation, crosslinking network structure, rheological properties and foaming behavior is not thoroughly understood. In this work, the contents of crosslinked chains, dangling chains and free chains were characterized by low-field nuclear magnetic resonance (LF-NMR), which revealed the transformation of dangling chains into crosslinked chains with increasing the content of crosslinking agent. Additionally, utilizing chain segment information and rheological results, it was found that the densely crosslinking network structure of silicone rubber results in a storage modulus significantly exceeding its loss modulus. This leads to a smaller cell diameter in silicone rubber foam compared to thermoplastic polymers such as PP and PET, consequently resulting in a lower expansion ratio for silicone rubber. This study also investigated the mechanical properties of samples with different cell morphologies. The findings provide insights into the differences in foaming behavior between silicone rubber and thermoplastic polymers.

Illustrations of different machine learning methods and parameter evaluation.

Polypropylene in-reactor alloys present a complex structure influenced by diverse polymerization process parameters, posing challenges for traditional analysis methods in establishing a quantitative relationship between process conditions, alloy structures and mechanical properties. To address this issue, a series of polypropylene/poly(ethylene-co-propylene) alloys with varied structures were synthesized by gas-phase polymerization. Machine learning methods were employed to develop regression models for predicting flexural strength (FS), impact strength (IS) and rubber phase content. The importance of structure and process condition descriptors was further analysed to reveal the process–structure–property relationship. The FS and IS prediction models utilizing Extreme Gradient Boosting (XGB) algorithms achieved impressive R² scores of 0.9846 and 0.9841, respectively. Notably, the significant contribution of the rubber phase content to FS and IS prediction was observed in the structure descriptors. Furthermore, process condition descriptors (flowrate and initial pressure) played crucial roles in rubber synthesis, thereby exerting a substantial impact on FS and IS. In light of the feature importance analysis, new experimental runs were designed to synthesize alloys with enhanced IS. The experimental results closely aligned with the model predictions (RMSE = 4.4751 for IS). This research provides a new approach to establish process–structure–property relationships for in-reactor alloys, providing a convenient method for designing experiments to attain desired material properties.