嘉兴南湖学院

The rational design of polyurethanes (PUs) with tailored thermal properties is often impeded by the complex interplay between molecular architecture and microphase-separated morphology. While machine learning (ML) offers a promising method to accelerate materials discovery, existing models often lack mechanistic interpretability. Herein, we develop a robust predictive model for the glass transition temperature (T-g) by benchmarking a suite of ML algorithms against a carefully curated and expanded PU data set. Among the evaluated architectures, ensemble-based methods demonstrate better performance, with the optimized eXtreme Gradient Boosting (XGBoost) model achieving R-2 > 0.91 and RMSE approximate to 9.6 K, outperforming Lasso regression, Random Forest and Support-Vector Regression. Beyond accurate prediction, Shapley Additive Explanations (SHAP) methodology is employed to elucidate the mechanistic underpinnings of feature contributions in determining T-g. The SHAP analysis has been shown to quantitatively identify the density of quaternary carbon units within the hard segment (c HS), the hard segment weight fraction (HS wt %) and the molecular volume of the hard segment (HS MV) as the three dominant physicochemical descriptors governing T-g. Furthermore, all of the significant features that were examined can be categorized into three distinct groups, i.e., positive, negative, and dual effect groups, reflecting the competition between intramolecular chain stiffening and the disruption of intermolecular packing and the hydrogen-bond network. Overall, this study establishes concise data-driven design rules for modulating the thermal behavior of PUs, bridging the gap between statistical learning and fundamental polymer physics and enabling more informed accelerated PU formulation.

Designing high-temperature piezoelectric materials that combine an outstanding piezoelectric constant with increased electrical resistivity continues to be a major challenge. In this work, MnO2 was introduced into Bi2.98Ce0.02TiNb0.98Mo0.02O9 (BCTNM) ceramics to address this issue. The findings demonstrate that a proper level of MnO2 doping is effectively incorporated into the lattice, resulting in increased distortion of the (Ti,Nb)O6 octahedron and defect optimization. These microstructural modifications contribute to a notable improvement in the overall electrical properties. An optimized BCTNM-0.2Mn composition achieves a d33 value of 18 pC/N, an exceptional Curie point of 911 °C, and an electrical resistivity reaching 3.37 × 106 Ω·cm at 500 °C. Moreover, it indicates excellent thermal endurance, retaining over 90

Carbon dioxide (CO2) capture is a cornerstone of global carbon neutrality, yet the high energy penalty associated with solvent regeneration-particularly for monoethanolamine (MEA) systems-remains a major barrier to its sustainable deployment. This study presents a sustainable and high-performance catalytic solution using micro-sized iron oxyhydroxide (beta-FeOOH). Characterized by a high specific surface area ($287 m(2)/g) and a synergistic distribution of abundant Lewis and Br & oslash;nsted acid sites, the beta-FeOOH catalyst significantly enhances CO2 desorption kinetics. Experimental results demonstrate that the incorporation of beta-FeOOH into a 30 wt% MEA solution increases the CO2 desorption rate by 10.9% while simultaneously lowering the regeneration temperature from the conventional 120 degrees C to 85 degrees C. Such a reduction in thermal requirements offers a pathway to utilize low-grade industrial waste heat, drastically improving the process's energy efficiency. Furthermore, the catalyst exhibited remarkable cyclic stability over ten consecutive cycles, maintaining its structural integrity and catalytic activity. These findings highlight beta-FeOOH as an eco-friendly, cost-effective, and robust catalyst that aligns with the principles of green chemical engineering, offering a scalable strategy to enhance the sustainability of carbon capture operations.

In this study, PAAS/Au composite hydrogels with intrinsic flexibility, electrical conductivity, and rapid self-healing capability were fabricated via a synergistic strategy employing multiple non-covalent interactions (Au–S coordination, hydrogen bonding, and electrostatic interactions), using PAAS as the matrix and Au0-BACA as functional fillers. Characterizations including FTIR, XRD, and SEM confirmed the formation of a well-defined and stable three-dimensional porous network through these multiple non-covalent interactions. Rheological analysis revealed that the hydrogel maintains a stable gel state (G′ > G′′) under low strain conditions, while undergoing a gel-to-sol transition under high strain. Upon recovery from high-strain disruption, the storage modulus rapidly returns to its initial value with a recovery efficiency of 107

The cracking and peeling of thermal insulation boards used in external thermal insulation composite systems (ETICSs) under heating or rain exposure have a very adverse impact on the long-term safe service of ETICSs. When the thermal insulation board is subjected to rain exposure or heating, calculating its internal stress and deformation becomes a spatial problem. It is very difficult to obtain an exact solution that satisfies all the basic equations and boundary conditions. Cracking in ETICSs due to thermo-hygric cycling is a pervasive durability issue, yet a predictive model based on fracture mechanics is still lacking. This study aims to develop a generalized thermo-fracture framework to quantify crack initiation and propagation in brittle insulation boards. The model simplifies the board as orthotropic slats and incorporates the thermal stress field to calculate Mode I and II stress intensity factors (SIFs). As a case study, the framework is applied to expanded perlite insulation boards. The fracture toughness parameters (KIc, KIIc) of the boards are experimentally determined. Validation against experimental data shows that the model effectively predicts crack initiation, propagation direction, and crack-tip strain. It is found that rain-induced shrinkage dominates fracture via Mode I cracking, while heating-induced compression does not lead to Mode II failure under the specific boundary conditions. The proposed framework provides a theoretical foundation for the durability assessment and design of ETICSs comprising brittle insulation materials.