Constructing and maintaining a three-dimensional network structure with high porosity is critical to the preparation of silica aerogel materials because this structure provides excellent properties. However, due to the pearl-necklace-like structure and narrow interparticle necks, aerogels have poor mechanical strength and a brittle nature. Developing and designing lightweight silica aerogels with distinct mechanical properties is significant to extend their practical applications. In this work, thermally induced phase separation (TIPS) of poly(methyl methacrylate) (PMMA) from a mixture of ethanol and water was used to strengthen the skeletal network of aerogels. Strong and lightweight PMMA-modified silica aerogels were synthesized via the TIPS method and supercritically dried with carbon dioxide. The cloud point temperature of PMMA solutions, physical characteristics, morphological properties, microstructure, thermal conductivities, and mechanical properties were investigated. The resultant composited aerogels not only exhibit a homogenous mesoporous structure but also achieve a significant improvement in mechanical properties. The addition of PMMA increased the flexural strength and compressive strength by as much as 120% and 1400%, respectively, with the greatest amount of PMMA (M_w = 35,000 g/mole), while the density just increased by 28%. Overall, this research suggests that the TIPS method has great efficiency in reinforcing silica aerogels with less sacrifice of low density and large porosity.

With the rapid pace of industrialization and the increasing intensity of human activities, the global climate change and energy crisis have reached a heightened level of severity. Consequently, achieving an early peak in carbon emissions has become an imperative in addressing this pressing issue. Particularly, coastal provinces, known for their developed economies, high population density, and substantial building energy consumption, have emerged as significant contributors to carbon emissions. Notably, public buildings, serving as critical constituents of the construction industry, possess immense potential for both energy conservation and emissions reduction. In light of this, the present study focuses on Fujian Province, situated along the coast, and constructs a carbon emission estimation model for public buildings based on the Kaya identity. This model takes into account various factors specific to Fujian Province, including population characteristics, economic conditions, tertiary industry development, public building area, and energy consumption. Through scenario analysis, the study projects that the year of peak carbon emissions for public buildings in Fujian Province is estimated to be 2030, 2035, and 2040 under low-carbon, baseline, and high-carbon scenarios respectively. The corresponding peak carbon emission levels are anticipated to reach 23.62 million t, 24.18 million t, and 24.76 million t CO₂. Lastly, based on local policies and actual conditions, the study proposes a set of policy measures and feasible approaches tailored to Fujian Province, aiming to achieve an early peak in carbon emissions.

Accurate quantification of the apparent chloride diffusion coefficient is pivotal for predicting the service life and assessing the durability of coastal infrastructure. While existing empirical models are informative, they fundamentally overlook the influence of reinforcement. This study establishes an integrated computational framework combining XGBoost machine learning with finite element (FE) analysis to elucidate chloride transport mechanisms in reinforced concrete (RC), explicitly accounting for the presence of reinforcement. Based on 171 experimental datasets, this study developed a prediction model to estimate the apparent chloride diffusion coefficient in reinforced concrete subjected to dry–wet cycles. The diameter of rebar was innovatively incorporated as a parameter, systematically integrating seven other key influencing factors into the model. Shapley Additive Explanations (SHAP) analysis reveals that exposure duration, sampling depth, coarse-to-total aggregate ratio, and rebar diameter constitute the dominant influencing parameters. Furthermore, FE analysis reveals that the presence of rebar redistributes aggregates, forming preferential pathways that increase chloride concentration at the steel-concrete interface. This study shows that the influence of reinforcement on chloride diffusion cannot be ignored. The proposed methodology advances durability science by data-driven modeling with physics-based modeling, providing actionable strategies for marine infrastructure optimization.