Graphene oxide (GO), a pivotal derivative of graphene, has revolutionized nanotechnology with its tunable physicochemical properties and interdisciplinary applications in energy storage, environmental remediation, and biomedicine. Despite its exponential research growth, existing reviews remain fragmented, lacking holistic insights into evolving synthesis–application linkages, global collaboration patterns, and emerging convergence trends. This study bridges these gaps through a scientometric analysis of 14,124 peer-reviewed articles (2022–2025) from the Web of Science Core Collection, utilizing CiteSpace for co-occurrence network mapping, burst detection, and cluster analysis. Key findings reveal (1) a thematic shift from traditional synthesis optimization (e.g., Hummers’ method) toward driven material design and sustainable applications like GO membranes for water purification; (2) China’s dominance in publication output (38.5%) contrasts with the U.S. and Europe’s leadership in global collaborations; (3) interdisciplinary journals such as Chemical Engineering Journal (centrality: 0.25) and emerging clusters like “circular economy” signal transformative priorities; and (4) critical gaps in scalability, ecological safety, and cost-effective production hinder industrial translation. This work provides a roadmap for aligning research with sustainability goals, fostering global partnerships, and accelerating innovations in scalable nanotechnology.

Cement-based materials are essential in modern construction, valued for their versatility and performance. Rheological properties, including yield stress, plastic viscosity, and thixotropy, play indispensable roles in optimizing the workability, stability, and overall performance of cement composites. This review explores the effects of supplementary cementitious materials (SCMs), chemical admixtures, nanomaterials, and internal curing agents on modulating rheological properties. Specifically, SCMs, including fly ash (FA), ground granulated blast furnace slag (GGBFS), and silica fume (SF), generally improve the rheology of concrete while reducing the cement content and CO₂ emissions. Regarding chemical admixtures, like superplasticizers (SPs), viscosity-modifying agents (VMAs), setting-time control agents, and superabsorbent polymers (SAPs), they further optimize flow and cohesion, addressing issues such as segregation and early-age shrinkage. Nanomaterials, including nano-silica (NS) and graphene oxide (GO), can enhance viscosity and mechanical properties at the microstructural level. By integrating these materials above, it can tailor concrete for specific applications, thereby improving both performance and sustainability. This review presents a comprehensive synthesis of recent literature, utilizing both qualitative and quantitative methods to assess the impacts of various additives on the rheological properties of cement-based materials. It underscores the pivotal roles of rheological properties in optimizing the workability, stability, and overall performance of cement composites. The review further explores the influences of SCMs, chemical admixtures, nanomaterials, and internal curing agents on rheological modulation. Through the strategic integration of these materials, it is possible to enhance both the performance and sustainability of cement composites, ultimately reducing carbon emissions and advancing the development of eco-friendly construction materials.

Field monitoring of methane emissions from landfills is of great importance for both environmental concern and economic benefit. This study presents a highly effective method to measure methane emissions from landfills based on tunable diode laser absorption spectroscopy (TDLAS). Methane concentration is obtained by analyzing the absorption spectrum of the laser after passing through the landfill gas. The relationship between methane concentration and the optical signal was calibrated in the laboratory. As the methane concentration increased from 400 ppm to 5000 ppm, the absorption spectrum amplitude increased linearly from 0.0005 to 0.0046. In situ testing of methane emissions at a large-scale landfill in China demonstrated the accuracy of the TDLAS method. The methane concentrations in the well-covered areas were generally below 100 ppm. In the working area or the coverage area with holes, the methane concentration was about 700 ppm. The methane concentration was up to 1900 ppm, where the gas collection pipe is disconnected. Due to to the accuracy and simplicity, the TDLAS method is suitable to detect methane emissions on a large-scale from landfills.

Capacity expansion of existing landfills is one of the most economical methods of relieving the stress caused by the disposal of local municipal solid waste (MSW) in developing countries. In this paper, the compression characteristics and settlement of the aged MSW in Xiawan landfill were studied. The physical composition, moisture content, volumetric weight, and modified primary compression index of the aged MSW at different depths were tested, and then the settlement of the MSW in the area to be filled with fly ash was calculated. The test results showed that the main components of the aged MSW were muck and textile, indicating that kitchen waste was degraded. The moisture content of the MSW generally increased with depth, and the average moisture content within 5 m of the shallow part of the landfill was 97%. The void ratio of the aged MSW decreased from 4.0 to 2.2 with an increase in vertical load from 1 kPa to 400 kPa. The modified primary compression index of the aged MSW ranged from 0.22 to 0.27, and generally decreased with depth. Under the load generated by the subsequent filling of fly ash, the final strains of the aged MSW was approximately 15% to 20%. The physical composition and compression characteristics of the aged MSW measured in this paper were compared with those reported in previous studies to provide reference for the settlement analysis of landfills with aged MSW.

Accurate prediction of the mechanical and rheological properties of self-compacting concrete (SCC) is critical for mixture design and engineering decision-making; however, conventional empirical approaches often struggle to capture the coupled nonlinear relationships among mixture variables. To address this challenge, this study develops an integrated and interpretable hybrid machine learning (ML) framework by coupling three ML models (RF, XGBoost, and SVR) with five metaheuristic optimizers (SSA, PSO, GWO, GA, and WOA), and by incorporating SHAP and partial dependence (PDP) analyses for explainability. Two SCC datasets with nine mixture parameters are used to predict 28-day compressive strength (CS) and slump flow (SF). The results show that SSA provides the most stable hyperparameter optimization, and the best-performing SSA–RF model achieves test R² values of 0.967 for CS and 0.958 for SF, with RMSE values of 2.295 and 23.068, respectively. Feature importance analysis indicates that the top five variables contribute more than 80% of the predictive information for both targets. Using only these dominant features, a simplified SSA–RF model reduces computation time from 7.3 s to 5.9 s and from 9.7 s to 6.1 s for the two datasets, respectively, while maintaining engineering-level prediction accuracy, and the SHAP and PDP analyses provide transparent feature-level explanations and verify that the learned relationships are physically consistent with SCC mixture-design principles, thereby increasing the reliability and practical applicability of the proposed framework. Overall, the proposed framework delivers accurate prediction, transparent interpretation, and practical guidance for SCC mixture optimization.