To address the disposal dilemmas of construction engineering spoil and the high carbon footprint of conventional stabilizers, this study successfully developed a clinker-free, eco-friendly fluidized solidified soil (FSS) using a loess matrix, calcium carbide slag (CCS) as the alkaline activator, and a synergistic blend of blast furnace slag (BFS) and calcined coal gangue powder (CCGP) as precursors. Utilizing the simplex centroid design method, the evolution laws of fresh paste fluidity and shear rheological features, alongside the unconfined compressive strength (UCS) and water stability coefficient (Kw) of hardened matrices, were systematically characterized. Fuzzy optimization theory was applied for multi-objective quantitative trade-offs, and microstructural binding mechanisms were comprehensively elucidated. The results indicate that the fresh paste complies with the Bingham fluid model, displaying a negative correlation between rheological parameters and fluidity. The mechanical strength is co-regulated by the activator dosage and precursor configurations, presenting an inverted "U"-shaped pattern with higher CCS content. Trade-offs via fuzzy optimization reveal that the mix proportion consisting of 20% CCS and an 8:2 BFS/CCGP ratio achieves the highest relative superiority of 0.81, optimizing engineering performance (fluidity: 230mm; 28-day UCS: 3.63MPa; Kw: 98.78%). Microstructural analyses verify that CCS-induced alkalinity prompts the "depolymerization-reconfiguration" of active aluminosilicate networks into high-polymerization C-(A)-S-H gels that encapsulate loose soil particles. Environmental and economic accounting confirms that the heavy metal leaching risk of the specimens is extremely low, while the raw material costs and carbon emissions of Scheme B4 binder can be reduced by 37.6% and 96.9%, respectively, compared with ordinary Portland cement under the equivalent binder dosage.
Iron-based amendments are widely used to stabilize As- and Sb-contaminated soils, but their performance varies across soil systems. A machine-learning-assisted framework was developed to predict stabilization efficiency and support preliminary remediation design using a literature-derived database covering soil properties, metalloid concentrations, amendment characteristics, and operational conditions. Among six algorithms (SVR, RF, MLP, XGBoost, LightGBM, and CatBoost), the CatBoost pipeline provided the best balance between predictive accuracy and stability for both As and Sb. The CatBoost-As model retained moderate predictive performance under Group_ID-aware validation, whereas the Sb model showed a greater performance decline when entire experimental series were withheld, indicating higher sensitivity to between-study heterogeneity and limiting its current use to preliminary screening rather than direct remediation-parameter determination. The applicability domain was delineated using SHAP-weighted Euclidean distance; out-of-domain predictions retained some external predictive value but required experimental confirmation before practical use. SHAP analysis identified soil physicochemical properties as the dominant predictor category, contributing 31.1% and 36.3% of total SHAP attribution in the As and Sb models, respectively. Higher Fe content and longer treatment time were associated with higher predicted As stabilization, whereas Sb responses were more condition-dependent. A graphical user interface was developed for pre-experimental prediction and remediation recommendation. Independent validation yielded R² values of 0.620 and 0.687 for As and Sb, respectively. A three-objective NSGA-II procedure coupled with TOPSIS identified compromise solutions balancing predicted As and Sb stabilization efficiencies against amendment dosage. An external experimental validation case using a previously developed nano-ZVI/Fe₃O₄ composite further showed that the material achieved >90% concurrent stabilization of As and Sb in black, loess, and alluvial soils after 28 days. The framework supported model-informed preliminary assessment, candidate prioritization, and dose-aware optimization within the represented soil–amendment domain.
The uncontrolled disposal of contaminant-enriched biomass following wastewater phytoremediation increases the risk of secondary pollution (e.g., damage to terrestrial environment). This study evaluates the sustainable valorization of post-phytoremediation Pistia stratiotes biomass (PPB) through three scenarios: direct anaerobic digestion (AD), direct pyrolysis, and an integrated alkaline pretreatment (APT/AD) with digestate pyrolysis. These three strategies are compared to the “business-as-usual” practice of PPB landfilling. While experimental results showed that 3% NaOH pretreatment increased methane yields by 2.96-fold, life cycle assessment (LCA) revealed a trade-off between high energy recovery and the environmental risks of chemical consumption. Alternatively, the “direct pyrolysis” scenario depicted a more environmentally benign approach, attributing to the reduced reliance on synthetic chemicals and improved biochar recovery. A multi-criteria scoring system using TOPSIS was employed to determine an optimal valorization pathway and provide a clearer framework for decision-makers in the waste management sector. The scoring system identifies the integrated APT/AD/pyrolysis scenario as the most viable pathway, benefiting from the shortest payback period (4.23 years) and a higher internal rate of return (25.89%). By weighing energy independence and carbon credits against operational expenditures, this study demonstrates that the scoring approach is an essential tool for implementing circular economy models in resource-constrained environments.
Micellar-enhanced filtration achieves high ion removal but often suffers from high operating pressures and low permeability. Moreover, the synergistic role between membrane surface functionality and micelle–metal stabilization remains largely unexplored. Hence, a dual-functional chitosan–mica-embedded polysulfone membrane was developed for micellar-enhanced microfiltration to improve permeability and cadmium (Cd) removal under low-pressure conditions. Membrane performance was performed under optimized parameters, including surfactant type and concentration, Cd concentration, pH, temperature, mixing time, and mica loading. Chitosan provided abundant amino and hydroxyl groups for Cd chelation, while mica introduced negatively charged surfaces that stabilized surfactant–metal micelles. Among three anionic surfactants, SLESP showed the highest Cd removal due to its polyoxyethylene ether chains, which enhance hydrophilicity and stronger electrostatic interactions with Cd ions, thereby forming more stable micelle-metal complexes. Under prolonged filtration with SLESP, flux decreased by only 1.7% and Cd removal by 10% after four cycles, with an average flux of 306.2 L m−2 h−1 bar−1 and 88.9% Cd removal. The system integrates adsorption, electrostatic attraction, micellar encapsulation, and size-exclusion mechanisms to improve Cd retention while mitigating fouling. These results demonstrate that the synergistic membrane functionality and micellar stabilization enable energy-efficient, high-permeability, and superior Cd removal for scalable heavy-metal remediation.
