Low-Carbon Alkali-Activated Gangue-Based Solid Waste Filling Materials: Geopolymerization–Mineralization Mechanism and Mechanical Property Regulation

In this study, gangue-based solid waste was utilized as the primary raw material to prepare filling materials using a composite alkali activator comprising calcium oxide (CaO) and sodium metasilicate (Na2SiO3). By varying the proportions and total content of the alkali activators, with the total content fixed at 12 wt% of coal gangue, the resulting filling materials were systematically investigated. The mineralogical composition, morphology, and hydration degree of the materials were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TG). In addition, the compressive strength of the materials was measured. The results demonstrated that both the type and dosage of alkali activators significantly influenced the mineral phases and surface morphology of the filling materials. CaO and Na2SiO3 exhibited distinct effects on the degree of hydration, and the curing age was also found to be a critical influencing factor. For single-component activators, the compressive strength of the filling materials initially increased and then decreased with increasing activator content, with optimal values observed at CaO contents between 6% and 9% and Na2SiO3 content around 2%. In the case of the composite CaO–Na2SiO3 system, the uniaxial compressive strength exhibited a similar trend, increasing first and then decreasing with the CaO-to-Na2SiO3 ratio, with the optimal ratio determined to be 3:1. Furthermore, a positive correlation between curing age and compressive strength was observed. This study elucidates the synergistic mechanism of CaO and Na2SiO3, identifies optimal mix proportions, and quantifies empirical relationships between raw material properties, reaction conditions (activator ratio/content, curing age), and compressive strength. These relationships serve as core data for subsequent construction of a “raw material–reaction condition–strength” correlation model, providing support for formulation optimization of gangue-based filling materials.