Tailoring mechanical properties of alkali activated slag-electric furnace nickel slag composite system through Ca/Si and Si/Al ratios
Source abstract
Abstract
This study develops an alkali-activated slag-electric furnace nickel slag (AAS-ENS) composite system by incorporating electric furnace nickel slag (ENS) and granulated blast furnace slag (GBFS) to explore a sustainable and high-value utilization pathway for ENS. The Ca/Si and Si/Al ratios of the precursor materials were adjusted by controlling the GBFS dosage, and their effects on the compressive strength, flexural strength, and microstructure of AAS-ENS composites were systematically investigated. The results indicate that compressive strength exhibits a quadratic relationship with the Ca/Si ratio, with an optimal range of 0.35–0.45. Additionally, the Si/Al ratio shows a negative correlation with compressive strength, with the optimal Si/Al ratio estimated to be below 4. Microstructural analysis, including heat of hydration, X-ray diffraction (XRD), thermogravimetric analysis (TG-DTG), mercury intrusion porosimetry (MIP), and scanning electron microscopy (SEM), revealed that properly balanced Ca/Si and Si/Al ratios promote the formation of C-(A)-S-H gel, leading to higher material density and improved mechanical performance. These findings provide a theoretical foundation for optimizing the mechanical properties and microstructure of AAS-ENS composites and offer practical insights into the sustainable utilization of ENS in eco-friendly construction materials. • Synergistic effects of Ca/Si and Si/Al ratios enhance AAS-ENS mechanical properties. • Optimal Ca/Si ratio improve gel polymerization, reduce porosity, and densify structure. • Facilitates the efficient utilization of ENS in alkali-activated cementitious materials.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Magnesium Oxide Properties and Applications · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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