Influence of residual iron content in steel slag on the long-term properties of carbonated steel slag blocks
Source abstract
Abstract
Accelerated carbonation effectively mitigates the unsoundness issue of steel slag caused by free CaO/MgO. However, the long-term impact of residual uncarbonated iron-rich phases, which may oxidize and lead to expansion and degradation during long-term curing, remains unclear. This study investigated the long-term behavior of carbonated steel slag blocks with varying residual iron content in the steel slag obtained by magnetic separation. The carbon sequestration efficiency, mechanical properties, volume stability, and microstructure were thoroughly evaluated. Results revealed that carbonated steel slag blocks with a higher residual Fe2O3 content (38.47%) achieved only 46% of the compressive strength and 37% of the CO2 uptake compared to those with lower residual iron content (25.88%). Through 180 days of accelerated degradation and natural curing tests, it was observed that the compressive strength of blocks exhibited an overall increase of 20%-150%, especially in blocks with high iron content, due to the hydration of uncarbonated steel slag. Despite the formation of rust in samples with elevated residual iron content, as verified through XRD and optical microscopy, no substantial negative impacts were observed at later stages. The excessive residual iron containing phase contributed to the formation porous microstructure and enhance the capacity of accommodating corrosion products. This study highlights that reducing residual iron content through magnetic separation not only enhances the mechanical properties and stability of carbonated steel slag blocks, but also offers a valuable recycling opportunity for steel and iron industries.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Magnesium Oxide Properties and Applications · CO2 Sequestration and Geologic Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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