Interrogation of Ecotoxic Elements Distribution in Slag and Precipitated Calcite through a Machine Learning‐Based Approach Aided by Mass Spectrometry

Abstract

AbstractCO2 mineralization in slag has been widely investigated as a potential solution for offsetting steelmaking industry emissions. However, it can be associated with ecotoxic elements release (e.g., V and Cr). The presence of such elements in heterogenous slag at the micro‐scale remains difficult for analysis since microstructural features can be missed during microscopy data inspection, thereby presenting a challenge in understanding how ecotoxic elements exist in slag. Here, an unsupervised machine learning‐based technique is used to analyze slag's microstructural features. Energy Dispersive Spectroscopy (EDS) data are analyzed through Hierarchical Density‐Based Spatial Clustering of Applications with Noise (HDBSCAN) method. Results show that passive CO2 mineralization has occurred in situ in the studied samples, on the surface, and within their pores. Additionally, V and Cr regions with equivalent diameters < 42 µm can exist within slag, potentially making such elements prone to mobilization due to slag pulverization. Interrogation of the samples with Laser Ablation Inductively Coupled Plasma Mass Spectroscopy (LA‐ICP‐MS) confirms the distribution of the elements obtained from the clustering algorithm and further demonstrates that up to 84 and 9 ppm of V and Cr are incorporated in the precipitated calcite, respectively. This implies that ecotoxic elements may be immobilized through calcite precipitation.