Interrogation of ecotoxic elements distribution in slag and precipitated calcite through a machine learning-based approach aided by mass spectrometry

Khudhur, F. W.K. , Divers, M., Wildman, M. , MacDonald, J. and Einsle, J. F. (2024) Interrogation of ecotoxic elements distribution in slag and precipitated calcite through a machine learning-based approach aided by mass spectrometry. Advanced Sustainable Systems, (doi: 10.1002/adsu.202300559) (Early Online Publication)

Abstract

CO2 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.

• Item Type: Articles
• Additional Information: FWKK acknowledges generous support from the University of Glasgow Lord 527 Kelvin/Adam Smith PhD scholarship.
• Keywords: CO2 capture and sequestration, EDS, HDBSCAN, ICP-MS, laser ablation, machine learning.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wildman, Dr Mark and MacDonald, Dr John and Divers, Matt and Einsle, Dr Joshua Franz and Khudhur, Faisal

Creator Roles

Khudhur, F.: Conceptualization, Data curation, Methodology, Writing – original draft
Divers, M.: Formal analysis, Methodology, Writing – review and editing
Wildman, M.: Data curation, Formal analysis
MacDonald, J.: Supervision, Funding acquisition, Methodology, Writing – review and editing
Einsle, J. F.: Supervision, Formal analysis, Methodology, Writing – review and editing

• Authors: Khudhur, F. W.K., Divers, M., Wildman, M., MacDonald, J., and Einsle, J. F.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Advanced Sustainable Systems
• Publisher: Wiley
• ISSN: 2366-7486
• ISSN (Online): 2366-7486
• Published Online: 01 March 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in Advanced Sustainable Systems 2024
• Publisher Policy: Reproduced under a Creative Commons licence