Using quantitative electron microscopy for process mineralogy applications

Gottlieb, Paul; Wilkie, Gregory John; Sutherland, D.N.; Ho-Tun, E.; Suthers, S. P.; Perera, K. S.; Jenkins, Benjamin M.; Spencer, Steven J.; Butcher, Alan R.; Rayner, J. C. W.

doi:10.1007/s11837-000-0126-9

Cited by 309 publications

(157 citation statements)

References 2 publications

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“…This still faces major limitations but can be addressed using, or combining, energy dispersive X-ray spectra (Gottlieb et al, 2000), backscattered electron intensities (Gu, 2003) or specular reflectance spectra (Pirard, 2004). The resulting mineral mappings typically serve as an input to the first principle of stereology to straightforwardly derive the volumetric ratios of minerals from planar ratios.…”

Section: Image Analysis Of Particulate Ore Materialsmentioning

confidence: 99%

“…This would require a system able to locate any pixel on a high resolution 3D grid and give it both a chemical and a crystallographical attribute. Most research teams involved in modal analysis or liberation analysis of ores have adopted the Scanning Electron Microscope as their privileged imaging device (Gottlieb et al, 2000;Petruk, 1998). This allows for high resolution images to be collected either as single-valued back-scattered electron (BSE) images or as a multiple set of elementary mappings derived from the energy dispersive X-Ray spectrum of each pixel (EDX).…”

Section: Mineralogical Imagingmentioning

confidence: 99%

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Particle texture analysis using polarized light imaging and grey level intercepts

Pirard

Lebichot

Krier

2007

International Journal of Mineral Processing

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The usual characterization of particulate ore material with image analysis includes modal analysis and liberation analysis. Both methods include stereological corrections based on intercept length distributions within each phase of interest. The first principle of stereology relies on a simple assumption of uniform random sectioning, whereas the liberation models require all particles to have similar textural properties. However, for the sake of adequate prediction of ore behavior in industrial processes it is becoming more and more important to be able to describe particle populations in terms of texture classes.Among the texture analysis techniques, this paper focuses on mean intercept length (MIL). It brings two important improvements: the first one is to improve grain boundary imaging by using images of bireflectant minerals under plane polarized illumination, the second one is to consider intercept analysis on grey level images instead of binary ones. The method is illustrated on a selection of critical hematite textures found within banded iron formations.

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Section: Image Analysis Of Particulate Ore Materialsmentioning

confidence: 99%

Section: Mineralogical Imagingmentioning

confidence: 99%

Particle texture analysis using polarized light imaging and grey level intercepts

Pirard

Lebichot

Krier

2007

International Journal of Mineral Processing

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“…Baseada fundamentalmente em geometria e estatística, essa técnica utiliza amostragem aleatória e sistemática para fornecer dados sem viés (HENLEY, 1983b). Assim, a qualidade dos resultados obtidos é dependente dos procedimentos de amostragem e de preparação (GOTTLIEB et al, 2000).…”

Section: Análise De Imagens E Estereologiaunclassified

“…Quanto mais fina for a textura, no caso, granulometria fino e distribuída, menor será esse tipo de erro (GOTTLIEB et al, 2000;SPENCER;SUTHERLAND, 2000).…”

Section: Figura 8 -Erros Estereológicos Decorrente Da Natureza Das Paunclassified

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Caracterização tecnológica de minérios auríferos por análise automatizada de imagens obtidas por microscopia eletrônica de varredura.

Nery¹

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

Khudhur,

Divers,

Wildman

et al. 2024

Advanced Sustainable Systems

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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.

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Using quantitative electron microscopy for process mineralogy applications

Abstract: Figure 1. A schematic of the basis of stereological error, with sections (gray lines) through liberated and composite particles of varying texture and the corresponding magnitude of bias.

Cited by 309 publications

References 2 publications

Particle texture analysis using polarized light imaging and grey level intercepts

Particle texture analysis using polarized light imaging and grey level intercepts

Caracterização tecnológica de minérios auríferos por análise automatizada de imagens obtidas por microscopia eletrônica de varredura.

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

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