Introduction
Iron ore characterization is a critical step in mineral processing, enabling engineers and geologists to assess the quality of ore bodies, optimize beneficiation strategies, and evaluate the degree of mineral liberation before downstream processing. The Semplor NANOS tabletop SEM, equipped with a 4-quadrant backscattered electron detector (BSD) and integrated Energy-Dispersive X-ray Spectroscopy (EDS), delivers the imaging and compositional analysis needed to characterize iron ore samples.
Backscattered Electron Imaging for Phase Contrast Visualization
Backscattered electron (BSD) imaging is the primary mode for iron ore analysis, as it generates compositional contrast based on the mean atomic number of phases present. Fe-rich minerals, such as iron oxides, appear bright due to their high atomic number, while silicate gangue phases appear darker, making it straightforward to visually distinguish between the two populations across the sample surface.
Three BSD images were acquired at different locations across the polished sample surface to evaluate spatial variation in particle distribution and phase contrast.
Figure 1: BSD image from Location 1 showing bright probable Fe-rich particles embedded in a darker probable SiO₂-rich matrix.
Figure 2: BSD image from Location 2 showing coarse probable Fe-rich particles with internal contrast variation, suggesting compositional heterogeneity within individual grains.
Figure 3: BSD image from Location 3 showing dispersed probable Fe-rich particles in a gangue-dominated area.
The three locations reveal significant spatial variability in particle size and Fe-phase distribution, underlining the importance of multi-location imaging for representative sample characterization. In all images, the coexistence of bright Fe-rich regions and darker SiO₂-dominated gangue is clearly visible.
EDS Elemental Mapping for Compositional Identification
To move beyond qualitative phase contrast and confirm the identity of the mineral phases observed in BSD imaging, an EDS elemental map was acquired over the area shown in Figure 1. EDS mapping simultaneously collects X-ray emission data across the entire field of view, producing spatial maps of element distribution that directly correlate with the BSD image contrast.
Figure 4: EDS elemental maps of the Figure 1 area, showing the spatial distribution of Fe, Si, O, and other detected elements across the analyzed field.
Figure 5: Individual EDS maps of Figure 4.
The elemental maps confirm a clear spatial separation between Fe-enriched regions and Si-rich regions. The bright BSD particles correspond predominantly to Fe-rich areas, while the darker surrounding matrix is mainly enriched in Si. Oxygen is detected in association with both the Fe-rich and Si-rich regions, indicating the presence of iron-oxide-bearing minerals and silica-rich gangue respectively. Based on elemental overlap, the Fe-rich areas are consistent with a magnetic iron oxide phase, and the Si- and O-rich areas are consistent with SiO₂-bearing gangue.
Full-field EDS quantification and a summed spectrum were acquired over the entire mapped area to provide bulk compositional context.
| Element | Weight % | Atom % |
| C | 55.7 % | 68.2 % |
| O | 23.7 % | 21.7 % |
| Fe | 9.0 % | 2.4 % |
| Al | 3.7 % | 2.0 % |
| N | 3.3 % | 3.5 % |
| Si | 3.3 % | 1.7 % |
| K | 1.1 % | 0.4 % |
| S | 0.2 % | 0.1 % |
| P | 0.0 % | 0.0 % |
Figure 6: Full-field EDS spectrum and quantification table of the mapped area, confirming the presence of Fe-, Si-, and O-bearing material. The strong C signal is attributed to mounting material or background contribution.
EDS Spot Analysis — Phase-Specific Elemental Quantification
Beyond full-field mapping, targeted EDS spot measurements were performed on individual phases identified in the BSD image of Figure 1 to obtain phase-specific elemental quantification. This approach is particularly powerful for identifying minor or accessory phases that may not be apparent from bulk analysis alone.
Silicate Gangue Phase
An EDS spot measurement on one of the darker minerals visible in the BSD image confirms a Si- and O-rich composition consistent with SiO₂-bearing gangue, with minor K, Al, W, and S also detected, suggesting the presence of a K-bearing aluminosilicate mineral (e.g. potassium feldspar or illite) rather than pure quartz.
Figure 7: EDS spectrum acquired from the spot measurement on the dark gangue particle, showing the characteristic Si and O peaks alongside minor K and Al contributions.
| Element | Weight % | Atom % |
| O | 38.7 % | 45.2 % |
| Si | 21.9 % | 14.6 % |
| C | 19.9 % | 31.0 % |
| K | 8.2 % | 3.9 % |
| Al | 6.8 % | 4.7 % |
| W | 4.3 % | 0.4 % |
| S | 0.1 % | 0.0 % |
| P | 0.0 % | 0.0 % |
Figure 8: Quantitative EDS results for the spot measurement shown in Figure 7.
Iron Oxide Phase
A spot measurement on one of the bright Fe-rich particles yields a Fe- and O-dominated spectrum consistent with an iron oxide mineral phase, with minor Al and S also detected.
Figure 9: EDS spectrum from the Fe-rich particle spot measurement, with dominant Fe and O peaks confirming an iron oxide phase.
| Element | Weight % | Atom % |
| Fe | 59.2 % | 26.8 % |
| O | 21.5 % | 34.1 % |
| C | 17.9 % | 37.8 % |
| Al | 1.3 % | 1.2 % |
| S | 0.1 % | 0.1 % |
| P | 0.0 % | 0.0 % |
Figure 10: Quantitative EDS results for the spot measurement shown in Figure 9.
Sulfur-Bearing Accessory Phase
A localized S-rich area identified in the EDS map (Figure 4) was targeted for spot analysis. The quantification confirms a co-occurrence of Fe and S, consistent with an iron sulfide phase such as pyrite, a common accessory mineral in iron ore deposits that has significant implications for ore processing and environmental management.
Figure 11: EDS spectrum from the S-bearing phase spot measurement, with a prominent S peak alongside Fe.
| Element | Weight % | Atom % |
| C | 35.5 % | 61.7 % |
| Fe | 32.9 % | 12.3 % |
| S | 22.0 % | 14.4 % |
| O | 7.9 % | 10.3 % |
| Al | 1.7 % | 1.3 % |
| P | 0.0 % | 0.0 % |
Figure 12: Quantitative EDS results for the spot measurement shown in Figure 10.
Calcium Phosphate Accessory Phase
A P-rich area identified in the EDS map (Figure 4) was also analyzed. The results indicate a strong Ca and P contribution, consistent with a Ca–P-bearing mineral phase such as apatite, a well-known penalty mineral in iron ore that can negatively affect steel quality if present in sufficient concentrations.
Figure 13: EDS spectrum from the Ca–P-bearing phase spot measurement, showing prominent Ca and P peaks.
| Element | Weight % | Atom % |
| O | 34.1 % | 40.7 % |
| C | 23.1 % | 36.8 % |
| Ca | 22.5 % | 10.7 % |
| P | 14.4 % | 8.9 % |
| Fe | 3.3 % | 1.1 % |
| Al | 1.7 % | 1.2 % |
| Cl | 0.8 % | 0.4 % |
| S | 0.2 % | 0.1 % |
Figure 14: Quantitative EDS results for the spot measurement shown in Figure 12.
Threshold-Based Particle Characterization
In addition to qualitative imaging and elemental analysis, quantitative particle characterization using Semplor Explore Fibers was performed on the BSD image of Figure 1 using threshold-based particle detection. This analysis provides particle count, projected area, perimeter-based size statistics, areal coverage, and global distribution parameters, all extracted directly from the BSD image without the need for additional software or manual counting.
A total of 306 particles were detected in the analyzed field, with a measured areal coverage of 45.39%. This provides a rapid, quantitative first assessment of particle density and size variability within the imaged area, and forms a strong basis for comparing multiple locations or sample preparation conditions.
Figure 15: Particle characterization overlay on the Figure 1 BSD image, showing threshold-based particle detection results and the corresponding quantitative particle statistics table.
Large-Area BSD Mapping for Representative Liberation Analysis
Single-field analysis, while informative, may not be representative of the full sample when significant heterogeneity is present. To assess mineral liberation at a statistically meaningful scale, a large-area BSD dataset was acquired over a stitched grid of approximately 9.748 mm × 9.051 mm (14 × 13 individual SEM images), providing a comprehensive overview of Fe-phase distribution across a macroscopically relevant area.
Figure 16: Large-area BSD map acquired on a 14×13 stitched grid, covering 9.748 mm (W) × 9.051 mm (H). The image provides a representative overview of Fe-phase and gangue distribution across the sample.
Image segmentation using global thresholding (gray-level range 200–255) was applied to isolate high-density Fe-bearing mineral phases from the silicate matrix and mounting resin. This binary segmentation was then refined manually to distinguish liberated Fe-phase grains from locked or associated grains, particles that remain physically connected to gangue material.
The analysis indicates a total Fe-phase content of 12.04% by area, of which 6.21% occurs as liberated grains. This corresponds to a degree of liberation of 51.56%, meaning approximately half of the detected Fe-bearing phase is present as free grains, while the remainder is locked within or associated with silicate gangue. This liberation figure is a key input for process engineers evaluating whether additional comminution (grinding) is required to release sufficient Fe-phase for effective magnetic or gravity separation.
Why Use Tabletop SEM for Iron Ore Analysis?
Traditional iron ore characterization relies on external SEM laboratories, automated mineralogy platforms, or time-consuming wet chemical methods. The Semplor NANOS brings key analytical capabilities in-house:
Rapid phase identification — BSD imaging and EDS mapping distinguish Fe oxides, silicate gangue, sulfides, and phosphate minerals within a single analytical session.
Quantitative liberation data — Large-area stitched BSD maps combined with threshold-based segmentation provide liberation and association data that directly inform beneficiation strategy.
Accessory phase detection — EDS spot analysis identifies low-abundance penalty minerals such as pyrite and apatite that affect processing performance and product quality.
Particle size characterization — Automated threshold-based particle detection delivers quantitative size and coverage statistics without manual measurement.
On-site accessibility — The compact footprint and intuitive interface of the NANOS make it suitable for laboratory environments at mine sites, processing plants, and research institutes alike.
