Understanding Heterogeneity of a Slag-Derived Weathered Material: The Role of Automated SEM-EDS Analyses

Automated SEM-EDS Analysis of Transition Metals and Other Metallic Compounds Emitted from Incinerating Agricultural Waste Plastic Film

Illegal open burning, which is criticized as a leading source of air pollutants among agricultural activities, currently requires constant effort and attention from countries around the world. A speciation analysis method is required to examine the harmful effects of particulate matter generated by incineration on the human body. In this study, to simulate open-air incineration, infrastructure for incineration tests complying with US EPA method 5G was built, and a large-area analysis was conducted on the particulate matter through automated SEM-EDS. For test specimens, waste mulching LDPE collected by Korea Environment Corporation Dangjin Office was used. To raise the identifiability of analyzed particles, the specimen was sampled on a plate made of Ag, which has a high atomic number, three times. Metal particulate matter showed a high reaction to C and C-O. The ratio of metal particulate matters that reacted to C and C-O was in the order of Cu (94.1%) > Fe (83.3%) > Al (79.7%). In this study, it was verified that waste mulching adsorbs metal chemicals originating from the soil due to its properties and deterioration, and that when it is incinerated, it emits particulate matter containing transition metals and other metals that contribute to excessive ROS production and reduction.

Correlating whole sample EDS and Raman mappings - A case study of a Chelyabinsk meteorite fragment

Whole sample microscopy mappings are of interest in many cases as they provide analytical information of phases varying in size by orders of magnitude and in composition across the sample. These benefits are amplified if more than one microscopic technique is used for the mappings. However, to take full advantage of correlative whole sample mappings, the data of each technique has to be carefully prepared, treated, correlated and evaluated. With this work, we want to present the key steps of our data treatment approach as well as the results on an exemplary sample, the Chelyabinsk meteorite. The most important step in our data treatment approach is to start by evaluating the spectral maps separately as far as possible (at-% quantification for EDS for example) and then generate pseudo spectral maps from this evaluation in the form of image stacks. This allows us to preserve the advantages of specialized software packages and standard work flows for every spectral mapping, whilst also unifying the data format and compressing the data sufficiently for correlation and the application of machine learning tools. We have performed whole sample mappings using SEM, EDS and Raman on a cross-section of a Chelyabinsk meteorite fragment, roughly 1.0cm × 0.8cm large. Combining these mappings into a single "super" spectral map, we were able to produce a uniquely detailed mapping of the composition of the meteorite fragment, as well as perform a quantitative analysis of the elemental composition of several crystallographic phases. The results of our compositional analysis; olivine (Fo₇₂Fa₂₈), pyroxene (≈ 97 % En₈₀Fs₂₀Wo₀ and 3 % En₅₆Fs₆Wo₃₈), feldspar (albite), troilite, FeNi (taenite and kamacite), merrillite, chromite and hydroxyapatite; agree qualitatively with other reports from literature.

Editorial for Special Issue “Metallurgical Slags”

Exploitation and further processing of mineral resources are of great importance for modern society [...]

SEM-Based Automated Mineralogy and Its Application in Geo- and Material Sciences

Scanning electron microscopy based automated mineralogy (SEM-AM) is a combined analytical tool initially designed for the characterisation of ores and mineral processing products. Measurements begin with the collection of backscattered electron (BSE) images and their handling with image analysis software routines. Subsequently, energy dispersive X-ray spectra (EDS) are gained at selected points according to the BSE image adjustments. Classification of the sample EDS spectra against a list of approved reference EDS spectra completes the measurement. Different classification algorithms and four principal SEM-AM measurement routines for point counting modal analysis, particle analysis, sparse phase search and EDS spectral mapping are offered by the relevant software providers. Application of SEM-AM requires a high-quality preparation of samples. Suitable non-evaporating and electron-beam stable epoxy resin mixtures and polishing of relief-free surfaces in particles and materials with very different hardness are the main challenges. As demonstrated by case examples in this contribution, the EDS spectral mapping methods appear to have the most promising potential for novel applications in metamorphic, igneous and sedimentary petrology, ore fingerprinting, ash particle analysis, characterisation of slags, forensic sciences, archaeometry and investigations of stoneware and ceramics. SEM-AM allows the quantification of the sizes, geometries and liberation of particles with different chemical compositions within a bulk sample and without previous phase separations. In addition, a virtual filtering of bulk particle samples by application of numerous filter criteria is possible. For a complete mineral phase identification, X-ray diffraction data should accompany the EDS chemical analysis. Many of the materials which potentially could be characterised by SEM-AM consist of amorphous and glassy phases. In such cases, the generic labelling of reference EDS spectra and their subsequent target component grouping allow SEM-AM for interesting and novel studies on many kinds of solid and particulate matter which are not feasible by other analytical methods.

Automated mineralogy for quantification and partitioning of metal(loid)s in particulates from mining/smelting-polluted soils

Topsoils near active and abandoned mining and smelting sites are highly polluted by metal(loid) contaminants, which are often bound to particulates emitted from ore processing facilities and/or windblown from waste disposal sites. To quantitatively determine the contaminant partitioning in the soil particulates, we tested an automated mineralogy approach on the heavy mineral fraction extracted from the mining- and smelting-polluted topsoils exhibiting up to 1920 mg/kg As, 5840 mg/kg Cu, 4880 mg/kg Pb and 3310 mg/kg Zn. A new generation of automated scanning electron microscopy (autoSEM) was combined and optimized with conventional mineralogical techniques (XRD, SEM/EDS, EPMA). Parallel digestions and bulk chemical analyses were used as an independent control of the autoSEM-calculated concentrations of the key elements. This method provides faster data acquisition, the full integration of the quantitative EDS data and better detection limits for the elements of interest. We found that As was mainly bound to the apatite group minerals, slag glass and metal arsenates. Copper was predominantly hosted by the sulfides/sulfosalts and the Cu-bearing secondary carbonates. The deportment of Pb is relatively complex: slag glass, Fe and Mn (oxyhydr)oxides, metal arsenates/vanadates and cerussite were the most important carriers for Pb. Zinc is mainly bound to the slag glass, Fe (oxyhydr)oxides, smithsonite and sphalerite. Limitations exist for the less abundant contaminants, which cannot be fully quantified by autoSEM due to spectral overlaps with some major elements (e.g., Sb vs. Ca, Cd vs. K and Ca in the studied soils). AutoSEM was found to be a useful tool for the determination of the modal phase distribution and element partitioning in the metal(loid)-bearing soil particulates and will definitely find more applications in environmental soil sciences in the future.

Towards Identification of Zircon Populations in Permo-Carboniferous Rhyolites of Central Europe: Insight from Automated SEM-Mineral Liberation Analyses

Zircon is a main mineral used for dating rhyolitic magmas as well as reconstructing their differentiation. It is common that different populations of zircon grains occur in a single rhyolitic sample. The presence of both autocrystic and antecrystic zircon grains is reflected in their strongly varied chemical compositions and slight spread of ages. However, postmagmatic processes may induce lead loss, which is also recorded as a spread of zircon ages. Therefore, new approaches to identify different zircon populations in rhyolitic rocks are needed. In this study, we suggest that detailed examination of zircon positions in the thin sections of rhyolitic rocks provides valuable information on zircon sources that can be used to identify autocrystic and antecrystic zircon populations. Automated Scanning Electron Microscope (SEM) analyses are of great applicability in determining this, as they return both qualitative and quantitative information and allow for quick comparisons between different rhyolite localities. Five localities of Permo-Carboniferous rhyolites related to post-Variscan extension in Central Europe (Organy, Bieberstein, Halle, Chemnitz, Krucze) were analyzed by automated SEM (MLA-SEM). The samples covered a range of Zr whole rock contents and displayed both crystalline and glassy groundmass. Surprisingly, each locality seemed to have its own special zircon fingerprint. Based on comparisons of whole rocks, modal composition and SEM images Chemnitz ignimbrite was interpreted as containing mostly (or fully) antecrystic zircon, whereas the Bieberstein dyke was shown to possibly contain both types, with the antecrystic zircon being associated with disturbed cumulates. On the other hand, Organy was probably dominated by autocrystic zircon, and Krucze contained dismembered, subhedral zircon in its matrix, whereas Halle zircon was located partly in late veins, filling cracks in laccolith. Both localities may, therefore, contain zircon populations that represent later stages than the crystallization of the main rhyolitic body.

Automated SEM Mineral Liberation Analysis (MLA) with Generically Labelled EDX Spectra in the Mineral Processing of Rare Earth Element Ores

Many rare earth element (REE) deposits have experienced multistage geological enrichment processes resulting in REE bearing mineral assemblages of considerable complexity and variability. Automated scanning electron microscopy (SEM) mineral liberation analysis of such REE ores is confronted by the difficult assignment of energy-dispersive X-ray (EDX) spectra to REE mineral names. To overcome and bypass this problem, a generic and reliable labelling of EDX reference spectra obtained from REE-bearing minerals based on their contents of Si, Ca, F and P in a bulk normalised analysis is proposed. The labelled spectra are then combined into groups of REE-P (~monazite), REE-Ca-Si-P (~britholite), REE-Ca-F (~synchysite) and REE-F (~bastnaesite, parisite, fluocerite). Mixed spectra with low counts for REE from minute REE mineral grains are combined into a separate group. This classification scheme is applied to automated SEM mineral liberation analysis (MLA) data from beneficiation products by comminution and multistage flotation of REE carbonatite ores. Mineral modes, mineral grain size distribution, mineral liberation, mineral locking and mineral grade versus recovery curves based on the analysis of >200,000 particles in a sample can be recognised and interpreted in virtual grain size fractions. The approach as proposed here will allow future process mineralogical studies of REE deposits to be robust and comparable.