Analysis iron distribution methods in fine sand- and silt-sized soil particles

Abstract

Iron is prone to change its form and speciation in phases. Thus, several methods have been developed to estimate iron partitioning in the mineral phases of soils. However, the accuracy of these methods to evaluate the iron contribution from minor phases, such as actinolite, almandine, biotite, chlorite, epidote, hornblende, muscovite, and Fe-diospide, remains low. Furthermore, most of the current iron speciation research is focused on bulk samples, and only applies to soil samples that are mostly composed of clays or clay minerals, without a wide-ranging evaluation of soil particles with different grain sizes. In this study, we classified several iron phases using a mineral liberation analyzer on desert soil particles with diameters ranging from silt- to fine-sand (5–20 µm, 20–45 µm, 45–63 µm, 63–75 µm, and > 75 µm). The iron containing minor phases were identified, the modal mineral abundances were determined via matching with the standard energy dispersive spectra library, and a particle size analysis was performed using mineral processing tools on each of the examined 40,000 particles. The iron partition results were ultimately established based on the standard iron concentration in the mineral phases and the modal mineral abundances. This new method could be automated, thereby facilitating high efficiency identification of iron-containing phases that would allow, for the first time, the generation of a dataset for iron partitioning in soil particles. This method allows the identification of minor iron phases in soil particles, and permits in situ mapping of iron mineralogy in fine sand- to silt-sized soil particles. Not restricted by single mineral particles, this method considers multi-phase complex particles. Thus, it largely improves the accuracy for estimating the iron partition parameter.

Genesis of Loess Particles on the Chinese Loess Plateau

“…The MMCA system consists of scanning electron microscopy (SEM), energy‐dispersive spectroscopy (EDS), Back‐Scattered Electron (BSE) imaging, and specialized data processing software (He, Li, et al., 2021). The MMCA technique enables the rapid phase identification and grain size analysis of the particles (Hagni, 2008).…”

“…It was upgraded and combined with the operation of the electron beam across the samples' BSE photos at a user‐defined pixel resolution (5 μm) (He, Sun, et al., 2021). For each pixel, the BSE signal and the Energy Dispersive X‐ray Spectroscopy (EDS) spectrum were correlated and compared with the predefined mineral database to ensure the quality of the data and the representativeness of the samples (large‐n with 10,000 single particles) (He, Li, et al., 2021). This specific setup of the SEM facilitates the analysis of heavy mineral species, which have a much lower concentration (∼1%) than those of quartz and feldspars.…”

“…The system was designed (Fandrich et al., 2007) to identify targeted resource minerals (He, Sun, et al., 2021), the percentages of which are measured in the MLA analysis. These targeted phases were identified and imaged using a computer‐aided system (MLA Processing Tool v2.9) (He, Li, et al., 2021).…”

Clay minerals identification in rock varnish by XRD: A one-step reduction approach

“…It is also reported that the importance of clays in varnish formation has a vital impact on oxides formation in the varnish layer [6] . Hence, it is necessary to remove amorphous coatings and crystals of free iron and manganese oxides from varnish deposited on rock substrates because iron and manganese cause fluorescence of X-Ray diffractograms, obscuring the clay peaks [7] .…”

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Fig. 3 The methodology proposed by He [7] is primarily used in removing the coatings of carbonates, organics, and Fe-Mn oxide-hydroxide in soil and sediments, which resulted in aggregates and coagulation of grains, however, the rock varnish is mainly Fe-Mn rich layer and therefore, the proposed one-step method is sufficient to serve the purpose.

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