The advancement of SEM with automated mineralogy has provided a quick and relatively economical quantitative mineral analysis solution. However, the absence of statistical errors makes the robustness of the results uncertain. This could damage the reliability of technical solutions taken on the onus of these quantitative outcomes [157]. Automated mineralogy-based measurements have been studied with several methods for the estimation of uncertainties. For instance, a statistical approach was developed by Benvie et al. in 2013 for using SEM automated mineralogy in accordance with diagnostic leaching tests [158]. It was concluded that to derive the standard deviation and background variance, at least two grain mount measurements were needed for each head and leach residue sample. In another study, the variability in mineral liberation analyses and mineral quantity was investigated by Lastra and Paktunc in 2016 [159]. They studied the fraction of sulfide flotation rougher concentrate of −509 to 208 µm in size through interlaboratory testing. Mineral quantities were found to have good agreement with the data, but mineral association and liberation analyses showed less agreement. This finding hints toward the idea that correct mineral liberation and association may not necessarily be found with correct mineral quantities. In 2021, Guseva et al. evaluated the analytical errors in mineralogical measurements by applying the point counting method via binomial distribution approximation [160]. Binomial approximation may not fit well with all cases, especially with coarse materials, and suitable methods for each case should be used, such as estimation of the confidence method [161] or the bootstrap resampling method [162].

The estimation of errors in textural characteristics measured by automated mineralogy can be efficiently identified using the bootstrap resampling method [163]. For instance, the bootstrap approach can help in evaluating the uncertainties related to particle properties measured by SEM automated mineralogy for the evaluation of magnetic separation efficiency [164,165], density separation processes [166], and the simulation and statistical modeling of mechanical separation processes [167]. The bootstrap resampling method considers a population of N samples, takes M random subsets, and replaces the randomly selected samples to ensure that the entire population is available for sampling [168,169]. The accepted statistical methods, which use the point counting method on polished sections and assess errors in mineral grades, agree well with the bootstrap method [170,171,172]. This method has the advantage of being assumption-free and can be applied to a wide range of particle characteristics [162]. It does not assume a bionomical distribution. These methods imply that the standard deviation of mineral grades is proportional to the square root of the number of particles measured or the total area of particles measured. The relative standard deviation of measurements for any mineral grade can be estimated as follows [173]:

The bootstrap method can also provide information about the measurement of how much total area (grains) is needed to reach a given uncertainty. In addition to the uncertainty, SEM also has some drawbacks, including (but not limited to) limited depth of penetration that primarily provides surface information; and low accelerating voltages that provide low-resolution images, while increasing the voltage starts damaging the surface of the sample.

It is a common claim in SEM-based automated mineralogy studies that minerals can be detected, identified, and quantified by their characteristic EDS spectrum (an example is shown in Figure 9, indicating feldspar mineral albite [84]). However, this claim cannot be fully correct, as minerals are characterized by their lattice structure indicated by XRD first, and then comes the use of elemental composition information provided by EDS spectrum quantification. Therefore, mineral identification remains incomplete with use of the EDS spectrum only, based on its foundations on elemental composition. Identifying a mineral by chemical composition alone can be misdirecting, as there are examples of minerals with similar chemical compositions but different crystal structures based on the crystallization conditions of minerals. For instance, pseudorutile and ilmenite are both titanium-iron oxide minerals, but they exhibit different crystal structures [84].

Another challenge in mineral detection, identification, and distinction using EDS spectra is that some minerals have very similar elemental compositions, such as hematite (Fe₂O₃) and magnetite (Fe₃O₄). Hematite is composed of 70% by weight Fe and 30% by weight O, while magnetite is made up of 72% by weight Fe and 28% by weight O. The EDS spectra for both minerals appear to be very similar, and the very trivial differences in Fe and O peaks cannot be resolved by appearance. In such scenarios, it is a good idea to use the BSE image gray level as an additional distinguishing standard. It must be noted that for such a measurement, a specific BSE brightness and contrast calibration is needed. Another challenge is the detection range of EDS spectra, as it does not cover the whole elemental periodic system. For example, the first light elements cannot be detected by EDS, such as H, He, Li, and Be. It is, therefore, recommended to complement EDS spectra with XRD and XRF methodologies for mineral identification and quantification [84]. Other limitations of EDS spectra include longer mapping causing damage to the samples, low sensitivity of light elements, low quantitative accuracy, information about the chemical composition only (not about functional groups or chemical bonds), and overlapping peaks, making it difficult to distinguish among elements present in the sample.

For the success of any SEM analysis, an optimal sample preparation process is essential. A wide variety of samples can be analyzed using SEM. The configuration of the sample holder system and the size of the SEM sample chamber are the defining parameters for choosing the type of sample for investigation. Grain mounts in round epoxy blocks are usually used for particulate or granular samples. If the samples are massive and contain compact matter, such as rocks, petrographic glass-mounted sections can be used. Depending on the type of sample, the production of thin grain mounts on glass is also possible. Two important configurations must be maintained, whether they are samples on glass or round block sample holders, i.e., the holder should be mounted perpendicular to the electron beam and parallel to the BSE detector [84].

Grain mounts in epoxy blocks are the best form to prepare samples if the sample material is noncompact, particulate, or granular, which can be ground, hand-picked single, or broken grains [174]. A potential problem occurs when the grains are not easily separated within the same colored grayscale BSE image, as most SEM-AM software packages are unable to distinguish between them. The use of pure graphite is beneficial in such cases, as it can be utilized in stirred form as a distance material into epoxy resin blocks [174]. In some granular sample cases, a wide range of densities can exist among the phases present in the sample. During the stirring process of sample grains with graphite-saturated epoxy resins, grains with larger sizes and high densities tend to move toward the bottom of the holding block, and it is more probable that small grains will be missed in the analysis. One good practice for dealing with such kinds of samples is cutting round blocks into vertical slices, which can be remounted as vertical sections [84]. It is also possible to study other materials, such as polymers and coal, with the use of EDS detectors. Since the BDE gray value of this organic matter is similar to that of epoxy resin, an alternative embedding material should be used [175]. Carnauba wax is an alternative material that can be used for embedding in these cases [176]. Carnauba wax is a very soft material, making it difficult to polish. One possible solution is to double-mount the Carnauba wax in epoxy resin blocks. Another prospective solution could be the doping of iodoform in epoxy resin [175,177]. The organic matter has a lower atomic number than the epoxy resin, which makes the epoxy a background material. A wide variety of epoxy resins are available for this purpose [178]. SEM images of some epoxy resins and their respective thermal conductivities are shown in Figure 10. In addition to the variety, the proportions of hardener and filler can be varied. The challenges in choosing an epoxy resin are that it must remain stable under a 25 kV electron beam, not evaporate under high vacuum conditions, and harden within convenient temperature conditions and time frames. The recommended approach to solving such problems is continuous application tests.

The complications associated with the sample preparation procedure depend on the type of sample material. If it is solid, dry, compact, and massive, the preparation of thin and thick sections is quite simple. In the case of brittle and/or porous material, epoxy resin is impregnated with a previous material for stabilization before sawing. Thin and thick section production has been reported by several studies [180,181,182]. Usually, silicon carbide (SiC) with 600 to 1000 mesh is used for lapping of the sample material behind the mounting on glass. In the standard lapping procedure, SiC 1000 works best for brittle and soft materials, with minimum substance loss compared to SiC 600. If the sample contains minerals with different optical properties but a closer chemical composition, thin sections are advantageous as an optical microscope can also be used to check the minerals and phases. In addition, a microscope with polarized light can be used to recognize samples with glassy phases owing to their optical isotropy. The reference EDS spectra list can be compiled based on this set of information [84].

A plane and well-polished surface is needed for SEM-AM to analyze grain mounts of thin and thick sections and mounts in epoxy resins. Every material needs a specific treatment, so it is safe to state that the polishing part is a work of craftsmanship. In most cases, water is used in the polishing procedure. If there is a chance of water reacting or mixing with the minerals or materials, the sample preparation procedure can be carried out with water-free liquids such as ethylene glycol [84]. A variety of industrial ashes, such as power plant and sewage ashes, can contain anhydrite, and the use of water-free liquids is recommended in such cases. For samples with varying degrees of particle hardness, covering the polishing plates with hard textile cloth is proposed. Plates covered with soft cloth having long fibers work well for samples containing minerals, soft metals, or ore minerals. The procedure of polishing the sample works well with decreasing grain size, for example, using abrasive papers first, then grinding, and then polishing powders on textile cloth. It is important to mention avoiding the use of lead-bearing polishing plates for general sample preparation, as it may cause sample contamination with lead. For the last step of sample polishing, the use of diamond powder with diamond paste or lubricant is very effective. The polishing procedure can be controlled using a reflected light microscope to inspect the level of successive polishing steps. The impinging electrons in SEM should be dissipated well to obtain optimal BSE images. The use of carbon coating on polished samples provides a solution, which can be accomplished by either evaporation of carbon-loaded thread, electronic carbon thickness control, carbon rods, etc. [84].

The quality of SEM images in publications is essential for clear communication and interpretation. It is also significant for ensuring reproducibility and avoiding hindrance in future research directions. Blurry SEM images also cause limitations in quantitative data extraction and challenges to peer reviewers in analyzing, interpreting, and understanding the results. Low-resolution images in scientific papers appear for several reasons, some of which may be unintentional, while others are the result of constraints or limitations of the research process. The common reasons for the presence of low-quality SEM images in papers may include (but are not limited to) instrument limitations, sample conditions, resource constraints including time and budget, image processing and acquisition, sample size, scope of the paper, image compression, historical or legacy data, data storage, and file size. To produce focused and clear SEM images for the efficient transfer of information, the stigmator tool in the SEM instrument should be properly utilized.

The stigmator is one of the critical components of the SEM instrument and is responsible for maintaining the astigmatism of the electron beam and adjusting the focus of the SEM equipment. While examining the fine details of mineral structures, astigmatism can cause distorted and blurry images. The stigmator ensures the symmetry and focus of the electron beam, consequently producing quality SEM images. The proper use of a well-adjusted stigmator allows characteristic mineral identification, enhanced elemental analysis, quantitative analysis, and precise imaging of microstructures. It also helps in enhancing images of thin sections and provides crystal clear information about crystal faces, surface roughness, and other textural attributes, which is essential for understanding the formation of minerals and digging deep into the geological history of minerals.

Figure 11 shows wollastonite samples mounted on three stubs. Figure 12 shows the effects of layers and sputter coating on SEM analysis by comparing wollastonite samples A, B, and C for three magnifications, i.e., 5k×, 60k×, and 250k×. In the sample preparation stage, sample C was left uncoated to investigate the effect of sputter coating, while samples A and B were coated with gold-platinum coating. It is clearly illustrated in Figure 12 that all SEM images of sample C are fuzzy and dark with few very bright spots and lines, making it difficult to visualize the sample morphology. This is the charging effect, logically occurring due to the absence of a conductive material coating. Another important aspect can be found by comparing the 60k× and 250k× SEM images of sample C with its 5k× image. While charging effects are prominent in all SEM images, that with lower resolution provides better visualization of features when compared to the ones at higher resolution. This suggests that for samples that are difficult to coat with conductive materials, it is useful to capture SEM images at lower resolution. When comparing the SEM images of samples A and B, it is observed that the morphology of the sample can be well studied with single-layered samples compared with multilayered samples.

To ensure that the stigmator is well adjusted for taking quality SEM images, the SEM instrument should be allowed to stabilize and warm up, which will ensure that the electron source and other components of the instrument are at steady state before any adjustment. There are usually two stigmation modes in SEM, i.e., objective lens stigmation and condenser stigmation. The specific requirement of the imaging task will require the selection of an appropriate stigmation mode. Misalignment in electron columns and detectors can adversely affect SEM image quality, which is why it is important to ensure proper alignment of these components before starting the imaging process. Some of the latest SEMs include automated alignment features. The sample preparation stage is also important for avoiding any contamination and charging effects hindering image quality. Dry, clean, and well-mounted samples provide a foundation for high-resolution SEM imaging. While focusing the electron beam on the sample, it is necessary to adjust the astigmatism controls to obtain a sharp image at low magnification. It is considered good practice to select a well-defined edge or feature on the investigated sample as a reference point for stigmation control adjustments. Astigmatism is usually indicated by distortions in the SEM image, such as asymmetrical or elliptical features. In the SEM imaging process, it is important to observe such biases. The X- and Y-stigmation (representing horizontal and vertical stigmation, respectively) need to be adjusted to eliminate any distortions. The focus of the electron beam should be rechecked and adjusted, if necessary, for proper and clear imaging. For optimal SEM imaging, several iterative adjustments might be needed. Figure 13 compares the stigmator adjustment effect on SEM images, which vividly indicates the importance of stigmator adjustment in SEM analysis. Additionally, Figure 14 shows the effect of maintaining the electron beam for a longer period of time at a single point, which damages the surface of the sample (rectangle marks indicated by dashed circles). This issue can be resolved by reducing the voltage of the electron beam, but that comes at the expense of lower resolution of the SEM image. Therefore, it is recommended to find an optimum voltage–resolution combination that works well for a specific type of sample material.