Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, including specimen preparation and image processing, and the fundamental theoretical calculations underlying SEM operation. It provides a foundational understanding for engineers and scientists who have never had a chance to dig in depth into SEM, contributing to their understanding of the workings and development of this robust analytical technique. The present review covers how SEM serves as a crucial tool in mineral characterization, with specific discussion on the workings and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, understanding the uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end, future research directions for SEM are analyzed with the possible incorporation of machine learning, deep learning, and artificial intelligence tools to automate the process of mineral identification, quantification, and efficient communication with researchers so that the robustness and objectivity of the analytical process can be improved and the analysis time and involved costs can be reduced. This review also discusses the idea of integrating robotics with SEM to make the equipment portable so that further mineral characterization insight can be gained not only on Earth but also on other terrestrial grounds.

Scanning electron microscopy (SEM) is a powerful tool in the domain of material science, mining, and geology, owing to its enormous potential to provide unique insights into the micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation including the specimen preparation and imaging process, and fundamental theoretical calculations underlying the SEM operation. It provides foundational understanding to the engineers and scientists, who never got a chance to dig in-depth into the SEM, to understand the working and development of this robust analytical technique. The present review covers how SEM has been serving as a crucial tool in mineral characterization, with specific discussions on the working and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, the understanding of uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end, future research directions for SEM are analyzed with the possible incorporation of machine learning, deep learning, and artificial intelligence tools, for automating the process of mineral identification, quantification, and efficient communication with the researchers, so that the analytical process robustness and objectivity can be improved, and the analysis time and the involved costs can be brought down. This review also discusses the idea of integrating robotics with SEM, to make the equipment portable, so that further mineral characterization insights can be gained not only on earth but also on other terrestrial grounds.