Data Science Meets Mineral Analysis: An Innovative Laser-Induced Breakdown Spectroscopy Experiment for Undergraduate Chemistry Students

Abstract

Laser-Induced Breakdown Spectroscopy (LIBS) is a versatile technique that can be used to determine the elemental composition of samples in all states of matter. Despite its advantages, including high sensitivity and cost-effectiveness, LIBS is often overlooked in undergraduate chemistry curricula, primarily due to perceived complexity and concerns over equipment costs. This study introduces a comprehensive LIBS laboratory experiment designed for upper-division instrumental analysis courses, focusing on hands-on learning, data science applications, and real-world analytical challenges. Students engage in experiments to determine the elemental composition of metallic alloys and minerals, utilizing Jupyter Notebooks for data analysis─a tool that has significantly improved learning outcomes and reduced misidentifications. The integration of Python programming enhances students’ analytical capabilities and their perception of coding, equipping them with essential skills for handling large data sets. Furthermore, the experiment includes a depth profiling activity, emphasizing sample handling and preparing students for advanced spatial analyses. This adaptable experiment, supported by a range of preparatory materials, can easily be integrated into different institutions’ curricula and offers a dry-lab option. By enabling the analysis of unprocessed samples and demonstrating LIBS’ real-world applications, this study underscores the technique’s relevance and potential. Its inclusion in the chemistry curriculum would better prepare students for the demands of modern chemical analysis.