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

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.