Integrated Laser-Induced Breakdown Spectroscopy and Pythonic Algorithms for Analysis of Penny Composition and Beyond

Pythonic algorithms are powerful tools for spectral analysis, determining the presence of certain elements by analyzing the spectra produced by Laser-Induced Breakdown Spectroscopy (LIBS). The LIBS setup used for data acquisition utilizes an Nd:YAG 1064 nm pulsed infrared laser to excite penny samples for spectral analysis. A set of algorithms was developed to compile and analyze the emission peaks of pennies using National Institute of Standards and Technology (NIST) wavelength data to identify specific metals in penny samples. These programs detect excited metal atoms and ions through their characteristic emission peaks. These algorithms not only automate spectral line identification but also optimize peak detection by accounting for noise and signal variations. Insights from penny composition algorithms pave the way for expanding LIBS applications to environmental monitoring, such as detecting trace metals in water, soil, or biological samples. Utilizing robust code that can analyze samples with predictable composition boosts confidence in determining the makeup of unknown samples. As the algorithms improve, they become instrumental in identifying contaminants in more complex, heterogeneous samples, supporting environmental sustainability and public health initiatives. This research advances LIBS applications and provides an educational platform, engaging high school students in a hands-on experience at the crossroads of physics, chemistry, environmental science, and beyond.