Advancing Quantitative Laser-Induced Breakdown Spectroscopy Algorithms for Environmental Analysis

Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful technique for determining the elemental composition of materials by optical emission spectroscopy. Building on our development of Pythonic algorithms for automated spectral analysis, we are refining these tools for environmental applications. We analyzed brownfield soil and sargassum samples collected in Miami to identify potential contaminants, comparing observed emission lines with National Institute of Standards and Technology (NIST) wavelength data and validating our methods against known standards. Our current focus is advancing from qualitative detection to quantifiable analysis, determining both element composition and concentrations. To improve precision, we are testing multivariate calibration methods that analyze multiple lines and elements simultaneously while accounting for spectral overlaps. We are also mitigating matrix effects, in which variables such as ambient air composition, sample preparation, or surface roughness influence plasma formation and signal intensity. By experimentally testing these parameters and adjusting accordingly, we can identify optimal sample preparation and measurement conditions to establish a robust framework for accurate, reproducible LIBS analysis. Extending this approach to real-world environmental systems, our research highlights how student-driven innovation can transform laboratory techniques into tools for understanding and addressing ecological challenges.