LIBS Info: Element Analysis

Title Authors Material Detector Spectrometer Software
Rapid Nuclear Forensics Analysis via Machine-Learning-Enabled Laser-Induced Breakdown Spectroscopy (LIBS) Kalambuka Hudson Angeyo, Alix Dehayem-Kamadjeu, Bobby Bhatt Actinides Ocean Optics HR2000 Ocean Optics HR2000 MATLAB
Laser: Nd:YAG
Gate Delay: Noneus
Gate Width: Noneus
Pellets of UO3 mixed with cellulose were analysed to understand the applicability of LIBS for the rapid quantification of Nuclear Materials [Nuclear Forensics]. Machine Learning Techniques (PCA and ANN) when then used to improve the performance/detection
Element Detection Limit (ppm) Wavelength (nm) Other Wavelengths (nm) Calibration Method Calibration Range (ppm) Notes
U 34.0000 (None) -10.0000 N/A Machnine Learning 54.0000-677.0000 The Machine learning model utilised Uranium peaks from several parts of the spectrum ((1) uranium lines (348 nm to 455 nm), (2) uranium lines (380 nm to 388 nm), and (3) subtle uranium peaks (UV range).) to aid detection. Relative Errors of 6-10% were achieved on validation samples

Element RMSE (ppm) Wavelength (nm) Calibration Method Notes