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Bayesian optimization based on Gaussian process regression was applied to optimize the conditions for the highly sensitive detection of surface contamination by laser-induced breakdown spectroscopy (LIBS). Three experimental parameters for laser ablation–pulse energy, stage height, and detection height, with 2160 possible combinations were employed for simultaneous optimization. The reciprocal value of the limit of detection (LOD) was defined as a target variable. This value was obtained from ten LIBS measurements of a pair of samples: a clean substrate and a sample with adhered silicone oil with a specified surface concentration. In total, 173 experiments under 91 conditions were completed in 16 optimization rounds. Six candidate conditions for LOD evaluation were specified based on the optimization results. The LOD evaluation by LIBS employing a detection system using an intensified charge-coupled device provided the lowest LOD value of 0.084 ± 0.008 μg cm−2. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) was applied to inspect trace substances on aluminum alloy surfaces. Silicone oil, which is often used as a release agent, was employed as the adhered substance. Nanosecond laser pulses at UV wavelengths (KrF laser, λ = 248 nm) were employed for the LIBS measurements. Although the absorption of silicone oil at 248 nm was negligible, the Si emission of surface-adhered silicone oil was detected. The intensity ratio of the Si emission at 288 nm to the Al emission at 309 nm increased with increasing surface concentrations in the range 1–35 μg cm−2, and a linear dependence on the silicone oil surface concentration was observed at low surface concentrations (＜5.0 μg cm−2). The limit of detection was evaluated to be 1.18 μg cm−2. [ABSTRACT FROM AUTHOR]