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The use of microjoule high pulse repetition frequency (PRF) lasers as excitation sources is an important direction in the miniaturisation of laser-induced breakdown spectroscopy (LIBS) instruments. However, high PRF LIBS is sensitive to experimental parameters, and the relationship between the experimental parameters and the spectral intensity of high PRF LIBS is less studied. In this work, we present a model based on Gaussian spot overlapping ablation with aluminium alloys as samples. After the index values have been collected in the non-motion state, the model derivation formula can be used to calculate the intensity of the spectral peaks under any proposed experimental parameters in the motion state. The experimental results show that the mean relative error (MRE) between the measured and predicted values of spectral peak intensity is ≤0.15 under different experimental parameters, and this result proves the validity of the model in predicting the spectral peak intensity. Meanwhile, we used 5 standard aluminium alloy samples to construct the standard curves of measured and predicted values under different experimental parameters. The experimental results show that the MRE between the measured and predicted values is ≤0.15, and the two standard curves fitted with the measured and predicted values have a high similarity with an average R2 ≥ 0.85. This study is expected to provide a universally applicable and efficient method for the quantitative analysis of high PRF LIBS in different application scenarios. [ABSTRACT FROM AUTHOR]

We briefly describe the design of a handheld metal detection instrument based on microjoule high repetition frequency laser-induced breakdown spectroscopy. The instrument uses a Raspberry Pi as the control core and a laser with a frequency of 10 kHz and a single pulse energy of 100 µJ as the excitation source. In addition, a mini-putter is built into the instrument to move the laser, allowing the ablation of the sample surface line area without external auxiliary equipment. The excitation-generated plasma radiation is collected by a simple optical path and transmitted directly to the spectrometer. We also constructed and trained a Backpropagation Artificial Neural Network (BP-ANN) model based on 12 different grades of alloys and transplanted the feedback process of the BP-ANN to the Raspberry Pi, which realized the rapid classification of the 12 alloys with > 95% classification accuracy on the handheld instrument. [ABSTRACT FROM AUTHOR]

The Z-pinch device is a critical component in inertial confinement fusion, where stainless steel electrodes must withstand high current densities of up to MA/cm2. Gases and difficult-to-remove impurities adhering to the electrode surfaces can ionize, significantly impacting the device's electrical conductivity efficiency. In this paper, the surface of stainless steel electrodes was subjected to cleaning using a large-area plasma jet under atmospheric pressure. The wettability, chemical composition, and chemical state of the electrode surface were characterized using a water contact angle measuring instrument and X-ray photoelectron spectroscopy (XPS). The cleaning effect under different discharge parameters was systematically analyzed. The results revealed a significant reduction in the content of carbon pollutants on the surface of stainless steel electrodes, decreasing from 62.95% to a minimum of 37.68% after plasma cleaning. Moreover, the water contact angle decreased from 70.76° to a minimum of 29.31°, and the content of water molecules adsorbed on the surface decreased from 17.31% to a minimum of 5.9%. Based on the evolution process of micro-element content and chemical state on the surface of stainless steel electrode, the cleaning process of adhering substances on the surface by atmospheric pressure plasma was analyzed by the layered cleaning model for surface pollutants on stainless steel. [ABSTRACT FROM AUTHOR]