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Design and optimization of handheld alloy analysis instrument based on microjoule high pulse repetition frequency LIBS.
Qu, Dongming; Yang, Guang; Zhou, Wenwen; Sun, Huihui; Fang, Jiaxuan; Tian, Di; Li, Chunsheng; Li, Qingkai.
Afiliación
  • Qu D; College of Instrumentation Electrical Engineering, Jilin University, Changchun, JiLin 130061, China.
  • Yang G; Jilin Provincial Key Laboratory of Trace Analysis Technology and Instruments, Changchun, JiLin 130061, China.
  • Zhou W; College of Instrumentation Electrical Engineering, Jilin University, Changchun, JiLin 130061, China.
  • Sun H; Jilin Provincial Key Laboratory of Trace Analysis Technology and Instruments, Changchun, JiLin 130061, China.
  • Fang J; Beijing Triumph Technology Co., Ltd, Beijing 100000, People's Republic of China.
  • Tian D; College of Instrumentation Electrical Engineering, Jilin University, Changchun, JiLin 130061, China.
  • Li C; Jilin Provincial Key Laboratory of Trace Analysis Technology and Instruments, Changchun, JiLin 130061, China.
  • Li Q; College of Instrumentation Electrical Engineering, Jilin University, Changchun, JiLin 130061, China.
Rev Sci Instrum ; 95(8)2024 Aug 01.
Article en En | MEDLINE | ID: mdl-39105598
ABSTRACT
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.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Rev Sci Instrum Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Rev Sci Instrum Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos