在激光诱导分解光谱 (LIBS) 中逐渐除波动的现场动态校正,使用拉曼光谱和深度学习
Yao Li1, Leiyi Ding1, Yinghao Wang1
1Key Laboratory of High Performance Manufacturing for Aero Engine (MIIT), Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072 China.
Talanta
|February 25, 2025
概括
这项研究引入了一种新的现场动态校正方法,使用拉曼光谱来稳定激光诱导分解光谱 (LIBS) 测量结果. 该技术显著提高了LIBS准确性,用于实际的分析应用.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 材料科学 材料科学 材料科学
背景情况:
- 激光诱导分解光谱 (LIBS) 的动态波动阻碍了其精度.
- 持续的激光样本相互作用会导致显著的除变异性.
- 开发强大的校正方法对于推进LIBS技术至关重要.
研究的目的:
- 为LIBS.开发一个现场动态校正方法.
- 在LIBS动态消去过程中优化和纠正波动特征.
- 提高LIBS作为分析工具的准确性和适用性.
主要方法:
- 为金属样品构建了一个连续的LIBS废弃模型.
- 在连续激光脉冲下分析了动态除机制.
- 集成的拉曼光谱与深度学习用于现场在线反校正.
- 使用深度卷积神经网络 (CNN) 架构和功能级数据融合.
主要成果:
- 等离子体温度分布被建模为高斯式.
- 实现了LIBS血温度的代校正.
- 分类模型的性能显著改善:准确性,精度,回忆和F1-Score在校正后超过99.3%.
- 校正前的模型性能低于82%.
结论:
- 开发的现场动态校正方法有效地减轻了LIBS波动.
- 拉曼光谱和深度学习的整合提高了LIBS测量的准确性.
- 这种方法为成熟和实用的LIBS应用提供了强有力的支持.
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