Exploring the Impact of Ambient Gas Property on the Signal of Laser-Induced Breakdown Spectroscopy with Neural Network.

IF 6.7 1区化学 Q1 CHEMISTRY, ANALYTICAL

Analytical Chemistry Pub Date : 2025-07-25 DOI:10.1021/acs.analchem.5c02073

Yuzhou Song,Zongyu Hou,Chenyu Yan,Weiran Song,Chenwei Zhang,Zhe Wang

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Abstract

Laser-induced breakdown spectroscopy (LIBS) has long been regarded as an ideal analytical technology with the unique capabilities of real-time and multielement sensing. However, the lack of a clear understanding of the impact of ambient gas properties on the LIBS signal has severely hindered LIBS quantification improvement. We proposed an innovative approach by applying neural networks to discover the dependence of the LIBS signal on the ambient gas properties supported with a series of purposely designed experiments. For the first time, the full picture of the dependence of the LIBS signal on the main gas properties was clearly discovered, and the impact mechanism was further clarified. It is not only the first time that AI was used for complicated physical dependence rather than quantification in LIBS and the spectroscopic field but also established a new paradigm for the application of AI in complicated physical dependence by constructing comprehensive data points that are virtually impossible to attain through traditional experimental methods.

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用神经网络研究环境气体性质对激光诱导击穿光谱信号的影响。

激光诱导击穿光谱（LIBS）以其独特的实时和多元素传感能力一直被认为是一种理想的分析技术。然而，由于缺乏对环境气体性质对LIBS信号影响的清晰认识，严重阻碍了LIBS量化的改进。我们提出了一种创新的方法，利用神经网络来发现LIBS信号对环境气体性质的依赖，并辅以一系列精心设计的实验。首次清晰地发现了LIBS信号对主要气体性质依赖的全貌，并进一步阐明了影响机理。这不仅是人工智能首次在LIBS和光谱领域用于复杂物理依赖而不是量化，而且通过构建传统实验方法几乎无法获得的综合数据点，为人工智能在复杂物理依赖中的应用建立了新的范式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Analytical Chemistry 化学-分析化学

CiteScore

12.10

自引率

12.20%

发文量

1949

审稿时长

1.4 months

期刊介绍： Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.