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机器学习结合激光诱导的分解自逆同位素光谱测量,用于确定同位素度

Madison R Moran, Ashwin P Rao, Anil K Patnaik

|August 12, 2025

概括

激光诱导分解自逆同位素光谱法 (LIBRIS) 量化了同位素. 该研究显示波长偏移与6Li度相关,使用机器学习实现了精确的同位素分析.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
测量同位素比率的测量方法

背景情况:

同位素分析对各种科学领域至关重要.
精确量化同位素 (6Li和7Li) 存在分析方面的挑战.
现有的方法可能缺乏精度或需要复杂的样本准备.

研究的目的:

实施和评估激光诱导分解自逆同位素谱法 (LIBRIS) 用于同位素量化.
为了建立光谱自逆转移和6Li原子百分比之间的相关性.
开发一种机器学习模型,用于准确确定同位素度.

主要方法:

使用LIBRIS测量Li 670.8 nm峰值的自逆转移.使用LIBRIS测量Li 670.8 nm峰值的自逆转移.
准备的LiOH·H2O样本具有不同的6Li原子百分比 (3-95%).
在光谱数据上训练有素的监督机器学习回归模型,包括堆叠组合.

主要成果:

在测试的同位素范围中观察到一个光谱自逆转峰值中心波长转移13.813±1.21 pm.
堆叠组合模型实现了5.66原子%的根平均平方误差 (RMSE).
为6Li度确定了18.8原子%的检测极限.

结论:

利布里斯是一种可行的技术,用于非破坏性量化同位素组成.
机器学习,特别是堆叠组合,显著提高了基于LIBRIS的同位素分析的准确性.
开发的方法为同位素度的确定提供了灵敏而精确的方法.

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