从分子层面探索溶解有机物质光化学的复杂性,使用机器学习方法
Chen Zhao1, Xinyue Xu2, Hongmei Chen3
1Department of Ocean Science and Center for Ocean Research in Hong Kong and Macau, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
Environmental science & technology
|May 29, 2023
概括
机器学习模型现在可以预测溶解有机物 (DOM) 的光化学反应性. 这种方法整合了辐射实验,以了解河口的DOM转变和退化.
科学领域:
- 环境化学环境化学
- 有机地化学 有机地化学
- 摄影化学的使用.
背景情况:
- 溶解有机物 (DOM) 对于海洋有机物运输至关重要.
- 光化学反应显著改变了DOM在海上运输中的命运.
- 传统辐射实验的不一致结果限制了对DOM光化学的理解.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测DOM分子的光化学反应性.
- 克服传统方法在评估DOM光化学命运方面的局限性.
- 分析全球不同河口的DOM光化学反应.
主要方法:
- 在河口DOM样本上进行了辐射实验.
- 利用里叶变换离子循环子子共振质谱法 (FT-ICR-MS) 进行分子特征.
- 将概率标签分配给分子以定义光化学反应性.
- 开发并应用机器学习模型来预测未分析的分子的反应性.
主要成果:
- 使用ML模型识别了许多具有强光可变性的分子.
- 证明河流DOM化学是光化学转化的主要驱动因素.
- 成功预测了辐射实验中未检测到的分子的光化学反应性.
结论:
- 一种可扩展和可再生的基于ML的方法可以整合用于DOM研究的现有辐射数据.
- 这种ML方法提供了对DOM转换和降解过程的见解.
- 河流DOM成分显著影响其在海洋环境中的光化学命运.
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