评估UV-C诱导的聚氨和聚烯降解:比较O-PTIR和便携式ATR-FTIR
Cihang Yang1, Jun-Li Xu1, Aoife Gowen1
1School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
概括
这项研究使用光谱学和机器学习来检测聚氨和聚烯等聚合物的早期紫外线降解. 这些方法可以在暴露几天内准确识别化学和结构变化.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物化学 聚合物化学
- 频谱学是一种光谱学.
背景情况:
- 紫外线辐射会导致聚合物降解,影响材料的寿命和性能.
- 聚氨 (PU) 和聚烯 (PS) 是常见的聚合物,具有不同的降解模式.
- 早期检测紫外线降解对于材料评估至关重要.
研究的目的:
- 调查PU和PS的早期UV-C诱导的化学和结构变化.
- 评估ATR-FTIR,O-PTIR和机器学习组合用于非破坏性聚合物分析的有效性.
- 建立一种可靠的方法来识别聚合物中的紫外线降解.
主要方法:
- 使用便携式的减弱总反射 - 里埃变换红外光谱 (ATR-FTIR) 和光热光红外光谱 (O-PTIR) 显微镜.
- 使用的机器学习分类模型:部分最小方程差异分析 (PLS-DA),支持向量机 (SVM) 和随机森林.
- 收集的光谱数据,以区分聚合物中紫外线降解的阶段.
主要成果:
- 在O-PTIR数据中,PLS-DA实现了最高的分类准确度 (PS:81%,PU:80%).
- SVM表现出强的表现 (PS: 71%,PU: 82%),捕捉了复杂的光谱关系.
- 综合光谱和机器学习方法成功地从最早的4天暴露中识别出紫外线降解.
结论:
- 开发的方法提供了一种可靠的,非破坏性的方法,用于早期检测聚合物中的紫外线降解.
- O-PTIR为局部降解分析提供高空间分辨率,补充了ATR-FTIR广泛的化学洞察力.
- 机器学习模型,特别是PLS-DA,是分析光谱数据以评估聚合物降解的有效工具.
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