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Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

机译:多因素加速风化暴露下聚对苯二甲酸乙二酯薄膜降解的预测模型

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摘要

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.
机译:在聚对苯二甲酸乙二醇酯(PET)薄膜上进行了加速的风化暴露。针对黄度指数(YI)和雾度(%)的变化,开发了纵向多级预测模型,该模型是PET等级和暴露类型的函数。使用线性固定效应建模方法对YI中具有相似变化的曝光进行建模。由于雾度形成的复杂性,测量的不确定性以及样品响应的差异,雾度的变化(%)取决于各个样品的响应,因此使用了线性混合效应建模方法。当与固定效应模型比较时,在霾形成模型中添加随机效应会显着增加所解释的方差。对于这两种建模方法,诊断图均确认了独立性和同质性,并具有正态分布的残差。对于模型的真实预测误差和预测能力的预测R 2 值表明,该模型不会过度拟合。这些模型可以在给定的曝光时间(或紫外线照射下的光剂量)下,在预定义的曝光条件下进行预测。在结合紫外线和冷凝湿度的循环曝光下,PET的降解是由光解和水解机制引起的,该机制导致泛黄和混浊。通过对这些降解途径的定量了解,可以将这些基于实验室的暴露与实际寿命预测条件进行互相关。

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