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

• Published in: PloS one Vol. 12; no. 5; p. e0177614
• Main Authors: Gok, Abdulkerim, Ngendahimana, David K., Fagerholm, Cara L., French, Roger H., Sun, Jiayang, Bruckman, Laura S.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.05.2017; Public Library of Science (PLoS)
• Subjects: Additives; Analysis; Animal models; Biodegradation; Cadmium; Chemical engineering; Chemical properties; Climatic data; Climatic zones; Collaboration; Color; Corrosion; Crystallinity; Data processing; Decomposition (Chemistry); Degradation; Discoloration; Dopamine D1 receptors; Durability; Earth Sciences; Engineering; Environmental factors; Epidemiology; Ethylene; Exposure; Genetic transformation; Haze; Heat; Humidity; Hydrolysis; International standardization; Kinetics; Learning algorithms; Light; Manufacturing industry; Materials science; Mathematical models; Membranes, Artificial; Models, Theoretical; Moisture content; Normal distribution; Oxidation; Particulates; Photovoltaic cells; Physical Sciences; Physical training; Physics; Pollution control; Polyethylene terephthalate; Polyethylene Terephthalates - chemistry; Prediction models; Research and Analysis Methods; Safety engineering; Service life; Statistical analysis; Statistical methods; Studies; Temperature effects; Thermodynamics; Transformation; U.V. radiation; Ultraviolet radiation; Ultraviolet Rays; Weathering; United States; Cleveland Ohio; United States > US; Ohio
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177614

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.