Degradation Science: Mesoscopic Evolution and Temporal Analytics of Photovoltaic Energy Materials

Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, the mesoscopic evolution of mesoscopic energy materials can now be more fully studied. The temporal evolution is vastly complex in time and length scales and is fundamentally challenging to scientific understanding of degradation mechanisms and pathways responsible for energy materials evolution over lifetime. We propose a paradigm shift towards mesoscopic evolution modeling, based on physical and statistical models, that would integrate laboratory studies and real-world massive datastreams into a stress/mechanism/response framework with predictive capabilities. These epidemiological studies encompass the variability in properties that affect performance of material ensembles. Mesoscopic evolution modeling is shown to encompass the heterogeneity of these materials and systems, and enables the discrimination of the fast dynamics of their functional use and the slow and/or rare events of their degradation. We delineate paths forward for degradation science

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

<div><p>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.</p></div

Parameter estimates for Model 1 and Model 2.

<p>Parameter estimates for Model 1 and Model 2.</p

Parameter estimates for Model 3 and Model 4.

<p>Parameter estimates for Model 3 and Model 4.</p

Diagnostic plots for the change in yellowness index (YI) under the DampHeat and FreezeThaw exposures (Model 2).

<p>(A) Residuals vs. fitted and (B) Normal Q-Q.</p

Parameter estimates for Model 1 and Model 2.

<p>Parameter estimates for Model 1 and Model 2.</p

The change in haze (%) for all material and exposure types as a function of exposure step.

<p>HydStab is hydrolytically stabilized PET; UnStab is unstabilized PET; UVStab is UV stabilized PET. Each exposure is plotted on a free scale.</p

Diagnostic plots for the change in haze (%) under the CyclicQUV exposure (Model 3).

<p>(A) Residuals vs. fitted and (B) Normal Q-Q.</p

Diagnostic plots for the change in yellowness index (YI) under the HotQUV and CyclicQUV exposures (Model 1).

<p>(A) Residuals vs. fitted and (B) Normal Q-Q.</p