Abstract
The application of data analytical approaches to understand long-term stability trends of organic photovoltaics (OPVs) is presented. Nearly 1900 OPV data points have been catalogued, and multivariate analysis has been applied in order to identify patterns, produce models that quantitatively compare different internal and external stress factors, and subsequently enable predictions of OPV stability to be achieved. Analysis of the weights associated with the acquired predictive model shows that for light stability (ISOS-L) testing, the most significant factor for increasing the time taken to reach 80% of the initial performance ( T 80) is the substrate and top electrode selection, and the best light stability is achieved with a small molecule active layer. The weights for damp-heat (ISOS-D) testing shows that the type of encapsulation is the primary factor affecting the degradation to T 80. The use of data analytics and potentially machine learning can provide researchers in this area new insights into degradation patterns and emerging trends.
Highlights
Stability remains a critical issue for researchers and industrialists in Organic Photovoltaics (OPVs) and Accelerated Life Testing (ALT) is regularly used, for example, to identify optimal material sets, predicting lifetime and providing relative comparisons of product stability [1]
The multivariate linear regression analysis (MLR) technique has been applied to the accelerated lightdegradation studies (ISOS-L) and accelerated temperature/humidity studies (ISOS-D) separately
Whilst regression data is not shown, the independence criteria are satisfied well for the ISOS-D data and the E0 and T80 from the ISOS-D data some minor trends were observed in the TS80 data
Summary
Stability remains a critical issue for researchers and industrialists in Organic Photovoltaics (OPVs) and Accelerated Life Testing (ALT) is regularly used, for example, to identify optimal material sets, predicting lifetime and providing relative comparisons of product stability [1]. Given the vast amount of data available on OPV stability, it might be possible to utilize large datasets containing results achieved across the community to better understand major patterns in OPV stability as well as enable lifetime prediction. Such a study could improve the understanding of lifetime data as the conclusions of the experiments have been conducted with varying types and levels of stress factors including light, temperature and humidity. Given such a large number of potential combinations, multivariate analysis of large datasets can be invaluable for sourcing trends in data
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