As the demand for energy continues to increase, the utilization of organic photovoltaics (OPVs) based on organic semiconductors (OSCs) as alternatives to fossil fuels has become more common. Although OPVs offer many advantages, the susceptibility of the OSC active layer materials to degradation means that after a certain point in the OPV lifecycle, replacement is more economical than continued operation of a low efficiency device. Analytical tools that provide fast and accurate predictive information about the extent of active layer degradation are critically needed to inform workers in the field charged with making decisions about replacement of aging devices. Our work is motivated by the belief that multivariate chemometric techniques, which apply statistical and mathematical methods to chemical data, can be used successfully for these predictive purposes. Our recent work has proven the utility of two such chemometric techniques, principal component analysis (PCA) and linear discriminant analysis (LDA), to classify ATR-FTIR spectral data of photodegraded OSC films by both substrate type and extent of degradation. In follow on efforts described in detail here, we demonstrate the success of ATR-FTIR coupled with PCA-LDA for accurate prediction of the extent of OSC degradation in films of poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl][3-fluoro-2-[(2-ethyl hexyl)carbonyl]thieno[3,4-b]thiophenediyl ]] (PTB7). Additionally, we introduce ATR-FTIR spectral data coupled with PCA-LDA analysis from model organic photovoltaic (OPV) devices based on a stack architecture comprised of a Au cathode, a solid-state PTB7 thin film (active layer), and an indium tin oxide (ITO) anode. These results highlight further the potential for diagnostic use of such spectroscopic tools in practical contexts.

Chemometric approaches for accurate predictive analysis of organic semiconductor photodegradation by FTIR spectroscopy

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