Predictive Modeling of Dye Solar Cell Degradation

Aapo Poskela*, Armi Tiihonen, Heikki Palonen, Peter D. Lund, Kati Miettunen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
64 Downloads (Pure)


Degradation of dye solar cell performance based on the early changes in electrolyte color is predicted, allowing to estimate the lifetime of the dye solar cells even before their efficiency declines. Previous predictive models commonly rely on regression analysis of the predicted parameter; thus, they are unable to capture degradation before a significant decrease in performance. Degradation tests, even when accelerated, may take thousands of hours. As such, recognizing degradation trends early can lead to rewarding cuts in the duration of solar cell development pipelines. With accurate lifetime predictions, researchers can steer materials research to reach longer lifetimes in shorter cycles. The predictive power of our model relies on color changes in the electrolyte that directly correlate with the concentration of tri-iodide charge carriers within it, the loss of which is the predominant degradation mechanism for most liquid-electrolyte dye solar cells. By linking the physical mechanisms inside the cell, which eventually start to degrade the performance of dye solar cells, an early prediction of the lifetime can be made even when the device performance still appears stable. It is exemplified with dye solar cells that integrating architecture-specific knowledge on degradation mechanisms has potential to improve lifetime predictions for photovoltaics.

Original languageEnglish
Article number2101004
Number of pages8
JournalSolar RRL
Issue number6
Early online date11 Feb 2022
Publication statusPublished - Jun 2022
MoE publication typeA1 Journal article-refereed


  • color analysis
  • degradation
  • dye solar cells
  • lifetime prediction
  • stability


Dive into the research topics of 'Predictive Modeling of Dye Solar Cell Degradation'. Together they form a unique fingerprint.

Cite this