Data-driven modelling for electrolyte optimisation in dye-sensitised solar cells and photochromic solar cells

Area of Science:

• Materials Science
• Renewable Energy
• Photovoltaics

Background:

• Dye-sensitized solar cells (DSSCs) offer potential for semi-transparent glazing applications.
• Photovoltaic performance and light transmission in DSSCs are influenced by dyes and electrolytes.
• Photochromic dyes enable dynamic light transmission in solar cells, but require tailored electrolytes.

Purpose of the Study:

• Develop novel photochromic dyes for solar cells.
• Create electrolytes optimized for these photochromic dyes.
• Investigate electrolyte formulations for improved transparency and photovoltaic efficiency.

Main Methods:

• Design of Experiments (DoE) combined with Machine Learning (ML) for electrolyte optimization.
• Characterization of new photochromic dyes.
• Testing of iodine-based and TEMPO/TEMPO+ redox system electrolytes.

Main Results:

• Optimized iodine-based electrolytes achieved 57%-23% average visible transmittance (AVT) with >2.9% photovoltaic conversion efficiency (PCE).
• TEMPO-based electrolytes yielded photochromic cells with 55%-13% AVT and up to 3.46% PCE.
• A TEMPO-based electrolyte with a non-photochromic dye achieved a PCE of 7.64%.

Conclusions:

• An ML-driven approach efficiently optimizes electrolytes for photochromic DSSCs.
• Tailored electrolytes significantly enhance the performance of DSSCs for dynamic glazing.
• High PCEs were achieved with both photochromic and non-photochromic dyes using optimized TEMPO-based electrolytes.