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Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.

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Researchers developed a self-driving lab to optimize organic photovoltaics (OPVs). This automated system significantly reduces material usage, enabling rapid discovery of efficient and stable OPV blends.

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Area of Science:

  • Materials Science
  • Renewable Energy
  • Chemical Engineering

Background:

  • Organic photovoltaics (OPVs) efficiency and stability are enhanced by multicomponent active layer blends.
  • Developing optimal polymer blends for OPVs requires efficient experimental methods.

Purpose of the Study:

  • To develop high-throughput and autonomous experimentation methods for optimizing multicomponent polymer blends in OPVs.
  • To demonstrate a self-driving laboratory for mapping and optimizing quaternary OPV blends for photostability.

Main Methods:

  • Automated film formation capable of fabricating up to 6048 films per day.
  • Integration of Bayesian optimization with the automated platform to create a self-driving laboratory.
  • Autonomous evaluation of measurements to design and execute subsequent experiments.

Main Results:

  • A 4D parameter space of quaternary OPV blends was mapped and optimized for photostability.
  • Screening of 2000 combinations using less than 10 mg of material, compared to ~100 mg with conventional methods.
  • Identification of stable OPV compositions using machine-learning-enabled autonomous experimentation with less than 1 mg of material.

Conclusions:

  • Automated experimentation platforms and self-driving laboratories accelerate the optimization of multicomponent polymer blends for OPVs.
  • Significant reduction in material consumption is achievable with autonomous experimentation, facilitating faster discovery of high-performance OPV materials.
  • The developed methods pave the way for rapid advancement in OPV efficiency and stability through intelligent experimental design.