Machine-Learning and Design of Experiments-based Optimization of Organic Solar Cells

• Author / Creator

  Kirkey, Aaron

• Over the coming decades, global population and energy consumption are projected to increase
  dramatically, with the latter doubling by 2050 as per the most conservative estimates. Much of
  this demand is likely to be met with increased use of fossil fuels. The burning of fossil fuels is
  a major contributor to the ever-increasing CO 2 concentration in the atmosphere, a major driver
  of climate change. In order for countries and companies to meet their climate targets, they must
  undergo a transition to low or CO 2 -free energy sources (wind, solar, hydroelectric, for example).
  Solar power, typically harvested using photovoltaic and solar thermal devices, is considered one
  of the most promising renewable energy technologies due to the sheer quantity of solar irradiation
  impinging upon terrestrial earth. Organic photovoltaics (OPVs) are a subset of PV technology
  that are thin, lightweight, printable using roll-to-roll and spray coating technologies, flexible, and
  can be made semi-transparent. These features enable this class of photovoltaics to be considered
  in markets and locales otherwise inaccessible to traditional silicon devices, which are heavy and
  cumbersome. Organic photovoltaics comprise many layers that need to be manufactured with great
  care in order to yield devices capable of producing substantial power, in a reproducible fashion.
  The central layer in this stack is the critical light absorbing layer that consists of two or three, and
  occasionally more, different organic molecules.

• Subjects / Keywords

  • material science
  • optimization
  • machine-learning
  • photovoltaics
  • Organic phototolvaics

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-nncx-cf31

• License

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