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PhD Thesis Proposal: Julia Huddy

Aug

24

Thursday
9:00am - 11:00am ET

Rm B05, ECSC/Online

Optional ZOOM LINK

"Scalable Fabrication of Perovskite Solar Cells via Large Area Flexography"

Solar energy is a terawatt (TW) source of green power that could scale with global energy demand. Perovskite solar cells are a rising photovoltaic technology with the potential to outperform current Silicon technology in both photovoltaic conversion efficiency and cost efficiency, making them a leading competitor for use in solar energy generation. The materials used in perovskite solar cells are highly amenable to solution processing, making it possible to fabricate these devices using roll-based manufacturing methods, including high-speed flexography, which allows for additive patterning of materials during deposition, eliminating the need for post-deposition scribing steps that are both cost and time intensive.

We propose a combination of high-speed flexography with inline material characterization for rapid deposition and quality monitoring of large-area thin films implemented in perovskite solar cells. Using flexography, we have achieved deposition speeds up to 60 m/min for deposition of both large-area charge transport layers and perovskite thin film absorbers for application in perovskite modules. Patterning capabilities of this technology eliminate the need for post-processing scribing steps currently required by module manufacturing methods. By pairing this with scanning photoluminescence measurement, we optimized the optoelectronic properties of the printed perovskite films and monitored defect formation for higher uniformity devices. This combination of printing and characterization allowed for faster and more efficient production of high-efficiency (> 20 %) perovskite solar cells, accelerating the upscaling of this technology. Continuing this foundational work for design of reliable, high-efficiency devices, we precisely engineer our precursor ink rheologies to develop a class of self-leveling inks for use in flexographic printing, implementing machine learning to optimize key ink parameters and achieve ultra-uniform perovskite films with long-term reliability.

Thesis Committee

  • William Scheideler (Chair)
  • Jifeng Liu
  • Fiona Li
  • Nakita Noel (University of Oxford)

Contact

For more information, contact Julia Abraham at julia.s.abraham@dartmouth.edu.