Development of High-Efficiency Single-Crystal Perovskite Solar Cells Guided by Text-Based Data-Driven Insights

Student thesis: Doctoral Thesis


Of the emerging photovoltaic technologies, perovskite solar cells (PSCs) are arguably among the most promising candidates for commercialization. Worldwide interest has prompted researchers to produce tens of thousands of studies on the topic, making PSCs one of the most active research topics of the past decade. Unfortunately, the rapid output of a substantial number of publications has made the traditional literature review process and research plans cumbersome tasks for both the novice and expert. In this dissertation, a data-driven analysis utilizing a novel text mining and natural language processing pipeline is applied on the perovskite literature to help decipher the field, uncover emerging research trends, and delineate an experimental research plan of action for this dissertation. The analysis led to the selection and exploration of two experimental projects on single-crystal PSCs, which are devices based on micrometers-thick grain-boundary-free monocrystalline films with long charge carrier diffusion lengths and enhanced light absorption (relative to polycrystalline films). First, a low-temperature crystallization approach is devised to improve the quality of methylammonium lead iodide (MAPbI3) single-crystal films, leading to devices with markedly enhanced open-circuit voltages (1.15 V vs 1.08 V for controls) and power conversion efficiencies (PCEs) of up to 21.9%, among the highest reported for MAPbI3-based devices. Second, mixed-cation formamidinium (FA)-based single-crystal PSCs are successfully fabricated with PCEs of up to 22.8% and short-circuit current values exceeding 26 mA cm-2, achieved by a significant expansion of the external quantum efficiency band edge, which extends past that of the state-of-the-art polycrystalline FAPbI3-based solar cells by about 50 meV — only 60 meV larger than that of the top-performing photovoltaic material, GaAs. These figures of merit not only set new record values for SC-PSCs, but also showcase the potential of adopting data-driven techniques to guide the research process of a data-rich field.
Date of AwardNov 2022
Original languageEnglish (US)
Awarding Institution
  • Physical Sciences and Engineering
SupervisorOsman Bakr (Supervisor)


  • solar cells
  • single-crystal perovskites
  • text mining
  • natural language processing
  • energy

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