Ph.D. Candidate supervised by Prof. Osman Bakr
ABSTRACT: Of the emerging photovoltaic technologies, perovskite solar cells (PSCs) are arguably the most promising for commercialization. Worldwide interest has prompted researchers to produce tens of thousands of studies on the topic, making PSCs one of the hottest research topics of the past decade. Unfortunately, the rapid output of a substantial number of publications has made the traditional literature review process a cumbersome task for both the novice and expert. In this dissertation, a data-driven analysis utilizing a novel natural language processing (NLP) pipeline is applied on the 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 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 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 brings it closer to that of the top-performing photovoltaic material, single-crystal 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.