Login / Signup

Texture Formation in Polycrystalline Thin Films of All-Inorganic Lead Halide Perovskite.

Julian A SteeleEduardo SolanoHandong JinVittal PrakasamTom BraeckeveltHaifeng YuanZhenni LinRené de KloeQiong WangSven M J RoggeVeronique Van SpeybroeckDmitry ChernyshovJohan HofkensMaarten B J Roeffaers
Published in: Advanced materials (Deerfield Beach, Fla.) (2021)
Controlling grain orientations within polycrystalline all-inorganic halide perovskite solar cells can help increase conversion efficiencies toward their thermodynamic limits; however, the forces governing texture formation are ambiguous. Using synchrotron X-ray diffraction, mesostructure formation within polycrystalline CsPbI2.85 Br0.15 powders as they cool from a high-temperature cubic perovskite (α-phase) is reported. Tetragonal distortions (β-phase) trigger preferential crystallographic alignment within polycrystalline ensembles, a feature that is suggested here to be coordinated across multiple neighboring grains via interfacial forces that select for certain lattice distortions over others. External anisotropy is then imposed on polycrystalline thin films of orthorhombic (γ-phase) CsPbI3- x Brx perovskite via substrate clamping, revealing two fundamental uniaxial texture formations; i) I-rich films possess orthorhombic-like texture (<100> out-of-plane; <010> and <001> in-plane), while ii) Br-rich films form tetragonal-like texture (<110> out-of-plane; <110> and <001> in-plane). In contrast to relatively uninfluential factors like the choice of substrate, film thickness, and annealing temperature, Br incorporation modifies the γ-CsPbI3- x Brx crystal structure by reducing the orthorhombic lattice distortion (making it more tetragonal-like) and governs the formation of the different, energetically favored textures within polycrystalline thin films.
Keyphrases
  • perovskite solar cells
  • room temperature
  • contrast enhanced
  • crystal structure
  • solar cells
  • high temperature
  • high efficiency
  • magnetic resonance
  • ionic liquid
  • magnetic resonance imaging
  • machine learning
  • deep learning