Structure and Surface Passivation of Ultrathin Cesium Lead-Halide Nanoplatelets Revealed by Multilayer Diffraction

06 September 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The research on bidimensional colloidal semiconductors has received a boost from the emergence of ultrathin lead-halide perovskite nanoplatelets. While the optical properties of these materials have been widely investigated, their accurate structural and compositional characterization is still challenging. Here, we exploited the natural tendency of the platelets to stack into highly ordered films, which can be treated as single crystals made of alternating layers of organic ligands and inorganic nanoplatelets, to investigate their structure by Multilayer Diffraction. Using X-ray diffraction alone, this method allowed to refine the structure of ∼12 Å thick Cs-Pb-Br perovskite and ∼25 Å thick Cs-Pb-Cl-I Ruddlesden-Popper nanoplatelets by precisely measuring their thickness, stoichiometry, surface passivation type and coverage, as well as deviations from the crystal structures of the corresponding bulk materials. It is noteworthy that a single, readily available experimental technique, coupled with proper modeling, provides access to such detailed structural and composition information.


multilayer diffraction
lead halide
surface structure

Supplementary materials

Supporting Information
Experimental conditions for the synthesis of Cs-Pb-X nanoplatelets and detailed synthetic protocol for Cs-Pb-Br nanoplatelet synthesis; description of the background and interferent signals subtraction; outline of the Multilayer Diffraction algorithm; preliminary simulations of the Cs-Pb-X diffraction patterns; parametrization of the Cs-Pb-X nanoplatelet structures and fit results; additional comments on the surface passivation of Cs-Pb-X nanoplatelets; SEM-EDS compositional analyses of Cs-Pb-X nanoplatelets.
Experimental Data and Code
Experimental XRD diffractograms and corresponding Jupyter Notebooks; Python script containing the Multilayer Diffraction fitting algorithm (ZIP).


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