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    Local Defects in colloidal quantum dot thin films measured via spatially resolved multi-modal optoelectronic spectroscopy.

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    Date Issued
    2020-03
    Publisher Version
    10.1002/adma.201906602
    Author(s)
    Lin, Yida
    Gao, Tina
    Pan, Xiaoyun
    Kamenetska, Maria
    Thon, Susanna M.
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    Permanent Link
    https://hdl.handle.net/2144/40502
    Version
    Accepted manuscript
    Citation (published version)
    Yida Lin, Tina Gao, Xiaoyun Pan, Maria Kamenetska, Susanna M Thon. 2020. "Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially Resolved Multi-Modal Optoelectronic Spectroscopy.." Adv Mater, Volume 32, Issue 11, pp. e1906602 - ?. https://doi.org/10.1002/adma.201906602
    Abstract
    The morphology, chemical composition, and electronic uniformity of thin-film solution-processed optoelectronics are believed to greatly affect device performance. Although scanning probe microscopies can address variations on the micrometer scale, the field of view is still limited to well under the typical device area, as well as the size of extrinsic defects introduced during fabrication. Herein, a micrometer-resolution 2D characterization method with millimeter-scale field of view is demonstrated, which simultaneously collects photoluminescence spectra, photocurrent transients, and photovoltage transients. This high-resolution morphology mapping is used to quantify the distribution and strength of the local optoelectronic property variations in colloidal quantum dot solar cells due to film defects, physical damage, and contaminants across nearly the entire test device area, and the extent to which these variations account for overall performance losses. It is found that macroscopic defects have effects that are confined to their localized areas, rarely prove fatal for device performance, and are largely not responsible for device shunting. Moreover, quantitative analysis based on statistical partitioning methods of such data is used to show how defect identification can be automated while identifying variations in underlying properties such as mobilities and recombination strengths and the mechanisms by which they govern device behavior.
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