Publication: Fabrication and study of self-assembled NiO surface networks assisted by Sn doping
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2020-06-25
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Elsevier
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Abstract
Complex patterning of surfaces commonly requires a significant effort in terms of time, cost, and advanced post-fabrication treatments design, which could be overcome by self-organization mechanisms avoiding damage to the surface materials and enhancing properties of interest. In the present work, grid self-assembled NiO complex surfaces have been fabricated assisted by Sn incorporation during growth, following thermal treatments at 1400 degrees C under a controlled Ar flow. The singular morphologies achieved enable the fabrication of robust high surface to volume ratio surfaces with a wide range of surface properties and functionalities. Based on the research performed by a set of complementary techniques, the mechanisms involved in the formation of these textured surfaces have been discussed and some of the fundamental electronic and optical properties of NiO have been analyzed, both aspects necessary to head up the potential development of applications based on this p-type material which is arousing growing attention. These singular micro- and nanostructures present high luminescence and tunable performance unusual to bulk NiO samples, which can broaden the applicability of this material to light-emitting devices. Moreover, the surface evolution depends on a controllable way on the preferential state of charge of the incorporated Sn, which could be selected through the convenient Sn-based precursor, its ratio to the metallic Ni starting precursor, and the atmosphere and duration of the fabrication treatment. Finally, in order to assess the dependence of some potential NiO-based applications on the insights achieved on the surface characterization, the gas-sensing response to ethanol from the Sn doped NiO samples was also evaluated. The study of the processes and mechanisms involved in the growth of these grid-patterned surfaces can be extended to similar oxide-based systems.
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©2020 Elsevier
The work was supported by MINECO/FEDER/M-ERA.Net Cofund projects: MAT 2015-65274-R, RTI2018-097195-B-I00 and PCIN-2017-106.