2026-06-02T10:37:45Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/392072026-02-03T11:05:30Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ramos-Terrón, Susana
author
De Miguel, Gustavo
author
Leinen, Dietmar
author
Munilla-Fajardo, Jorge
author
Romero-Gómez, Pablo
2025-07-02T10:23:30Z
2025-07-02T10:23:30Z
2025-06
Ramos-Terrón, S., De Miguel, G., Leinen, D., Munilla, J., & Romero-Gómez, P. (2025). Microscale image encoding on metal halide perovskite thin films for anti-counterfeiting applications. RSC advances, 15(24), 18768-18776.
https://hdl.handle.net/10630/39207
10.1039/d5ra00787a
Metal halide perovskite thin films are a demand in many technological areas since they fulfill the requirements expected in different applications, including solar cells and LEDs. In this article, we demonstrate for the first time that perovskite thin films have the potential to be incorporated in products as an anticounterfeit solution in self-identification methods due to their ability to generate images on its
surface with motifs at the microscale range. The images can be recorded using an optical microscope
with a camera and can be recognized automatically using a convolutional neural network. The surface
pattern of the thin films can be designed by modifying the perovskite composition, structure, and surface
strain making falsification difficult to reproduce and mimic. To this end, perovskite thin films have been
characterized using X-ray diffraction, UV-Vis spectroscopy and optical microscopy. We report a batch of
5 types of CsxFA1−xPbI3−xBrx perovskite thin films obtained by modifying the experimental conditions that
exhibit recognized patterns with accuracy detection of 94.2%.
eng
Atribución-NoComercial 4.0 Internacional
Ciencia de los materiales
Nanotecnología
Optica electrónica
Electrónica órganica
Perovskitas
Microscale image encoding on metal halide perovskite thin films for anti-counterfeiting applications
journal article
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URL
https://riuma.uma.es/bitstreams/bfd2ee43-d40b-411d-a729-f63e17bb7156/download
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1023715
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