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Dynamic Multicolor Upconversion Through Excitation Pulse Duration Modulation toward Machine Learning Assisted Optical Encryption
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DOI：10.1002/lpor.202500465.png)
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Abstract
En 中文
Optical encryption technology demonstrates significant potential for advanced information security applications due to its inherent advantages in high-speed operation, multidimensional processing, and parallel computation capabilities. However, current research in this field has predominantly focused on elementary optical anti-counterfeiting techniques and binary coding systems, with limited exploration of sophisticated encryption methodologies. In this study, a novel strategy is presented that employs NaYF4 multilayer core-shell nanocrystals that enable dynamic full-color upconversion (UC) emission modulation under single-wavelength excitation, thereby facilitating high-capacity optical encryption through machine learning (ML)-assisted processing. Through systematic investigation of UC photophysical mechanisms, it is revealed that the full-color tunability originates from both excitation power dependence and excitation pulse width sensitivity mediated by rare earth ion cross-relaxation processes. The rich optical information generated through these mechanisms has been systematically organized into a comprehensive ML-constructed database, functioning as an optical codebook for encryption protocols. The developed ML framework demonstrates exceptional capability in identifying subtle optical signature differences, achieving over 98% recognition accuracy through database pattern matching. This system theoretically enables the encryption and decryption of 188 distinct optical encryption patterns. These findings establish a new paradigm for optical encryption development and provide critical insights for advancing next-generation optical security systems.
Keywords:
cross relaxation
dynamic color-switchable emissions
machine learning
optical encryption
Journal
L
IF:
10
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3.7K
Citations:
2.1W