@article{Liu2025, 
author = {Tianyi Liu and Shufen Zhang and Suli Wu},
title = {Synthesis of ZnS@QDs microparticles with synchronized scattering color-luminescence in submicron size: Application for direct-ink-writing PUF labels decoding by few-shot learning},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {12},
pages = {94908189},
keywords = {luminescence, structural color, ZnS@QDs microparticles, weak interaction, few-shot machine learning},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94908189},
doi = {10.26599/NR.2025.94908189},
abstract = {The combination of high-precision structural color and luminescence was an effective way to improve encoding capacity. Hence, we synthesized ZnS@QDs (QDs = quantum dots) microparticles exhibiting synchronized size-dependent Mie scattering and tunable luminescence in submicron size. The loading of QDs onto ZnS microspheres not only enables uniform dispersion to prevent quenching but also enhances luminescence through Mie scattering. Macroscopic structural color patterns were fabricated by direct-ink-writing with ZnS microspheres suspension as ink, while the dots formed by ZnS@QDs microspheres were inserted randomly. Meanwhile, the stochastic distribution of microparticles during ink evaporation generated physically unclonable function (PUF) codes with a capacity up to 22500 within areas of 50 μm × 50 μm. ResNet-based few-shot machine learning achieved 100% authentication accuracy with a minimal training set (21 samples). At the same time, open large language models (DeepSeek) enable rapid consistency assessments. In addition, the labels demonstrate long-term stability (> 6 months), customizable colors, and compatibility with flexible substrates, offering potential applications.}
}