Department of Chemistry, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States.
Department of Computer Science, Indiana University, 700 N. Woodlawn Avenue, Bloomington, Indiana 47408, United States.
ACS Nano. 2021 Feb 23;15(2):2901-2910. doi: 10.1021/acsnano.0c08974. Epub 2021 Feb 9.
Counterfeit goods create significant economic losses and product failures in many industries. Here, we report a covert anticounterfeit platform where plasmonic nanoparticles (NPs) create physically unclonable functions (PUFs) with high encoding capacity. By allowing anisotropic Au NPs of different sizes to deposit randomly, a diversity of surfaces can be facilely tagged with NP deposits that serve as PUFs and are analyzed using optical microscopy. High encoding capacity is engineered into the tags by the sizes of the Au NPs, which provide a range of color responses, while their anisotropy provides sensitivity to light polarization. An estimated encoding capacity of 270 is achieved, which is one of the highest reported to date. Authentication of the tags with deep machine learning allows for high accuracy and rapid matching of a tag to a specific product. Moreover, the tags contain descriptive metadata that is leveraged to match a tag to a specific lot number (, a collection of tags created in the same manner from the same formulation of anisotropic Au NPs). Overall, integration of designer plasmonic NPs with deep machine learning methods can create a rapidly authenticated anticounterfeit platform with high encoding capacity.
假冒商品在许多行业造成了巨大的经济损失和产品故障。在这里，我们报告了一个隐蔽的防伪平台，其中等离子体纳米粒子（NPs）具有高编码能力的物理不可克隆功能（PUFs）。通过允许不同尺寸的各向异性 Au NPs 随机沉积，可以轻松地用 NP 沉积物标记各种表面，这些沉积物用作 PUFs 并使用光学显微镜进行分析。Au NPs 的尺寸为标签设计了高编码能力，提供了一系列颜色响应，而其各向异性则为光偏振提供了敏感性。实现了估计为 270 的编码容量，这是迄今为止报道的最高容量之一。通过深度学习对标签进行认证，可以实现高准确性和快速匹配标签到特定产品。此外，标签包含描述性元数据，可用于将标签与特定批号匹配（即，从相同配方的各向异性 Au NPs 以相同方式创建的一组标签）。总的来说，将设计的等离子体 NPs 与深度学习方法集成可以创建具有高编码容量的快速认证防伪平台。
