Modular Anti-Counterfeit Tags Formed by Template-Assisted Self-Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning.

Counterfeit goods are pervasive, being found in products as diverse as textiles and optical media to pharmaceuticals and sensitive electronics. Here, an anti-counterfeit platform is reported in which plasmonic nanoparticles (NPs) are used to create unique image tags that can be authenticated quickly and reliably. Specifically, plasmonic NPs are assembled into periodic arrays of NP clusters by template-assisted self-assembly (TASA), where the light scattering responses from the arrays are analyzed by dark-field optical microscopy. Tag design proved modular as plasmonic NPs with different optical responses can be selected and paired with Templates with different features (e.g., well size, well shape, and number and arrangement of wells in an array), giving access to a variety of color responses and unique images. These images can be differentiated from one another and authenticated by image analysis. Authentication methods based on shallow and deep neural networks are compared, where deep neural networks authenticated TASA tags with higher accuracy. Given the ease of tag fabrication and rapid image analysis, these platforms are ideal for on-the-fly tagging and supply-chain authentication of critical goods.