Masna Naren Vikram Raj, Huan Junjun, Mandal Soumyajit, Bhunia Swarup
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Sci Rep. 2021 Jun 9;11(1):12207. doi: 10.1038/s41598-021-91531-6.
Automatic recognition of unique characteristics of an object can provide a powerful solution to verify its authenticity and safety. It can mitigate the growth of one of the largest underground industries-that of counterfeit goods-flowing through the global supply chain. In this article, we propose the novel concept of material biometrics, in which the intrinsic chemical properties of structural materials are used to generate unique identifiers for authenticating individual products. For this purpose, the objects to be protected are modified via programmable additive manufacturing of built-in chemical "tags" that generate signatures depending on their chemical composition, quantity, and location. We report a material biometrics-enabled manufacturing flow in which plastic objects are protected using spatially-distributed tags that are optically invisible and difficult to clone. The resulting multi-bit signatures have high entropy and can be non-invasively detected for product authentication using [Formula: see text]Cl nuclear quadrupole resonance (NQR) spectroscopy.
自动识别物体的独特特征可为验证其真实性和安全性提供强有力的解决方案。它可以抑制全球供应链中最大的地下产业之一——假冒商品的增长。在本文中,我们提出了材料生物识别这一新颖概念,即利用结构材料的固有化学性质生成唯一标识符,以对单个产品进行认证。为此,通过可编程增材制造内置化学“标签”来修改要保护的物体,这些标签根据其化学成分、数量和位置生成签名。我们报告了一种基于材料生物识别的制造流程,其中使用光学不可见且难以克隆的空间分布标签来保护塑料制品。由此产生的多位签名具有高熵,并且可以使用³⁵Cl核四极共振(NQR)光谱进行非侵入式检测,以进行产品认证。