“…As most common consumer products contain plastic parts, we implemented the proposed material biometrics approach by tagging plastic filaments with an NQR-active compound, namely sodium chlorate ( ). Note that the tagged product need not contain only plastic parts; the inclusion of metal parts does not significantly affect the amplitude, other features, or signal-to-noise ratio (SNR) of the measured NQR signatures 13 , 14 . Figure 2 Spin echoes from pulsed NQR measurements using the spin-locked spin echo (SLSE) sequence 15 are collected, filtered, and fitted to exponential decay curves.…”
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 $$^{35}$$
35
Cl nuclear quadrupole resonance (NQR) spectroscopy.
“…As most common consumer products contain plastic parts, we implemented the proposed material biometrics approach by tagging plastic filaments with an NQR-active compound, namely sodium chlorate ( ). Note that the tagged product need not contain only plastic parts; the inclusion of metal parts does not significantly affect the amplitude, other features, or signal-to-noise ratio (SNR) of the measured NQR signatures 13 , 14 . Figure 2 Spin echoes from pulsed NQR measurements using the spin-locked spin echo (SLSE) sequence 15 are collected, filtered, and fitted to exponential decay curves.…”
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 $$^{35}$$
35
Cl nuclear quadrupole resonance (NQR) spectroscopy.
“…The very low excitation energies of nuclear resonances (even on the scale of the superconducting gap) provide a window into dynamical effects. Further, nuclear resonances have a plethora of practical applications, ranging from medical (MRI) and pharmaceutical [4][5][6] to detecting prohibited substances [7][8][9] and combatting terrorism [10][11][12][13] .…”
The deviation of the electron density around the nuclei from spherical symmetry determines the electric field gradient (EFG), which can be measured by various types of spectroscopy. Nuclear Quadrupole Resonance (NQR) is particularly sensitive to the EFG. The EFGs, and by implication NQR frequencies, vary dramatically across materials. Consequently, searching for NQR spectral lines in previously uninvestigated materials represents a major challenge. Calculated EFGs can significantly aid at the search’s inception. To facilitate this task, we have applied high-throughput density functional theory calculations to predict EFGs for 15187 materials in the JARVIS-DFT database. This database, which will include EFG as a standard entry, is continuously increasing. Given the large scope of the database, it is impractical to verify each calculation. However, we assess accuracy by singling out cases for which reliable experimental information is readily available and compare them to the calculations. We further present a statistical analysis of the results. The database and tools associated with our work are made publicly available by JARVIS-DFT (https://www.ctcms.nist.gov/~knc6/JVASP.html) and NIST-JARVIS API (http://jarvis.nist.gov/).
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