Abstract

Nuclear quadrupole resonance (NQR) is commonly used to characterize solid materials containing quadrupolar nuclei. For example, NQR is a promising technique for detecting plastic explosives and other forbidden substances as well as for authenticating pharmaceutical products. Spatially-resolved NQR measurements are of particular interest for enabling automated sample positioning, evaluation of sample heterogeneity, and chemometric authentication of objects. This paper proposes a rapid “single-shot” method for spatially-resolved NQR with the potential to benefit such applications. The proposed method takes advantage of the fact that certain NQR relaxation rates are field-dependent: the observed field dependence is used to convert relaxation time distributions measured in a static field gradient (estimated via Laplace inversion of time-domain data) into spatial distributions. The method was validated using 35Cl and 37Cl NQR of sodium chlorate and other compounds. Effective spatial resolution was also improved by using machine learning (ML) to classify the measured spatial distributions. In particular, experimental results demonstrate accurate ML-based classification of 3D-printed objects containing arbitrary binary distributions of sodium chlorate. Such distributions can thus be used as NQR-based “embedded barcodes” for authenticating high-value objects.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.