Non-invasive authentication of mail packages using nuclear quadrupole resonance spectroscopy.

The international postal network is one of the most widely used methods for correspondence throughout the world. Most postal traffic across the globe consists of legitimate interpersonal, business-consumer, and business-business communications. However, the global postal system is also utilized for criminal activity. In particular, it is often utilized to ship and distribute contraband, including illegal psychoactive drugs such as fentanyl and heroin, to consumers. Existing technological solutions are capable of identifying synthetic opioids and other illegal drugs within packages, but are accompanied by several disadvantages that make them unsuitable for large-scale authentication of international mail traffic. This paper presents a novel method for non-invasive authentication of mail packages that overcomes these challenges. The approach uses nuclear quadrupole resonance (NQR) spectroscopy to detect and quantify the presence of known active pharmaceutical ingredients (APIs) within the package. It has been experimentally demonstrated using a bench top prototype. Test results from a variety of package types demonstrate the effectiveness of the proposed authentication approach.

A smart mask for active defense against airborne pathogens.

Face masks are a primary preventive measure against airborne pathogens. Thus, they have become one of the keys to controlling the spread of the COVID-19 virus. Common examples, including N95 masks, surgical masks, and face coverings, are passive devices that minimize the spread of suspended pathogens by inserting an aerosol-filtering barrier between the user's nasal and oral cavities and the environment. However, the filtering process does not adapt to changing pathogen levels or other environmental factors, which reduces its effectiveness in real-world scenarios. This paper addresses the limitations of passive masks by proposing ADAPT, a smart IoT-enabled "active mask". This wearable device contains a real-time closed-loop control system that senses airborne particles of different sizes near the mask by using an on-board particulate matter (PM) sensor. It then intelligently mitigates the threat by using mist spray, generated by a piezoelectric actuator, to load nearby aerosol particles such that they rapidly fall to the ground. The system is controlled by an on-board micro-controller unit that collects sensor data, analyzes it, and activates the mist generator as necessary. A custom smartphone application enables the user to remotely control the device and also receive real-time alerts related to recharging, refilling, and/or decontamination of the mask before reuse. Experimental results on a working prototype confirm that aerosol clouds rapidly fall to the ground when the mask is activated, thus significantly reducing PM counts near the user. Also, usage of the mask significantly increases local relative humidity levels.

Detecting Dye-Contaminated Vegetables Using Low-Field NMR Relaxometry.

Dyeing vegetables with harmful compounds has become an alarming public health issue over the past few years. Excessive consumption of these dyed vegetables can cause severe health hazards, including cancer. Copper sulfate, malachite green, and Sudan red are some of the non-food-grade dyes widely used on vegetables by untrusted entities in the food supply chain to make them look fresh and vibrant. In this study, the presence and quantity of dye-based adulteration in vegetables are determined by applying 1H-nuclear magnetic resonance (NMR) relaxometry. The proposed technique was validated by treating some vegetables in-house with different dyes and then soaking them in various solvents. The resulting solutions were collected and analyzed using NMR relaxometry. Specifically, the effective transverse relaxation time constant, T2,eff, of each solution was estimated using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. Finally, the estimated time constants (i.e., measured signatures) were compared with a library of existing T2,eff data to detect and quantify the presence of unwanted dyes. The latter consists of data-driven models of transverse decay times for various concentrations of each water-soluble dye. The time required to analyze each sample using the proposed approach is dye-dependent but typically no longer than a few minutes. The analysis results can be used to generate warning flags if the detected dye concentrations violate widely accepted standards for food dyes. The proposed low-cost detection approach can be used in various stages of a produce supply chain, including consumer household.

NQR sensitive embedded signatures for authenticating additively manufactured objects.

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.

Single-shot spatially-localized NQR using field-dependent relaxation rates.

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.