To protect STCPs we must think checkpoints and beyond; in many scenarios, checkpoints are neither possible nor desirable. Currently, densely populated venues are often patrolled by canines which are trained to selectively detect and locate threats, but dogs require care, feeding, and continuous training. Furthermore, they are not available 24 hours a day; and it is difficult to tell when they are “on.” Our goal is to address this major gap in DHS’s detection capabilities to create systems that can be “on” without tiring and can track a threat signature to its source much like a trained canine. To achieve this, three novel vapor sensors are proposed. The first, called Digital Dog Nose (DDN), uses an array of thermodynamic sensors that can be flown to the plume source, i.e., drone-mounted, or discreetly hidden throughout a venue. The second and third use quantum cascade laser (QCL), midinfrared (MIR) laser spectroscopy, and a reflected light telescope, all aimed at the vapor plume. These novel sensors will not simply replace the canine and handler; they will be more efficient in finding and communicating the threat. Data will inform additional advanced sensing technology projects to iteratively improve the models.
Digital Dog Nose (DDN)
First responders, the military, and law enforcement have a growing need for an ultrasensitive, selective chemical sensor platform that cannot only identify potential threats but their locations. These threats include Improvised Explosive Devices (IEDs) and the energetic materials comprising them, as well as soft threats such as commonly used gun oils. We have identified over a dozen of these soft threats that indicate people may be carrying firearms, especially automatic weapons, into schools, malls, churches, and other venues where mass shootings can and have occurred (e.g. a grade school in Uvalde, Texas; a grocery store in Buffalo, NY). Shootings can be deterred by detection. At a Nordstrom department store, a “would-be” gunman was stopped from carrying out an armed robbery by a “gun sniffing” dog.
Having the ability to “sniff” a person or persons in a non-invasive way for potential threats, such as gun oils, could save lives. To that end, we have developed a “fingerprint” for a number of common gun oils (i.e. a unique signature that could be uniquely identified as such in the vapor phase). Not every gun oil is chemically the same, but most have a least one relatively high vapor pressure ingredient, which is ideal for the Digital Dog Nose (DDN) sensor. The DDN is comprised of a number of small thermodynamic sensors, each of which has a microheater coated with a metal oxide catalyst that is “tuned” to detect a specific vapor-phase analyte. In addition, a “bare” microheater is incorporated into the DDN that acts as a reference, such that any hydrodynamic or specific heat effects can be subtracted from the catalyst-coated microheater signals. Arrays of these sensors will provide the specificity to recognize and track a variety of threats in the vapor phase.
Several tasks that are essential to developing such a capability have already been initiated in Year 1 and are identified below:
- Determination of appropriate catalysts for a range of soft threats (i.e. gun oils)
- Demonstration that analytes (threats) can be identified in the presence of interferents such as nail polish remover, deodorants, perfumes, and toothpaste
- Determination of sensing range (distance)
- Relaying sensor information to the command center in real-time
Laser-Based Sensors (LaBS)
To further remove the need for checkpoints in crowded spaces, this project is also developing two laser-based sensors.
The first is a 24/7 continuously monitoring wide-area sensor capable of statically sampling the atmosphere surrounding STCPs using mid-infrared (MIR) lasers coupled to back-reflected light devices. The second is a dynamic MIR laser source 69 and detector mounted on unmanned ground/air vehicles used to confirm readings from close-range sensors. Both laser sensing schemes will be aimed at vapor plumes of chemical and biological threats (CBTs). MIR laser point sensing allows the user to detect threats using a non-invasive, eye-safe, real-time, and no-contact human-threat analysis technology that marks the upcoming security threat. Threats may be challenging to identify and locate, whether they be explosives (HE & HME), toxic industrial chemicals (TICs), insecticides/herbicides, narcotics, chem-bio threats (CBTs), etc., Developing unmanned vehicles mounted with chemical sensing platforms to identify the threat in real-time and locate its source will be an excellent tool for the DHS and defense agencies. The wide-area, continuous monitoring device will provide a detection vector to guide the unmanned vehicles toward the potential target. Back-reflection optical devices will be strategically located to cover as much head space as possible within the 0.1-1 km range (or >). These sensors require expanding their threat recognition libraries, training for detection at a distance under various conditions, and discriminating against interferents. This process will be assisted by multivariate analysis (MVA or Chemometrics) into the data analysis process and later in the project, the introduction of machine learning (ML) and artificial intelligence (AI) routines. Mounting the QCL/MCT on a UV and detecting threats in an open environment will be addressed in subsequent years.
