This PDF file contains the front matter associated with SPIE Proceedings Volume 13478, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The remote detection of symmetric diatomic gases, like hydrogen H2 and chlorine Cl2, is challenging for passive hyperspectral sensors that use infrared vibrational spectroscopy. Typically, Raman technology with ultraviolet (UV) excitation is employed for such detection . Due to differing UV absorption spectra, H2 and Cl2 exhibit distinct UV Raman responses. This work presents UV Raman studies of both gases, focusing first on contrasting modeling aspects and results, then comparing experimental data collected from distances up to 100 meters.

Spectrum Photonics has developed a low size, weight and power (SWaP) long wave infrared (LWIR) hyperspectral imaging (HSI) sensor for deployment on Group 2 small unmanned aerial systems (UAS). The sensor has been shown to be effective for standoff chemical detection applications in operationally-relevant environments. This airborne miniature Hyperspectral Chemical Airborne Reconnaissance Sensor (HyCARS-Mini) generates LWIR hyperspectral data-products using on-board processing and transmits detection products in real time to the ground station. Leveraging uncooled microbolometers and Spectrum Photonics’ spatial interferometer-based HSI technology, the HyCARS-Mini meets the requisite SWaP profile to enable small UAS integration. Results from our Group 2 UAS-borne demonstrations are reported.

Non-destructive forensic multimodal analysis of unknown objects, industrial components, investigation of industrial accidents and explosions directly in situ is still a major challenge. A prototype device for multimodal robotic analysis, imaging and mapping of 3D objects is being tested for the aforementioned areas. The system integrates imaging and analytical technologies onto six-axis robotic arms and allows non-destructive examination of wide spectrum of samples with complicated curvatures. The new generation of X-ray imaging detectors provides a high picture quality with a spatial resolution level in the micrometer range in 2D or 3D imaging. Extension modality is XRD phase mapping, XRF analyses, and VIS, VNIR, SWIR and UV modules. The use of system is rather extensive—tools and marks examination, defectoscopy and metallography and other forensic and security areas A fully autonomous version will be possible in the future.

Within the European Union, the regulation on precursors for explosive manufacturing is a tool to minimize the availability of chemicals used for production of improvised explosives. As a consequence, prosecution of individuals possessing the stipulated illicit chemicals is possible. Four of the compounds on this list of regulated precursors are potassium chlorate and perchlorate as well as sodium chlorate and perchlorate which are restricted for public use in concentrations above 40 wt-% (including mixtures of these materials). Therefore, methods that can provide initial results early on in the forensic process on the content of chlorate and/or perchlorate would prove valuable in the decision process and in the event of prosecution. The focus of this study is to investigate methods using Raman spectroscopy for provisional quantification of chlorate and perchlorate salts in aqueous media (to facilitate a homogeneous sample). Two methods were investigated; i) explored the possibilities of selective reduction of chlorate in mixtures of chlorate and perchlorate. From this method it was found that chlorate could be removed from perchlorate but that the strong acidic medium affected the spectral features of the water O-H bend (herein used as a constant internal standard) making the method complex. ii) used linear combinations of fractions of chlorate and perchlorate spectra at different concentrations, which generated synthesized spectra that were correlated to a measured spectrum of a chlorate-perchlorate mixture. It was found that the latter method was able to quantify the total chlorate and perchlorate concentrations in a range within 9% of the actual value. The proposed method could easily be implemented in a ”field forward” (e.g., regional forensic laboratories or border control sites) forensic context.

A dual-comb spectrometer was adapted to provide a new sensing modality: operation in stationary mode with an order of magnitude higher sensitivity compared with previous reports with the same technology. 1,3,5-trinitro-1,3,5-triazine (RDX) and Pentaerythritol tetranitrate (PETN) on metal and plastic surfaces with the mass loading of ⁓125 ng/cm² were detected. These limits of detection were achieved with ⁓ 6 s interrogation time between the sample and the laser beam, which also includes data processing time. This introduces options for the system to be used for spot checks of objects on a conveyor belt, where the laser beam from the spectrometer follows the target using the machine vision technology. The latter was tested for use with belt buckles. Identification and laser beam positioning on a target takes ⁓ 20 ms, which is enough for real-time operation.

The terahertz (THz) band in the electromagnetic spectrum is highly useful for material identification due to the unique absorption signatures of various materials. Traditionally, such analyses are conducted in laboratories, requiring materials to be transported from the field, which is time-consuming and cumbersome. To address this, a handheld device has been developed for on-field detection of materials through THz absorption spectra. The device operates by capturing back-reflected THz waves using a time-domain spectroscopy system, allowing for non-invasive, real-time spectral sensing. A protocol for analyzing the captured THz signals has also been presented. The sensor was tested on nitrogen-based materials hidden beneath barriers like paper and fabric, proving its effectiveness. This portable THz sensor has promising applications in areas such as security screening, chemical identification, and aerospace technology, offering efficient and immediate material detection capabilities.

In order to detect either aerosol or gaseous effluents at standoff distances, modern spectroscopic methods can use infrared light reflected from remote surfaces to detect the intervening species. The probing light can be either broadband or (nearly) monochromatic such as from a laser. How much light is returned to the sensor, either in a specular or diffuse fashion, depends not only on the output power of the source but also on the spectral properties of the reflective surface. In this paper, we report details on measurement of Surfaces of Opportunity (SOOs) used as part of the PICARD (Pursuing Intelligent Complex Aerosols for Rapid Detection) aerosol research program. The materials included surfaces of both high- and low-reflectivity, including e.g. aluminum plates, car body panels, marble, painted brick, etc. Both the total (diffuse + specular) and diffuse-only reflectance spectra from 7500–600 cm^-1 (1.33–16.67 μm) were measured in the laboratory to investigate the possibility of their use as SOOs for standoff detection.

Chemical warfare agents continue to be an ongoing threat and their properties and behavior under a variety of conditions need to be investigated and better understood. Dimethyl methylphosphonate (DMMP) is a widely used simulant that is being investigated to help understand the properties of CWAs. The vast majority of reported work on DMMP has focused on the heterogeneous decomposition of DMMP while it is interacting with oxides or metals, with some reports including the effects of photoirradiation/photocatalysis. There is surprisingly little research work on the homogeneous thermal decomposition of DMMP. We report here on decomposition measurements of DMMP in nitrogen and air at temperatures between 200 °C and 275 °C. While we observe decomposition to occur, the temperature-dependent rate changes in nitrogen appear to be relatively small, resulting in a relatively small activation energy. In air, our initial results do not provide a clear picture—at lower temperatures, the rates do not appear to change much, but at 275 °C we do see a significant increase in the rate. These initial results could indicate that at lower temperatures the observed decomposition may be dominated by non-thermal effects such as catalysis. However, additional work is needed to verify this hypothesis.

Using in situ kinetic diffuse reflectance infrared Fourier transform spectroscopy (k-DRIFTS), we characterized the photodegradation of the vapor-phase chemical warfare agent simulant dimethyl methylphosphonate (DMMP) on anhydrous and natural kaolinite, one of the most abundant minerals and component of atmospheric dust. We compare these results with those of DMMP on titania, which has been widely reported in the literature, and find that photocatalysis of DMMP on the kaolinite samples is almost nonexistent. As such, dust particles exposed to chemical warfare agents must be assumed to pose an ongoing threat.

Stimulated Raman scattering (SRS) is advantageous for in vivo diagnostic imaging due to its non-destructive, label-free, and chemically selective nature. It can significantly enhance the signal-to-noise ratio compared to the conventional spontaneous Raman scattering process, enabling fast acquisition of Raman signals for hyperspectral imaging. While it is used in various medical fields, such as cancer diagnostics, stain-free histopathology, and pharmaceutical research, its application outside clinical settings or laboratories is limited due to the complexity of the required light source. This study focuses on the development of a portable SRS system based on a tunable dual-output fiber-based light source which can be used not only for medical imaging but also for proximal standoff detection of chemicals and explosives. In order to determine the fiber laser design requirements, we set up a benchtop SRS system using a commercial free-space tunable dual-wavelength laser and experimentally analyze the laser-related factors influencing SRS signal generation, such as wavelength tunability, output power, power ratio of the two incident beams, spectral bandwidth, and pulse duration. Additionally, we evaluate the factors affecting the sensitivity and reproducibility of SRS detection, including the ratio of pump-Stokes beams and the distance between the sample and the detector. Based on the parametric study of SRS detection with the benchtop SRS system, we have determined the design parameters of the new fiber-based SRS source where the broadband pump beam is produced through supercontinuum generation for fast hyperspectral SRS imaging.

The effects of light scattering and refraction make it challenging to identify aerosolized chemicals using traditional spectral methods and reference libraries. Due to an infinite number of aerosol sizes, shapes and compositions, constructing a database of laboratory-measured reference spectra is not feasible. As an alternative approach, the measured wavelength-dependent n/k can be used in combination with photon absorption / scattering theory and the Beer-Lambert law to generate a series of synthetic infrared transmission / scattered light spectra. These synthetic spectra show that aerosol particle spectral signatures, for either transmission or scattering measurements, have distinct overall shapes as well as shifted peak positions and amplitudes compared to the reference data from bulk transmission measurements. To validate our synthetic signatures based on the derived n/k values, well-characterized aerosols of dioctyl sebacate are generated, and the spectral transmittance data are recorded for comparison.

A prescreener identifies an alarm location that may indicate a potential underground threat for further interrogation and analysis. It can reduce the searching time over a region, increase the rate of advance, and minimize data processing requirements. Prescreening using the hyperbolic signature of an underground object observed by a ground penetrating radar (GPR) is a widely used approach, and various feature-based techniques for detecting hyperbolic shapes have been proposed and developed over time. This work exploits the machine learning approach to extract the features automatically from a GPR image for prescreening, with a design optimized to run in real time.

Raman spectroscopy, whilst exhibiting exceptional molecular specificity, is perceived to be limited to proximal applications due to the weakness of the Raman scattering effect. By combining careful optical design, deep-UV laser technology, spectroscopic techniques and time-correlated, single-photon counting (TCSPC) techniques, the effective range over which airborne molecules can be detected is extended to many tens of metres, and the time-of-flight aspect of TCSPC facilitates location of the substance, as well as its identification. We have previously used this technique for the effective stand-off detection of hydrogen for the nuclear and energy sectors, and in this paper we will show how it can be expanded for any molecule which exhibits a characteristic Raman spectrum. This allows us to move towards a universal airborne stand-off detection tool over the ~100m range.

Real-time chemical detection is critical in many contexts, including national security and public safety. Machine learning has emerged as a valuable tool to support real-time chemical detection. However, procuring the large datasets necessary for training Machine Learning (ML) algorithms can be prohibitively time-consuming and costly. Data requirements and associated costs are compounded in the presence of multiple chemical sensors, each of which must be represented by sufficient training data for the ML algorithm to learn from. In such limited data scenarios, a common approach is to augment an existing dataset of experimentally acquired data with synthetic data generated by machine learning models. Herein, we explore the generation of synthetic data which can enhance the performance of downstream chemical detection models. Our research focuses on leveraging synthetic data generation when one is utilizing multiple chemical sensors. Specifically, we explore how data from one sensor modality can be used to support data generation for a different sensor modality. While each sensor captures distinct features, such as charge or fragmentation patterns, none provide complete chemical structure. As a result, data captured by one sensor may not be sufficient to generate data for another. Our approach revolves around mapping data to a fixed, information rich embedding that is based upon chemical structure and thus is common to all sensor types. These fixed embeddings, derived from an external deep learning chemistry model, capture information regarding chemical structure which is advantageous for generating data for various sensor modalities. We build upon our previous research, in which we successfully utilized our fixed embedding architecture to generate synthetic mass spectrometry data. The synthetic data created by our previous model both enhanced classifier accuracy and was correctly identified by an external spectrum matching tool. In this study, we expand our prior work to ion mobility data, laying the foundation for a larger data generation and sensor fusion model.

Physical Sciences Inc. (PSI) has developed a Raman sensor—ClearShot—for the rapid non-contact detection of chemical targets in containers. The system leverages fundamental light scattering phenomenology and combines PSI’s high throughput Spatial Heterodyne Spectrometer with a machine learning detection algorithm to provide end-users the ability to interrogate materials in bottles, bags, etc., without having to come into physical contact with a suspected hazard. The system provides two modes of interrogation—through the wall of a closed container, or through the mouth of an opened container. Suspected chemicals in plastic or glass containers (bags or bottles) can be interrogated directly through the container material. Metal or ceramic containers can be screened by simply opening the lid and interrogating the contents directly through the container’s mouth. The system can accept bottles ranging from 30 mL to 2 L in volume (up to 13” in height) and provides the ability to interrogate partially filled containers. A detection and identification decision is provided in 20 s. The system has been tested against a large number of chemical hazards and their simulants. A description of the sensor and its capabilities as well as the results from a large test campaign are provided here.

Alakai Defense Systems has recently developed what we believe is the first lightweight UV Raman sensor for standoff trace detection of chemicals, which we refer to as Argos. Due to the small size and weight, Alakai has been able to deploy Argos on several Robotic platforms, such as Unmanned Ground Vehicles (UGV’s) and Unmanned Aerial Vehicles (UAV’s). Alakai will preset data on the detection performance of Argos along with videos of the Argos sensor deployed on UGV’s and UAV’s. We believe this standoff chemical detection from a UAV is a new capability which will allow replacement of the larger and longer-range Raman systems.

Raman spectroscopy is a non-destructive analytical technique that has become invaluable in the pharmaceutical industry for the analysis of tablets. It works by shining a laser on the tablet and analyzing the inelastically scattered light, which provides a molecular fingerprint unique to the compound under investigation. Its ability to provide rapid, precise measurements without the need for sample preparation makes it an essential tool for quality control and assurance in drug production and testing. Traditional challenges include a bias towards surface materials due to the laser's penetration depth. Addressing this, HORC has innovated a unique microscope objective that produces collimated laser light and captures Raman backscatter devoid of focusing effects. This objective achieves comprehensive sampling by gathering both surface backscatter and internal Raman photons. A gold-plated holder featuring an integration sphere significantly amplifies the signal. The optic's efficacy, including its self-calibration capability and representative sampling claim, was assessed. An evaluation of this optic was conducted to determine the effectiveness of the self-calibration feature of the calcium fluoride (CaF₂) optic as well as the claims of representative sampling. Mixtures of acetaminophen and caffeine were used to explore quantitative analysis of pharmaceutical formulations, and potential applications of detecting adulterants or minor components for forensic applications.

The technology of active long-wave-infrared (LWIR) hyperspectral imaging (HSI) has been demonstrated to be a powerful method for the standoff, non-contact detection of trace chemicals on surfaces with many promising applications in contamination avoidance and checkpoint screening. The combination of LWIR spectroscopy and imaging effectively enables not only the ability to identify chemical contaminants, but to spatially localize them on the target as well. However, practical applications of this technology have hitherto been limited by the need to take reference measurements on spectrally neutral targets whenever the measurement parameters (e.g., standoff distance) are changed. To obviate this requirement, we have developed a technique, referred to here as “auto-referencing”, to create synthetic reference measurements from calibration measurements, enabling the use of LWIR HSI system as a “point and shoot” standoff trace chemical detector. Data obtained with auto-referencing is shown here to have higher signal-to-noise ratios than that obtained with manual referencing and regions of chemical contamination can be automatically identified with mapping algorithms for a variety of common surfaces. Finally, we demonstrate how the scan parameters can be dynamically changed to enable faster mapping of surfaces without loss in detection performance.

Our laser-based standoff detection system is designed to detect traces of explosives on peoples shoes during security checks. It uses mid-infrared and Raman spectroscopy in combination with image recognition and tracking to examine shoe surfaces. Fast and contactless detection together with real-time data analysis provide an easy and comfortable screening process. Here, we investigate the system sensitivity for the explosives pentaerythritol tetranitrate (PETN), hexogen (RDX), trinitrotoluene (TNT) and ammoniumnitrate (AN) on quartz surfaces. The results represent a first step towards determining the detection limit of our system. The benefit of using multiple detection techniques instead of only one method is discussed.

The current chemical, biological, radiological, nuclear and explosive (CBRNE) security environment presents emerging risks which require an innovative approach to address. Within the radiological-nuclear (RN) domain specifically, Canada’s Centre for Security Science has been developing new techniques that could significantly enhance radiation detection and identification, bridge extant gaps, ensure robust RN security and rapid responses to evolving challenges. An overview of recent work to develop such capabilities will be presented here.

Precise detection of radioactive materials is crucial for ensuring safety and identifying potential hazards. Several researchers have explored nuclear exploration using UAVs. Employing UAVs allows us to explore vast areas while avoiding radiation exposure for operators. Such exploration requires knowledge of the sensitivity of detectors to find the location of the radiation source with certainty. It depends on several factors like the activity of the source, the speed of the detector, and its distance from the source. This paper provides guidance on source localization using a drone-mounted detector where speed, distance, and source/detector characteristics are studied to define source position and activity based on given uncertainty limits. The findings from this study have significant implications for optimizing UAV flight paths and improving the precision of nuclear source localization in large, hazardous areas.

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

If humans desire to leave the safety of Earth and explore extreme environments, cost effective and low mass health sensors will be essential to monitor incident ionizing radiation, temperatures, or impacts as they travel. To ensure the safety of the astronauts, a luminescent material-based sensor might be used to provide reliable in-situ radiation monitoring, measure surface temperature, or detect impact for spacecraft. For the last fifteen years, an extensive research program has been completed many luminescent materials have been irradiated with protons and electrons. Results have generally shown that charged particle irradiation reduces the intensity of emitted luminescence by producing quenching centers. Related research has also shown that the luminescence properties for some of the same materials will change as a function of temperature. Calibrating this change can allow surface temperature to be measured without electrical contact. Finally, other research has shown that impacts can be detected using triboluminescence, which is the emission of copious light when a material is crushed or struck. This paper gives an overview of recent research designed to measure the effects of alpha particle irradiation on a selection of phosphors potentially used in future alpha-photovoltaic devices. Emphasis will be placed on comparing these results with earlier research on similar phosphors irradiated with protons.

There is a critical need for sensors that can provide rapid detection and identification of biological threats in the field. Currently established approaches (cell cultures, polymerase chain reactions, mass spectrometry) are time consuming, susceptible to contamination, or not amenable to field use. Physical Sciences Inc. in collaboration with iRiS Kinetics is developing a compact, portable and fieldable sensor for the rapid detection and presumptive identification of biothreats. The technology—termed Interferometric Reflectance Imaging Sensor for Biothreat Detection (IRIS-BD)—is a microfluidics-based lab-on-chip immunoassay and can screen for the detection and presumptive identification of bacterial, viral and toxin threats. The system employs three key components—an antibody conjugated to single stranded deoxyribonucleic acid (DNA), a complementary DNA functionalized sensing chip that interferometrically enhances the imaging contrast of captured particles, and an optical chip reader. The sensor can screen for up to 5 agents in a single assay and provide a detection and presumptive identification decision in <2 hours. The interferometric imaging optical approach is capable of detecting a single captured biothreat particle on the sensing chip. This ability gives IRIS-BD the potential to have comparable specificity to lateral flow immunoassays but with 50–1000× improved detection capabilities. A description of the sensor architecture and initial results on testing against biothreat simulants is presented here.

Infrared absorption spectra are calculated using density function theory (DFT), for isolated molecules of a set of chemical warfare agents. This study demonstrates using DFT for characterizing IR-spectral features of substances whose detection is of interest. DFT calculated absorption spectra of isolated molecules represent quantitative estimates that can be correlated with additional information obtained from laboratory measurements. The DFT software GAUSSIAN was used for calculating the infrared (IR) spectra presented here. DFT calculated spectra can be used to construct spectrum templates, for spectral-feature comparison, for detection of target materials.

In-situ and instantaneous detection of aerosolized chemical matter is paramount to swiftly and accurately counter terrorism acts, industrial accidents, and environmental disasters. Aerosol chemical threats pose a significant danger due to the extended range of distance these nanometers (nm) to micrometers (μm) scale particulates can travel with the wind vector without dilution. Detect-ION is developing a versatile chemical threat detection platform that would collect, detect, and identify such aerosolized matter ranging in size from 20 nm to 20 μm. The detector platform, called “SPECTRAL,” comprises three novel low-SWaP subsystems, namely an aerosol collection stage, a low-thermal-mass (LTM) gas chromatograph (GC), and a novel chip-scale mass spectrometer (MS) developed via prior IARPA funding, integrated together in a battery operated ⁓10-L form factor. Detect-ION will present the prototype development efforts, as well as the aerosol collection and detection performance for a broad range of chemical threat classes as defined by IARPA in the PICARD program.

The scientific community has carried out extensive research on the integration of unmanned aerial vehicle (UAV) integrated with various chemical sensors for remote and real-time detection of toxic gases. In South Africa, studies on this topic are at its early stage and research publications is very low according to our knowledge. Hence, literature in the past five years was reviewed to identify current research papers, research trends, progress and limitations. Literature published from 2020 to 2024, have demonstrated the successful integration of the different UAV platforms and chemical sensors to confirm the feasibility of developing a remote and real-time chemical detection system prototype. From the results, it was concluded that the integration of a chemical sensor and UAV is feasible and the application of this technology under a real environment is still a challenge. The main problem that affects the use of this detection technology in practical application is the aerodynamics issues such as the downwash wind generated by the spinning rotors of the UAVs. Future research studies should provide solutions for the reduction of the downwash effect generated by the UAV.

When the ground penetrating radar (GPR) sensor is attached to a drone for searching subsurface objects, knowledge about the position of the drone is crucial to ensure the regularity/uniformity of the data in forming an image for detection. A drone's flight path will involve many turns, velocity variations, and direction changes to cover the ground region of interest. This paper first introduces two possible flight paths and then investigates two different dynamic models for use with the Kalman filter in tracking a drone while scanning the ground. The two models are the linear motion constant velocity model and the linear motion constant acceleration model. The effectiveness of these models in tracking a drone with the two flight path patterns will be contrasted and evaluated.

Ammonia (NH3) is a fundamental chemical building block with toxicity and corrosiveness. The detection of small amount of NH3 is one of the most challenging issues for various applications. In this study, we demonstrate a flexible NH3 gas sensor based on copper bromide (CuBr) film, which is prepared by using low-temperature and vacuum-free processes. The device showed not only sensitive and selective NH3 gas sensing properties, but also good mechanical flexibility without significant degradation during the bending test, suggesting the potential as flexible and wearable applications.

Traditional optical methods face challenges in sensitivity and spectral selectivity, which are crucial for detecting and distinguishing weak absorption signatures in complex backgrounds. The development of advanced optical remote sensing technologies for detecting illegal drugs and Home Made Explosives (HMEs) production facilities hinges on understanding their unique chemical and spectral signatures.