Deep UV Raman spectroscopy has made significant strides from large lab-based systems to compact, highly ruggedized instruments being used for planetary exploration such the SHERLOC instrument on the Mars Perseverance rover. The combination of deep UV Raman and fluorescence spectroscopy has been particularly interesting as it offers a unique solution to rapidly search for targets of interest. This was previously demonstrated with biological detection using proximity and standoff deep UV fluorescence/Raman mapping devices. More recently we have been expanding this effort, to move into explosive detection. This talk will discuss the fundamentals of the detection methodology, the advantage of combining and collecting simultaneous deep UV Raman and fluorescence, and sensitivity of the new systems.

Explosives detectors are used to screen people, packages, and infrastructure, as well as conduct investigation of unknown substances. Optical detection techniques hold promise to enable stand-off detection of a range of threat materials, precursors and by-products. However, conventional Raman instruments, which rely on visible and near IR excitation sources, often exhibit fluorescence obscuration and poor sensitivity. An area of research interest is deep-UV Raman spectroscopy as a solution to both issues. Photon Systems RPL-200 is a deep-UV Raman and fluorescence microscope. This report presents preliminary experiments using this technology, for studies on the phenomenology of trace explosive detection, to inform future efforts that aspire to realize high sensitivity stand-off detection of explosives.

Traditional crime scene investigation is slow as traces are collected at the scene and only subsequently analyzed in the lab. Rapid contactless detection and examination of various trace materials at the crime scene without any alteration avoids degradation of traces, significantly speeds up investigation and reduces the time to capture the originator before he can commit further offences. Furthermore, contactless identification is crucial in guaranteeing the health and safety of crime scene investigators for chemical, biological and explosives treats. Here we report on the development of a quantum-cascade laser based infrared sensors, that allows instantaneous detection and identification of a wide range of forensic relevant samples, from explosives to drugs, their precursors, but also biological traces such as blood. The system is based on MOEMS-EC-QCLs that allow kilohertz spectral scan speed. Two such sources are combined in the sensor to extend the spectral coverage to increase the selectivity without sacrificing scan speed. We report on the system design and show first results on drugs and explosive identification.

Rapid substance screening is a vital yet difficult task. Different materials are best analysed by different techniques, and therefore a single tool may not always be able to identify an unknown substance. This can be addressed by a multimodal approach, simultaneously combining several orthogonal techniques into a single tool. Here we demonstrate a proof-of-concept for a solution based on three complementing techniques - infrared spectroscopy, ultraviolet fluorescence spectroscopy and microscopic imaging – for rapid acquisition of a rich dataset well suited for biochemical samples classification, with dedicated signal processing extracting the descriptive features and identifying the nature of the material.

Detection of trace quantities of explosives left behind by those handling explosives materials can be used to help identify concealed explosives and objects contaminated by bomb makers. Understanding the persistence of these particles is critical for tailoring detection strategies of non-contact optical techniques as well as non-optical detection techniques which utilize surface contact particle harvesting methods. We have measured the persistence of trace quantities of explosives materials when exposed to environmental factors such as temperature, airflow, and humidity. We have also developed a computational model based on first principles to describe sublimation behavior on both molecular and mesoscopic size and time scales, and find that it gives persistence times in agreement with our experimental data. This allows us to predict the sublimation behavior of arbitrary particle ensembles provided that certain physical properties, e.g., the temperature-dependent vapor pressure, are known. This work was funded by the Science and Technology Directorate, Department of Homeland Security under contract number 70RSAT20KPM000094.

Waveguide-enhanced Raman spectroscopy (WERS) efficiently collects Stokes-shifted scattering from target molecules in the evanescent field surrounding nanophotonic waveguides. By using a sorbent material as a top cladding, vapor phase analytes can be detected and identified at ambient densities as low as a few parts-per-billion. Previous demonstrations of vapor-phase WERS have used free-space optical components, such as microscope objectives and bulk Raman filters, to couple and filter light to and from the sorbent-clad waveguide. In this work we demonstrate a complete photonic integrated circuit (PIC) assembly that is packaged and fiber-coupled enabling us to measure WERS from trace vapor concentrations. The PIC comprises low-loss edge couplers from polarization maintaining single-mode optical fibers, sensing trenches with a sorbent top-cladding, and lattice filters for separation of the Stokes signal from the laser. The PICs are fabricated at AIM Photonics using the Silicon Nitride Passive PIC process with the TLX-VIS component library. Then, they are packaged into assemblies with permanent fiber-attach using fiber arrays. The sorbent is deposited in a thin, uniform layer in the sensing trench using one of two deposition techniques: nano-plotting and drip-coating. A laser wavelength of 785 nm enables the use of a compact spectrometer with a thermoelectrically-cooled silicon detector. Spectra are obtained with exposure times of a few seconds and show parts-per-billion detection limits for select vapors. This work successfully demonstrates the use of a compact Raman spectrometer integrated with a fully assembled PIC via optical fibers for the detection of low-density vapor-phase analytes.

Nitric oxide (NO) sensing is important for many applications including air quality and climate change monitoring. Current sensors have limited sensitivity, selectivity, and are affected by environmental interference such as humidity, which affects their accuracy. We use an ultra-sensitive optical sensing platform known as FLOWER (frequency locked optical whispering evanescent resonator) and combine it with a custom synthesized polymer coating to detect NO at a concentration of 6 ppt, which to the best of our knowledge is the lowest experimentally reported detection of NO to date. In addition, we demonstrate that our sensor is selective and humidity resistant.

We report experimental and theoretical/simulation results from 1-D and 2-D photonic crystals of novel biomaterials that are synthesized in-house at DEVCOM SC. Inspired by these biomaterials and related phenomena, we experiment with sensing analyte molecules for chem/bio detection. Because these biomaterials have high indices of refraction, they confine photons tightly. Optical properties are predicted computationally based on experimental measurements of indices of refraction using variable-angle continuous spectra ellipsometers, with both unfocused and focused probing spots. This biomaterial, when combined with polymers that enable smooth films (polyvinyl acetate, ethyl cellulose, etc.), would be a natural, environmentally friendly, non-toxic, and toxicologically safe material appropriate for scaling up for large-area optical sensing of molecules, especially toxic industrial molecules. We carry out initial research on the detection of analyte molecules in solution via optical methods and compare to simulations. We contrast with inorganic materials for remote sensing, reconnaissance, UAVs, etc. and compare challenges in scalable fabrication including synthetic biology.

More sensitive toxic gas sensors can provide earlier warning by detecting lower concentrations at greater distances from the source than conventional technologies. Recently, microtoroid whispering gallery mode optical resonators with selective polymer coatings have demonstrated part-per-trillion sensitivity to several gases, making them one of the most sensitive gas detection technologies. However, these sensors are currently coupled to laser sources via fragile and vibration-sensitive tapered optical fibers, hindering their translation from the laboratory into the field. Here we design and assemble periodic nanostructures onto the rim of microtoroids to improve free-space coupling efficiency, obviating the need for the tapered optical fiber.

Fastly detecting hazardous, non-volatile chemical substances on paved roads and streets is a topic of utmost military importance in an area denial scenario. Since the 1980s, inherently slow manual sampling has been avoided on armored vehicles using a small silicone wheel that continuously accumulates surface contaminations. After a given sampling period, collected (and potentially hazardous) contaminants on the wheel are thermally desorbed and analyzed by mass spectrometry. This approach led to further technological advancements, including implementing a double-wheel sampling system for automated, uninterrupted operation. Suspicious areas are examined at low driving speeds (approximating a fast-walking speed) with comparatively low spatial resolution, as the silicone wheels can only be rolled comparatively slowly to ensure continuous surface contact. Incremental improvements may further optimize the double-wheel sampling system. In that context, we are currently investigating laser desorption technology to achieve a more targeted heat treatment of the complete silicone wheel and increase spatial resolution and sensitivity. In addition, we also contribute to the development of advanced ion mobility spectrometers, which are both fast scanning and highly sensitive, as a viable alternative to cumbersome mass spectrometers. As a radically different approach, we report here on a measurement system using back-scattering IR-spectroscopy to optically interrogate samples at a standoff distance and process the information without delay. The used IR light source consists of three coupled broadband quantum cascade laser modules, each with an integrated micro-opto-electro-mechanical grating scanner (MOEMS EC-QCL). The elaborate coupling of three such pulsed laser modules provides an ultra-broadband spectral scan within the IR-fingerprint area (covered by those three MOEMS EC-QCLs) at a repetition rate of almost one kilohertz, thus resulting in measurement times of as short as one millisecond per (ultra-broadband) spectrum. We found that even minor contaminations of hazardous substances are identified using this setup. Furthermore, preliminary laboratory tests revealed a successful detection after the measurements on a fast-moving contaminated object. The experiments were performed at different observation angles with a considerable focal depth. The proof of concept shows that this novel QCL-based chemical detection approach is fast enough and promising to continuously monitor the ground with sufficient geometric resolution at cruise speeds on uneven and textured surfaces.

Alakai Defense Systems has recently developed what we believe is the first one-handed UV Raman sensor for standoff trace detection of chemicals, which we refer to as Argos. Argos, equipped with increased range and detection capability, is the higher performance version of the lower cost SAFR sensor. And because they are lightweight, both Argos and SAFR can be deployed onto unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). Data will be presented showing Argos detection performance on residue and trace samples at ranges from 2 m to 15 m. Further, data will be presented from UGV and UAV experiments performed with the SAFR system in warehouse and outdoor applications.

High peak power laser systems are capable of delivering 266 and 213-nm light pulses for proximal detection using Raman spectroscopy. These lasers are becoming more energy efficient and compact making them ideal as deep-UV excitation sources in hand-held devices. This talk will describe our efforts to over come some of the difficulties associated with using UV-Raman spectroscopy in a compact, battery operated system with a unique optical design with gated detection allowing spectra to be collected in daylight conditions from distances of 20 meters and more.

A hand-portable standoff trace chemical detection system was developed using a long-wave infrared (LWIR) microbolometer (MB) camera in combination with widely tunable external-cavity quantum cascade lasers. The system acquires hyperspectral images of the target surface’s reflectance in the LWIR portion of the “chemical fingerprint” band to allow for high-sensitivity detection and high-specificity identification of a wide range of surface chemicals. With a LiDAR-based autofocus, the system can measure targets at standoff distances as long as 15 m with clear chemical signatures in the resulting spectrum. Array scan measurements of powder and liquid chemicals at various standoff distances are presented and shown to enable the user to spatially locate trace contaminants on a variety of surfaces. Finally, the stability of the SNR is analyzed and shown to enable reference-free measurements, a significant step towards a versatile “point-and-click” LWIR-based standoff trace chemical detector.

In order to improve the health and safety of the warfighter by bringing clinical diagnostic capabilities into the field, the Applied Research Laboratory and The Materials Research Institute at Penn State University are developing BIRDE, the Biothreat Identification using Raman Diagnostic Evaluation system. BIRDE is a field portable, reusable diagnostic system designed to provide the warfighter a highly specific assessment of pathogens (i.e. viruses, bacteria, bio threats) present in saliva, nasal washes, aerosols, and wastewater. BIRDE utilizes a microfluidic carbon nanotube (CNT) device for non-destructive, label-free capture of pathogens, coupled to a Surface Enhanced Raman Spectroscopy (SERS) sensor for biomarker interrogation and identification. Spectra collected from the captured pathogens are analyzed using advanced machine learning approaches, including deep learning networks, to provide highly accurate biomarker identification. The full process can be completed in minutes, providing critical information in a SWaP friendly configuration designed to keep up with the warfighter’s quickly changing environment.

BioLayer Interferometry (BLI) is an analytical technique utilized for measuring binding interactions between biomolecules. This is a label-free technology that measures the change in white light signal as molecules, proteins, antibodies, or other ligands attach to the end of an optical sensor tip throughout an assay in real time. BLI is advantageous in detection because the sample preparation is free of tagging or other manipulations, samples can be analyzed without purification, and the sample is fully recoverable. BLI is commonly used to measure affinity between two or more molecules as a characterization technique, high throughput screening of molecules for bioactivity, and quantitation of molecules in reaction mixtures or cell lysates. We utilized BLI as a yes/no detection platform to replace prolonged immunoassays for biothreat surrogate detection and developed an accompanying data analysis tool to automate the decision-making process for inexperienced users. Proprietary anti-mouse capture biosensors bound to two separate monoclonal antibodies, which then bound varying concentrations of protein to measure the on- and off-rates of the protein to the antibody at equilibrium. Software fitting reports kinetics values that were used to develop a secondary screening tool to determine a true binding event over a false positive or nonspecific interaction between binding partners. Over 1300 binding curves were generated to define the parameters of this screening tool, resulting in high confidence in the yes/no decision process. Implementation of this tool reduces the expertise needed for biothreat detection in the field or a high-throughput screening scenario, while also reducing the time needed from sample receipt to answer 2-fold over ELISA or MAGPIX immunoassays.

In recent years, there has been a growing emphasis on the real-time detection and collection of airborne biothreats due to the challenges highlighted by events like the COVID-19 pandemic and the persistent concerns surrounding bioterrorism, bioweapons, and biowarfare in the realms of defense and national security. The study presents a solution to the limitations of inertia-based devices in capturing nanoscale biothreats, such as viral particles. It introduces a device that increases particle size by inducing water vapor condensation and heterogeneous nucleation on nanoparticles. Experimental results demonstrate its effectiveness in enlarging 0.4 µm polystyrene particles to approximately 2 µm. The device, featuring three segments, employs a stratified air and water flow and utilizes the principle that the mass diffusivity of water vapor in air surpasses the thermal diffusivity of air to create supersaturated conditions. Importantly, this device can seamlessly integrate with existing inertial-based systems, thereby enhancing their capability to capture nanoscale bioaerosols and improving collection and enrichment efficiency.

An advanced method for rapidly detecting bioaerosol particles was reported. This method can record circular intensity differential scattering (CIDS, normalized Mueller scattering-matrix element -S14/S11) phase functions from single individual flowing through particles at a measurement rate >50,000 particles/sec. Experimental results showed that this method can rapidly discriminate bioaerosol particles from non-bioaerosol particles.

To guarantee safe supply of drinking water, there is a need for fast, sensitive and robust techniques for early warning of potentially pathogenic microorganisms. An optical measurement system has been developed to spectrally study the UVinduced autofluorescence from single microorganisms in a water flow. Particles in the flow are detected with a continuouswave laser whereby an ultraviolet laser pulse is fired and a spectrometer measures emitted fluorescence. Suspensions of Cryptosporidium parvum (Crypto), Bacillus atrophaeus (BG, a non-pathogenic simulant bacterial spore) and Escherichia coli (Ecoli), in distilled and tap water have been examined. The results from single particle measurements are compared to fluorescence emission spectra captured on suspensions with a fluorescence spectrometer and the strength and variability of single organism spectra, with respect to detection applications, is also investigated.

Spectroscopic identification of aerosolized chemical threats is challenging due to the complex nature of the photon/particle interaction, as well as the diversity of possible particle sizes, morphologies, and compositions. Constructing a database of laboratory - measured transmittance spectra that covers each permutation is not practical. However, calculation of the spectra using the measured optical constants as a function of wavenumber, n(ν) and k(ν), for each of the liquids and/or solids composing the aerosol particles provides a viable alternative. These synthetic spectra (vis-à-vis laboratory-measured spectra) can be used to identify chemicals of interest and subtract out background interferents in field measurements. Using a well-established multiple pathlength approach, we measure the optical constants n/k for liquids that may be found as chemical species of interest or common background interferents aerosolized in plumes. These measurements are also used to generate several test case aerosol synthetic spectra.

Many CBRNE threat materials are optimally implemented as aerosols. However, aerosol threats present unique challenges for sensor development, test and evaluation since particles may disburse in a turbulent atmosphere differently from tracer gases. This presentation addresses the need for tracer aerosol particles with known size distributions to be released with agent target simulant aerosols to provide ground truth for sensor test and evaluation. A novel approach for achieving uniquely identifiable individual aerosol particles is described based on utilization of quantum dots (QDs) and/or other luminescent nanocrystals (NCs), to create a multiplexed spectral barcode in tracer aerosol particles. QDs are small, typically nanometer scale, compared to micron-sized polymer beads as host aerosol particles. They also possess desirable optical properties of narrow, efficient emission bands, and are typically long-lived compared to organic dye molecules that photodegrade in sunlight. Multiple QD subpopulations, each with a narrow emission band at a distinct peak wavelength, can be encapsulated in a polymer microbead, conferring a superposition emission profile having multiple narrow peaks. The relative intensities of the emission peaks can be controlled by adjusting the number of QDs in each subpopulation. This spectral emission profile effectively becomes an individual particle barcode. Multiple polymer bead samples can be prepared each with different emission pattern (barcode). These samples can be mixed with target materials to be simultaneously released as aerosols to provide test ground truth for the simulant. Proof-of-principle experiments assessing the feasibility for using combinations of embedded NC populations in micron-sized droplets, as well as potential challenges to practical implementation will be discussed.

One of the uses of the Ambient Aerosol Test Facility (AATF) at the U.S. Naval Research Laboratory is to operate as a test facility for developmental chemical aerosol sensors. The facility draws ambient air from outside the building, with or without HEPA filtration, then introduces aerosols by various means within a 30 cm diameter, 14 m long wind tunnel at flow velocities ranging from 2 to 20 m/s. The turbulent flow generated provides a uniform distribution to a few percent across 90% of the tube diameter to sampling ports in a 3 m long test section at the end. The test section allows sampling and analysis by various sensors to determine aerosol size, concentration, and chemical composition. For the current program of interest, we generate simulant aerosols representing various classes of chemicals of interest. A range of referee instruments to characterize the aerosol in terms of size, number and composition is planned. Commercially available particle sizers and counters, a gas analyzer and an aerosol mass spectrometer are part of the suite of referee instruments. We use a high-resolution, time-of-flight aerosol mass spectrometer (HR-ToF-AMS) from Aerodyne, Inc., which is configured with an aerosol focusing lens to transmit between 50 nm and 3.5 μm diameter particles, and provides size, mass loading and chemical composition information. A calibration system consisting of a scanning mobility particle sizer and a water-based condensation particle counter is used to validate the operation of the AMS instrument. We describe the AMS instrument and its use at the AATF for the assessment of other instruments.

This study examines an application of simulated single-particle spectra for analysis of IR scattering by suspensions of small particles. Particularly, the case of aerosols comprised of solid particles is explored. These particles vary in size, shape, and orientation with respect to the incident light. Single particle scattering is numerically calculated and the simulated data is surveyed. The scattering of particle ensembles is considered and a prototype system consisting of a sparse distribution of particles is evaluated.

The Savannah River National Laboratory (SRNL) has been developing advanced spectroscopic tools for the characterization of plutonium-bearing compounds with the intent to develop material and process signatures for nuclear forensics. Plutonium in a production, refining, or finishing facility will exist in many forms including oxide precursors (PuF₄, PuF₃, Pu oxalate, etc.), oxide, and metal. The ability to identify plutonium in each of these chemical forms and determine their processing history is crucial for the development of spectroscopic signatures. This presentation will focus on our work to develop and apply spectroscopic tools at SRNL using doubled-walled cells (DWC) to characterize the thermal decomposition of oxalates, calcination chemistry, alpha-decay-induced chemistry, age dating since last calcination, and other signatures related to plutonium processing.

In the aftermath of a radiological dispersal device (RDD) or dirty bomb explosion one of the priorities of health and safety efforts is to characterize the radiation contamination in the affected area including ground zero, which could be in very high radiation area where measurements by human cannot be performed. The Nevada Nuclear Security Site (NNSS) at Remote Sensing Laboratory (RSL) at Joint Base Andrews, Maryland has designed, developed, and deployed a couple of remote-controlled robots to mount a 4” x 4” x 2” sodium iodide scintillator about (~0.6 m) for radiological contamination mapping that can work in high radiation dose area. The device is most useful in large area contamination characterization and detecting invisible embedded sub-micron particulate debris with gamma radioactivity from surface/subsurface up to a depth of 3” underground for forensic work. The system employs a configurable four wheel drive All Terrain Robot from SuperDroid Robots. The robot comes with a 24-inch x 24-inch aluminum chassis to house the four motors, motor driver, wheels, and batteries. The 10-inch all terrain pneumatic wheels are powered by Model IG52-04 24 VDC 136 RPM gear motors. This device development project provided a versatile and robust platform for deployment of ground sensors to collect, and document radiation monitoring data with meta data from multiple modality (still video images, gamma neutron images). By deploying a number of these in the field for both Preventative Radiological Nuclear Detection (PRND) operations and Consequence Management (CM) monitoring purpose it supports a gamut of NNSA emergency response operations. Future work will involve establishing communications between these robotic platforms using radios like MPU5 and resident third-party software to provide multiple algorithms based on Bayesian decision making.

Uranium hexafluoride (UF₆) is a commonly used material feedstock for uranium enrichment processes. When introduced to water in the atmosphere, it reacts rapidly to form uranyl fluoride (UO₂F₂). Here, we investigate the UF₆ hydrolysis reaction by cryogenically trapping reaction intermediates and characterizing the trapped species by Fourier transform infrared (FTIR) spectroscopy. The reactant species are sequentially layered onto a diamond substrate held at 10K by a closed-cycle liquid helium cryostat. At this temperature, the hydrolysis reaction is not spontaneous and can be catalyzed by the introduction of heat. Upon heating, the reaction moves through several intermediate compounds before proceeding to the final UO₂F₂ product. Several previously unobserved bands appear while the reaction progresses. These bands may help to elucidate the mechanism behind UF₆ hydrolysis.

As the world pivots away from hydrocarbon to hydrogen energy sources, new detection methodologies will be required to maintain safety. A critical factor in the safe use of hydrogen energy sources is access to low-cost, high-performance stand-off detection technology which can readily and autonomously detect hydrogen leaks. The tried-and-trusted path of absorption spectroscopy cannot be utilized with hydrogen due to the absence of optical absorption features for hydrogen. In addition to this, the difficulty in performing range-resolved absorption measurements, precludes the use of backscatter-absorption techniques for hydrogen detection. However, the significant Raman scattering cross-section for hydrogen can be exploited as a route to detection. This approach mandates the use of time-correlated single photon techniques and so confers significant advantage over absorption techniques: specifically, revealing the nature and position of the target substance. We therefore exploit hydrogen’s Raman-scattering cross-section, together with state-of-the-art UV excitation laser and single-photon detection technology to realize a practical handheld system permitting sub-percent level measurements within a 3m range with ~1second integration times. In this paper, we will outline the need for this detection methodology; the challenges associated with realizing practical systems based upon it; and demonstrate our recently developed hand-held hydrogen sensing device.

Advances in critical subsystem technologies have allowed Telops to develop the next-generation of hyperspectral imaging systems with significant reductions in Size, Weight, and Power (SWaP) requirements while maintaining imaging and data quality performance. This presentation will serve as an overview of the system architecture and analysis capabilities of the next-generation Hyper-Cam Nano hyperspectral imaging system. The Hyper-Cam Nano platform includes a miniaturized (172 x 172 x 181 mm) Fourier Transform Spectrometer (FTS) mounted on a gimbal, which can be deployed in a ground configuration, or easily affixed to an octocopter drone. The real-time data analysis capabilities embedded in the Hyper-Cam Nano provide an ability to simultaneously resolve multiple spectral signatures within a scene for the detection and identification of gases and solids, and even quantification for gases. This novel instrument will offer new capabilities in gas detection and identification applications for the defense, industrial, and environmental sectors.

There is a need in the Homeland Security Enterprise for small, lightweight, and low-cost chemical sensors for many real-world applications. One important aspect is ensuring food protection against both accidental and intentional chemical contamination. Factors such as production batch size, shelf life, and quality control procedures already in place could affect the number of people impacted. Emerging sensing technologies and data analytic tools from the fields of nanomaterials and machine learning provide an opportunity for low-cost microsensors that could be widely distributed to provide onsite and real-time awareness of contamination events. To survey the challenges around this objective, a functionalized carbon nanotube-based set of sensors were evaluated for the ability to identify chemical vapors and detect contamination in complex mixtures in common food matrices such as apple juice. It was found that the detector could identify pure chemicals in dry lab air as well as contamination that was present in the headspace over food samples. Technical challenges were identified, with the most significant being variable signature responses between the three different identically configured detectors. Strategies for mitigation of sensor variability were evaluated, including machine learning techniques as well as sensor calibration procedures.

The author has reviewed the Proceedings archive, and has selected a number enduring themes from the nearly 750 papers submitted across twenty-four years. The author believes these themes serve as important touchstones for the successful development of future capabilities to address CBRNE challenges, whilst admitting a personal bias towards spectroscopic methods for chemical sensing and imaging. Taking a historical perspective reveals impressive developments in foundational and underpinning aspects, as well as novel sensing techniques and their applications; it also provides context to the issues identified, from which lesson can be learned. The author also shares their personal view on the value of the Conference to the community of practice that it brings together.

We present recent results from using a quantum cascade laser as a broadband infrared source for real-time spectroscopy. Using a Fabry-Perot quantum cascade laser (FP-QCL), we illuminate samples with broadband IR light from 8.2 μm to 10.2 μm. This laser source and its operating conditions (25°C and 500 ns pulses at 200 kHz repetition rate) were chosen to give broad spectral coverage and high power output (42.7 mW average power, pulsed operation). We utilized a simple grating spectrometer together with a high resolution (1280 x 1024 pixel) micro-bolometer focal plane array to capture each full spectrum in a single frame. Several samples were characterized using this apparatus in a transmission-style measurement and their real-time spectra were compared to their Fourier Transform Infrared (FTIR) spectra. The results show a good agreement between FTIR and the real-time grating spectrometer for several representative samples, including bandpass filters, and IR active materials such as polystyrene, polyester, and polypropylene. Finally, we show the real-time benefit of this approach by measuring a moving target foil on a rotating chopper wheel.

A standoff chemical detection system was developed to rapidly detect trace chemicals on surfaces. In only 0.1 s, the system can measure the long-wave infrared (LWIR) spectral reflectance from a surface over the wavelength range of 7.5 – 10.5 μm with a spectral resolution of 2 cm^-1. Under these conditions, a signal-to-noise ratio (SNR) > 100 was demonstrated at standoff distances of 0.5 – 1.5 m. As a detection example, saccharin was detected on high-density polyethylene (HDPE) at a surface concentration of 30 μg/cm². The high-speed acquisition capability was made possible by combining a thermoelectrically cooled single-pixel HgCdTe (MCT) detector, advanced acquisition electronics, and fast-tuning external-cavity quantum cascade lasers (EC-QCLs).

Volatile chemicals can form expansive toxic gas clouds after an accidental or deliberate large-scale release. The emerging toxic clouds may be invisible to the optical spectrum of the bare eye, but they are generally detectable using suitable standoff or point detectors. Standoff detectors are particularly suited for monitoring a large area within their line of sight, whereas remotely controlled point detectors may be used to survey specific areas of strategic interest. A favorable spatial and temporal detection resolution is usually achieved using standoff Fourier Transform Infrared (FTIR) spectrometers. To obtain a proper spatial resolution beyond a mere imaging view, at least two imaging systems must operate concurrently with an adequate opening angle concerning the distance of reconnaissance. During a field trial in Umeå, Sweden, we utilized an appropriate setup for standoff tomography to detect and identify comparatively small-scale chemical releases of gaseous substances and evaporating aerosols. We reached high resolutions in space and time at a standoff distance of over a kilometer. Thus, we have shown that a targeted early warning and short response times for emerging threats are possible while operators remain at a safe location. Additionally, the field trial revealed the significant influence of the properties and concentration of the deployed chemicals, wind shear, and turbulence on the detection result. In support of spatially and temporally resolved standoff detection, targeted drones carrying fast and sensitive point detectors, such as ion mobility spectrometers, may be used as an orthogonal technique to independently confirm identification.

Colorimetric chemical sensors are some of the simplest and low-cost sensors available to the End User today. Advancements in small, chip-based optical readout packages, coupled with low-power electronics and wireless communications modules, have allowed for the development of a hands-free colorimetry-based chemical sensor that effectively acts as self-reading M8 paper that responds to vapor-phase chemical threats. We have designed a self-contained, throwable and reusable prototype (TOSSIT – the Tactical Optical Spherical Sensor for Interrogating Threats) that can be used by the End User, acting as a remote point vapor sensor to provide early warning of a chemical threat. The TOSSIT sensor has wireless capability to let the user know what is the current alarm state of the device. These prototypes have been tested against toxic industrial chemical (TIC) vapors in the lab and have undergone surety testing against select chemical agent threats. Other mission areas under consideration include perimeter defense using the sensor in unobtrusive packaging as well as wide area deployment by aerial assets.

Spectral data (e.g., Raman and IR) is crucial for field-forward detection and identification of various threats. In addition, the knowledge concerning desensitization of explosive materials is of importance with regard to safe handling and hazard management protocols. In this study, spectroscopic data as well as impact sensitivity measurements of tetraammine copper perchlorate (TACP) and tetraammine copper chlorate (TACC) were collected. Since the tetraammine copper complexes are claimed to be hygroscopic and to some extent undergo hydrolysis in the presence of water, it could be a suitable desensitization agent. Therefore, a further purpose of this study is to investigate changes in spectral features and impact sensitivity after added desensitization agent (water) has been removed from the explosive by drying. For example, it is of utmost importance to understand if addition of water to the explosive ”permanently” degrades the material. The initial results presented herein implies that TACC is more chemically resistant to water compared to TACP.

Rapid analysis of clandestine laboratories is necessary to maintain a protective posture while gaining an understanding of surroundings. We have shown that there is a potential of using a threat anomaly detection (ThreAD) algorithm that allows for rapid, real-time hyperspectral analysis. Understanding if there are primary reagents or products of explosive materials is of concern to recognize what the potential threat in a clandestine laboratory may be. Herein, we discuss the use of a commercial hyperspectral system that is used to collect data that is analyzed using our ThreAD algorithm for existing and emerging explosive threats. In this work, we look at pure spectra of the principle synthetic components of potential explosive threats and the resulting explosive that is made in a method that is consistent with what may be done in a clandestine laboratory. The spectra are parameterized consistent with what is used in the ThreAD algorithm and is clustered in three-dimensional space. The separation of the principle synthetic components and resulting explosives are compared and related to other explosive threats and potential surfaces that the material may reside on. This provides us with the basis of understanding what threats may be detected as anomalies with our ThreAD algorithm and how they compare to others.

The capabilities of the dual-comb spectrometer for stand-off sensing of traces of explosives in scanning mode were systematically tested at a distance of 0.5 m with RDX and PETN with the mass loadings of 5-8 μg/cm² deposited onto nonporous surfaces: glass, transparent acryl, white and black plastic. Total scanning time of 400 points area (12 x12 cm) with the beam diameter of ~ 1 cm in the target area was 14 s, limited by the processing time of the frequency comb data using our current hardware. Data analysis of the scanning area comprised baseline removal and plotting of Pearson correlation coefficients, as the heatmaps take 30-90 s to acquire depending on both the algorithms for baseline removal and the computer hardware used currently (24-core processor). Baseline removal with the asymmetric least squares (ALS) algorithm is more reliable but takes longer to process (90 s). The probability of detection on nonporous surfaces with the ALS method is 92-98% for PETN and 65-90% for RDX with the confidence level of 90%, depending on the surface material. With the false positive rate being set to 10%, the true positive rate of the system reaches 90% with the ALS method for baseline removal for RDX and PETN on tested surfaces.

A clear understanding of sublimation kinetics is critical for developing detection techniques. Sublimation affects the shape and size of small particles as well as the environment. The particle characteristics are essential for various types of measurements including optical response. Molecular dynamic simulations were used to better understand the kinetics of both the condensation of molecules onto and the sublimation of molecules from the surfaces of explosives materials such as 2,4-dinitrotoluene and 2,4,6-trinitrotoluene. These studies were undertaken to better understand the persistence of trace quantities of particles on surfaces to aid efforts to optical detect strategies of explosives as well as physical harvesting approaches such as collection with swabs. Potential-energy-function parameters for the molecular dynamic (MD) simulations are designed and values for the probability of recondensation onto the surface (i.e., sticking coefficient) and the velocity distribution of molecules escaping the particle surface calculated. These values were compared with other experimental and simulation efforts for the studied materials.

Fast and accurate quantification of a dangerous chemical release can reduce human exposure and environmental contamination. We demonstrate that deep learning models can provide quick and practical source term estimates from images of chemical releases, using a dataset compiled from volcanic plume observations. We nd that the best performing deep learning models are able to predict "large," "medium," or "small" SO₂ releases from unseen images of volcanic plumes with over 80% accuracy, and can be trained on imagery taken from the ground, air, or space. We test a range of model architectures, training strategies, and optimization approaches to determine what combination of properties produce a model that is robust and operates in the widest possible variety of situations. We evaluate how well the model generalizes to images of industrial SO₂ plumes and find that the best-performing volcanic plume model achieves 70% accuracy on images of industrial SO₂ plumes, demonstrating the potential of using deep learning for image-based hazard quantification.

We are investigating the interaction of dimethyl methylphosphonate and diisopropyl methylphosphonate, both simulants of chemical warfare agent, on anhydrous and natural kaolinite and compare the results with those of these two simulants on titania. Infrared spectroscopy indicates the presence of multiple rotational isomers that have different interactions with the surface. Initially, we observe physisorbed species which desorb again over time. However, at later times we observe a chemisorbed species which appears stable.

The determination of the refractive index (n) and extinction coefficient (k) of a compound is a key step for the prediction of its optical properties in different morphological states. For a solid, these optical constants can be obtained experimentally by spectroscopic methods such as ellipsometry and single-angle reflectance spectroscopy. However, in the context of sustaining databases for hyperspectral imaging with the n and k values of hazardous or noxious chemicals, these methods are not always conceivable due to the unobtainability of a single crystal or the hazards associated with heating, ball-milling, grinding and/or pressing the compounds. Hence, exploring a complementary preparation technique holds great interest. To this end, this work revisits the mull technique, which is classically used to perform qualitative FTIR transmission spectroscopy of a powder by trapping it in a mineral oil such as Nujol. By adding gravimetric measurements during sample preparation, this study seeks to provide a quantitative method satisfactory for deriving the absorption coefficient (α) and, consequently, the extinction coefficient (k).

In this study we investigate the possibility of using spectral resolutions for infrared measurements of solids and liquids that are not powers of two, e.g. are not at 1, 2, 4, 8, or 16 cm^-1 resolution. In almost all reported literature of the last fifty years the resolution used to record a Fourier transform infrared spectrum has been a power of two. This stems from the fact that 1) the Cooley-Tukey algorithm used to compute such a transform was constructed to use only powers of two and was also driven by 2) the fact that the computing horsepower required to compute the Fourier transform increases as Nlog₂(N), where N is the number of points in the interferogram (spectrum). For typical spectra, however, the CPU time is no longer a consideration. Our study is based on both liquid and solid spectra, all of which were recorded at 2 cm^-1 resolution. There were a total of 70 solids spectra representing 2,472 spectral peaks and 61 liquids spectra (1,765 spectral peaks), each peak inspected for being singlet / multiplet in nature. Of the 1,765 liquid bands examined, only 27 have widths less than 5 cm^-1. Of the 2,472 solid bands examined, only 39 peaks have widths less than 5 cm^-1. For liquids, the mean peak width is 24.7 cm^-1 but the median peak width is 13.7 cm^-1, and, similarly, for solids, the mean peak width is 22.2 cm^{- 1}, but the median peak width is 11.2 cm^-1. In both cases, solids and liquids, a skewed peak width distribution was observed, the peak of the distribution representing narrower bands in the 7 to 9 cm^-1 FWHM range but displaying a long tail to the very broad bands, with some displaying spectral widths of 100 cm^-1 or more. Because one of the most important criteria for successful instrumental design in IR spectroscopy is the spectral resolution, the data were further analyzed showing that a value to resolve 95% of all bands is 5.7 cm^-1 for liquids and 5.3 cm^-1 for solids; such a resolution would capture the native linewidth (no instrumental broadening) of 95% of all the solids and liquid bands, respectively. Based on the present results we suggest that, when accounting only for intrinsic linewidths an optimized resolution of 6.0 cm^-1 will capture 91% of all condensed-phase bands for IR detection of chemical, mineral, and biological materials.

Using kinetic FTIR, we have measured the infrared spectra of diisopropyl methylphosphonate (DIMP), a chemical warfare agent (CWA) simulant, and its decomposition products under a variety of heating conditions. DIMP and its phosphorus-containing decomposition products have many overlapping spectral features, all of them changing with temperature, making unique identification of individual compounds exceedingly difficult. We have analyzed these spectra and identified spectral features that allow us to identify specific compounds.

Performing standoff detection tests of aerosol clouds is complicated by the level of effort to set up for such experiments and uncontrolled ambient airflows. A more controlled approach is desirable for early stage experiments and for infield validation tests or calibration purposes. Here we describe the development of a static panel coated with the particle constituents of an aerosol cloud collapsed into a two-dimensional rendering. Providing the support panel is sufficiently optically benign at relevant wavelengths for the target aerosol particles and the particles are separated by sufficient distances to interact with infrared light independently from one another suitable test panels maybe be fabricated. Here we elaborate on our two-dimensional static aerosol panel concept and discuss the choice of infrared benign support, how to deposit aerosol particles over a relatively large footprint up to one-meter square and the optical characterization of test panels produced.

We are evaluating a range of commercially available liquid aerosol generators to produce chemical aerosols of controlled characteristics such as aerosol size, mass, and concentration that can be implemented at our Ambient Air Test Facility (AATF). For our current program of interest, we generate simulant aerosols representing classes of chemicals of interest. Various types of generators are characterized in the laboratory, including atomizing nozzles and vibrating mesh-based devices. Studies are conducted to quantify the range of aerosol size, mass, and number concertation achievable for the different types of aerosol generators. Some of the analysis and the laboratory results are presented here.

The size of particles typically present in aerosol clouds are in the range of 0.1-10 μm, which is within one order of magnitude of the infrared (IR) wavelengths in the molecular “fingerprint” region (approx. 6-12 μm). This length scale is also close to the optical absorption depth for materials of interest. Consequently, IR scattering signatures of aerosols differ from those associated with reflectance from surfaces of bulk materials. Furthermore, the shape of particles is also a factor that affects IR scattering spectra. Accordingly, both aerosol particle size and morphology must be considered in the development of accurate models and reliable detection algorithms. This report describes recent advances concerning modeling of IR scattering signatures of micron-sized spherical particles (found in liquid aerosols) and irregularly-shaped particles (found in solid aerosols). In our model, spherical particles are modeled using Mie scattering theory while non-spherical ones require numerical modeling, using finite-difference time-domain (FDTD) solvers. The model inputs involve particle optical constants (complex index of refraction - n and k), aerosol concentration, particle size (diameter) distribution and various shape parameters (for solid aerosols only). Our model addresses two detection scenarios: one where the signatures consist of back-scattered light only and the other where the portion of the forward scattered light which (diffusely) reflects off background surfaces is also collected. We discuss the effect of particle size and shape distribution on the IR signatures of aerosol clouds. We report on efforts to optimize our model such that a large number of spectra can be generated quickly and efficiently, which is a requirement for use in detection algorithms, both for training and usage. We also present preliminary results on machine learning approaches to develop detection algorithms capable of detecting aerosol clouds that have variable IR signatures due to different particle/size distributions. In this paper, we focus on liquid aerosols, while solid aerosols are discussed in a related conference paper.

Silicon-nitride-based photonic integrated circuits (PICs) can operate with low loss at visible and near-infrared wavelengths. This spectral range is essential for many applications in chemical and biological sensing, quantum sensing and networking, physical sensing, precision timekeeping, and augmented/virtual reality. At present, highquality silicon nitride PIC platforms optimized for operation in the visible are offered by low-volume custom foundries or by 200 mm silicon-based foundries. Both typically lack the minimum feature sizes and wafer throughput required for high-yield, high-volume operation at short wavelengths. In this work we describe a new component library and foundry process developed at AIM Photonics, a state-of-the-art PIC foundry. The TLX-VIS component library for the Silicon Nitride Passive PIC process is designed to operate in three bands at wavelengths from 500 nm to 1000 nm. A trench down to the primary waveguide layer is offered for sensing applications, and a dicing trench enables access to waveguide facets for low loss edge coupling. Propagation losses range from 0.2 dB/cm at 785 nm to 2 dB/cm at 532 nm. The component library is designed for both the TE₀₀ and TM₀₀ modes and includes broadband directional couplers, polarization rotators, edge and grating couplers, lattice filters, and high-Q ring resonators. The waveguides have small minimum bend radii (<100 μm) and low fluorescence, which is critical for applications in Raman sensing and quantum information. The component library and PICs are compatible with AIM Photonics’ Test, Assembly, and Packaging facility, enabling fully-packaged, fiber-attached assemblies.

Vibrational absorption spectra are presented for isolated molecules of some common polyfluorides, present in food and pharmaceutical production, which are calculated using density function theory (DFT). This study further demonstrates using DFT for characterizing IR-spectral features of substances within the environment. 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 templates, which are for spectral-feature comparison, and thus detection of spectral-signature features associated with target materials.

Airborne viruses and bacteria are the cause of many deaths annually worldwide. Biosensors allow for the specific detection of target molecules and can be used in conjunction with a capture device to create a standalone system to monitor the air for airborne pathogens. To be able to detect multiple biothreats, a multiplex biosensor functionalized with different aptamers can be used. This multiplex sensor is composed of a series of compact sensors each with a unique aptamer immobilized onto the sensor surface. Here, we report on a compact, aptamer-based biosensor that allows for multiple sensors to go in series with a capture device to detect Ebola virus soluble Glycoprotein (sGP).

Mid-infrared sensing in the broadband spectral region of 5 – 11 𝜇m is suitable for detecting and quantifying multiple trace species. However, the challenge in detection is precise discrimination due to the broad linewidth of molecular transitions of species like methane, nitrous oxide, and other volatile organic compounds. In addition, isotopic transitions are generally weaker, with significant overlap with the neighboring abundant molecular transitions. This paper shows broadband detection of multiple species using an external cavity laser operation in 6 to 11 𝜇m spectral region. We use a combination of Savitzy-Golay filtering and machine learning-based classification to discern weaker rotational vibrational transitions. The proposed scheme is used to denoise and discriminate molecular transition in mid-infrared absorption spectroscopy. We show that an optimized S-G framework can be used by choosing a selected frame length determined by the adaptive learning outcome with low loss. We show that an ML-based adaptive SV filter can effectively suppress mod-hop (or any other instrumental-related effects and drifts). This is achieved by appropriately training the absorption spectroscopy signals with a calibrated reference in a (gaussian or thermal) noisy environment.

Operating a chemical detector system at a stand-off distance from a hazard is advantageous. By increasing the distance between the detector and the hazard, the risk of contamination decreases, whilst increasing personnel safety. The aim of this work is to develop an optical bench mounted system for presumptive identification of toxic chemical simulants at a stand-off distance. Unlike commercially available instruments, the Dstl system is not a light tight unit, but instead offers a platform in which different optical sub-components can be tested in an attempt to optimise the overall sensitivity of the system. By operating in a pulsed mode, not only is detector noise reduced but also the background radiation due to laboratory light. To test its performance, the Dstl Raman system was assessed at a stand-off distance of 2 m and collected spectra from Raman standard materials. Additional work was conducted with the Dstl Raman system to collect spectra from toxic chemicals associated with chemical warfare agents (CWAs). Indicative comparisons were made against data collected on commercially available systems, in order to identify future development for the Dstl system. Overall, this research piece aimed to exploit the powerful discriminatory nature of the Raman technique whilst demonstrating it under room lights and at useful stand-off distances. The Dstl stand-off Raman system performed well and provided clean spectra for the CWA simulant study. However all the tests reported here were measured on bulk samples and further work is needed to determine how the system performs against samples deposited as thin layers on realistic substrates.

This study presents a scientometric analysis of hydrogen peroxide (H₂O₂) sensor research, leveraging data visualizations generated by VOSviewer to elucidate the field's evolving trends and dichotomies. Through a detailed examination of clustered terms, our analysis distinguishes two primary research trajectories: technological and material developments (Cluster 1) versus application-oriented investigations, primarily in biological contexts (Cluster 2). Cluster 1 emphasizes the critical roles of catalysis and metal nanoparticles in enhancing sensor selectivity and sensitivity, highlighting the integration of chemistry and biosensing techniques to overcome the challenge of developing highly sensitive H₂O₂ sensors. Meanwhile, Cluster 2 explores the use of H₂O₂ sensing in biomedical and clinical applications, including glucose monitoring for diabetes management and the potential of H₂O₂ as a biomarker for oxidative stress and cancer diagnosis. This division reflects a comprehensive approach that spans controlled laboratory experiments to potential medical applications, underlining the significance of H₂O₂ in linking material science advancements with biomedical application development. Our findings reveal the complexity of H₂O₂ detection systems and identify the primary challenge of improving sensor performance to broaden their biological application. This analysis underscores the importance of a cross-sectoral strategy in advancing H₂O₂ sensor innovation, crucial for healthcare monitoring and environmental pollution tracking, thereby offering a comprehensive overview of the current state and future directions in H₂O₂ sensor research.