This PDF file contains the front matter associated with SPIE Proceedings Volume 11416, including the Title Page, Copyright Information, and Table of Contents.

Hybrid metal-semiconductor systems are promising substrates for field Raman analysis due to their ability to use both electromagnetic and chemical enhancement pathways for surface enhanced Raman spectroscopy (SERS). Photo-induced Raman spectroscopy (PIERS) has previously been shown to be a promising method utilizing an additional enhancement route through photo-inducing atomic surface oxygen vacancies in photocatalytic metal-oxide semiconductors. The photoinduced vacancies can form vibronic coupling resonances, known as charge transfers, with analyte molecules, enhancing the signal beyond conventional SERS enhancements. However, conventional UV sources most often used for excitation of the PIERS substrate are impractical in combination with portable Raman systems for field analysis. In this work we show how a small UVC LED, centered at 255 nm, can replicate the same results previously reported with the benefit of allowing greater in-situ real time measurements under constant UV exposure. The UV LED source can be controlled more easily and safely, making it a practical UV source for field PIERS analysis.

Waveguide-enhanced Raman spectroscopy (WERS) enables the detection and identification of trace concentrations of vapor-phase analytes using a functionalized chip-scale photonic circuit. Here, we show that WERS signal can be collected from part-per-billion levels of targeted analytes in a backscatter geometry, which, compared to forward-scatter, simplifies component integration and is more tolerant of waveguide loss and modal interference. In addition, we discuss our progress towards a compact Raman sensing system that incorporates a handheld spectrometer and chip-scale optical filters. We demonstrate that a handheld, thermo-electrically cooled spectrometer can be used for backscatter WERS with a comparable signal-to-noise to that of a liquid-nitrogen cooled benchtop spectrometer. Finally, we describe efforts to integrate the dichroic Raman filter on-chip using arrays of unbalanced Mach-Zehnder interferometers. Measurements show filter performance sufficient for integration with WERS: Transmission of >80% of the laser in the cross port and Stokes signal in the through port; and extinction of the laser by >20 dB in the though port and of Stokes signal by >8 dB in the cross port.

The optical constants, n and k, are required to model the reflection, refraction and transmission of light at the first surface interface of a material, as well as its propagation through the material; here n corresponds to the real component and k the imaginary component of the refractive index. Several spectroscopic methods have been used to determine the n and k values for different materials, including single-angle reflectance spectroscopy and ellipsometry. The single-angle reflectance method quantitatively records the specular reflectance R(ṽ) from a plane parallel face of the material and uses the Kramers-Kronig transform to extract the n and k values. For most compounds, however, it is difficult to obtain a single crystal or high-quality window of sufficient planarity and of the appropriate dimensions (several mm) to make the measurement. For this reason, we further investigate the use of pressed pellets of neat powdered substances to measure optical constants of these substances using the single-angle reflectance method. We have found that surface roughness can significantly influence the measured quantitative reflectance spectrum R(ṽ) and, consequently, the derived n and k values. A collaborative study between Defence Research and Development Canada - Valcartier Research Center (DRDC-VRC) and Pacific Northwest National Laboratory (PNNL) has been carried out using different pellets of neat ammonium sulfate [(NH₄)₂SO₄] to show how parameters, such as the particle size composition and the grinding process, can affect the reflectance spectrum used to derive the optical constants. All pressed pellets were characterized by single-angle reflectance spectroscopy.

Inconsistent and unacceptable probability of detection (PD) and false alarm rates (FAR) due to varying environmental conditions hamper buried object detection. A 4-month study evaluated the environmental parameters impacting standoff thermal infra-red (IR) detection of buried objects. Field observations were integrated into a model depicting the temporal and spatial thermal changes through a 1-week period utilizing a 15 minute time-step interval. The model illustrates the surface thermal observations obtained with a thermal IR camera contemporaneously with a 3-d presentation of subsurface soil temperatures obtained with 156 buried thermocouples. Precipitation events and subsequent soil moisture responses synchronized to the temperature data are also included in the model simulation. The simulation shows the temperature response of buried objects due to changes in incoming solar radiation, air/surface soil temperature changes, latent heat exchange between the objects and surrounding soil, and impacts due to precipitation/changes in soil moisture. Differences are noted between the thermal response of plastic and metal objects as well as depth of burial below the ground surface. Nearly identical environmental conditions on different days did not always elicit the same spatial thermal response.

A compressive sensing hyperspectral imaging (CS-HSI) platform has been developed for low-cost, standoff, wide area Early Warning of chemical vapor plumes. The sensor, operating in the longwave infrared (LWIR) spectral range with a single-pixel architecture, simultaneously addresses two practical shortcomings of LWIR chemical plume imaging platforms: (1) the single pixel architecture enables an order of magnitude cost reduction relative to HSI sensors employing a cooled focal plane array or high-speed gimbaled scanner, and (2) the inherent imaging modality achieves a favorable pixel fill factor and associated probability of detection for relevant chemical threats relative to single pixel scanned sensors. The CS-HSI employs a low-cost digital micromirror device modified for use in the LWIR spectral range to spatially encode an image of the scene. An LWIR spectrometer employing a tunable Fabry-Perot filter and a mercury cadmium telluride single element photo-detector spectrally resolves the spatially integrated image while mitigating instrument radiance. A CS processing module reconstructs the spatially compressed hyperspectral image where the measurement and sparsity basis functions are specifically tailored to the CS-HSI hardware and chemical plume imaging. An automated target recognition algorithm is applied to the reconstructed hyperspectral data employing a variant of the Adaptive Cosine Estimator for the detection of the chemical plumes in cluttered and dynamic backgrounds. The development, characterization, and a series of capability demonstrations of a prototype CS-HSI sensor are presented. Capability demonstrations include chemical plume imaging of R-134 at mission-relevant concentration pathlength product levels in a laboratory setting.

Laser-based, long-wave infrared (LWIR) hyperspectral imaging systems are being developed for the standoff detection of trace chemicals on surfaces. Results of testing this technology at the Indianapolis Motor Speedway during the Indy500 will be presented. Also, we will describe two prototype systems that are being developed to address two different applications. One prototype will be mounted on a pan/tilt pointing system and be capable of detection at standoff distances between 5 and 30 m. A second system is being designed to screen parcels while they are being transported on a conveyor belt.

We present a portable broadband photoacoustic spectroscopic system for trace gas or aerosol detection using distributed feedback quantum cascade laser arrays. By sequentially firing 128 lasers, our system acquires a photoacoustic spectrum covering 565 cm^-1 (935-1500 cm^-1) with a normalized-noise-equivalent-absorption coefficient of 2.5×10^-9 cm⁻¹WHz^−1/2. In addition to sequential firing mode, the laser source can be operated in multiplexing mode. The firing sequence that determines when and which laser to be activated is programmable, which enables frequency-multiplexing excitation. For demonstration, we modulate 12 lasers at 12 distinct frequencies and a photoacoustic spectrum is acquired within 13 ms.

A stand-off photothermal sensor platform has been developed which combines two laser technologies: an external cavity Quantum Cascade Laser (EC-QCL), and a near-infrared laser Doppler vibrometer (NIR-LDV). The former is used as a 'pump' to induce vibrations/acoustic waves in the sample, whereas the latter is used to 'probe' these photothermal (PT) effects as the pump wavelength is varied; yielding spectral information on the target analyte. The EC-QCL uses an acousto-optic deflector (AOD) to obtain single-mode mid-infrared light of high output power with up to 1.6 µm of wavelength tuning. Using this AO based approach allows ultra-fast scanning across the full spectral bandwidth of the QCL gain chip at MHz rates, thus facilitating high speed identification of hazards. Initial validation of this pump-probe platform is demonstrated for the detection of 1,3-dinitrobenzene (DNB) and nitrobenzene (NB) on an aluminium substrate at a distance of several metres.

The proliferation of chemical threats in the modern world requires the development of new, low cost, flexible sensors, with the ability to be widely deployed. Ideally these sensors will be capable of both proximate and standoff sensing of threats (i.e. not require intimate contact with the chemical) without the need for highly trained/specialized operators. To meet this unique set of requirements, we are developing a bio-inspired chemical sensing method modeled on human color vision. This bio-inspired approach provides the ability to quickly and reliably discriminate between chemicals based on the interaction of infrared chemical absorption bands with selected broadband infrared filters. Here we describe the capabilities of a prototype bio-inspired sensor for standoff detection of threat chemicals on surfaces versus common interferents. We discuss the design and operation of the bio-inspired sensor and preliminary data demonstrating its ability to discriminate between a nerve agent simulant dimethyl methylphosphonate (DMMP) and the common background interferent US Army insect repellant (containing N,N-Diethyl-meta-toluamide (DEET)) in a laboratory environment. We discuss paths forward for this technology, as well as how the bio-inspired sensing approach can result in high-confidence discrimination of threats at proximate standoff distances.

This presentation introduces advances realized at Pendar Technologies on standoff detection of chemicals. We will first focus on improvements realized to our QCL array-based prototype for infrared hyperspectral imaging of small amounts of CWAs, with the addition of visible imaging to enhance detection confidence. We will then introduce the technological advances enabling Pendar X10, our handheld standoff Raman system, designed for the identification of bulk chemicals. New capabilities of this product will be discussed in the context of specific field measurement examples and the impact of these capabilities on user safety and measurement speed will be detailed. Finally, our presentation will close on recent results obtained with our Raman platform for the detection of smaller amount of materials.

A combination of experimental lidar results and shape-dependent scatter amplitude matrix calculations are used to explore the utility of polarimetric lidars for aerosol characterization. Solutions are developed for the induced polarization vector response of skewed spheroidal particles and the scatter is then computed using an improved anomalous diffraction approximation method. Experimental data was collected on biological simulant, chemical simulant, and interferent aerosol clouds using a 1047 nm micropulse lidar designed to measure the simultaneous depolarization using a linearly polarized source. Depolarization signatures obtained during testing show a clear difference between wet and dry biological simulant aerosols, wet chemical simulant releases, and some interferents. Combining these measurements with the shape-dependent model calculations help us understand the unique polarimetric signatures that may be exploited for aerosol characterization using stand-off lidar techniques.

Environmental sensors are employed for a variety of industrial safety and disaster response applications to detect emissions of concern that may indicate leaks, lack of regulatory compliance or illicit activities. Effusive emissions of interest include precursors, reaction intermediates and products, decomposition products, growth media, fermentation metabolites, solvents, and cleaners used in a variety of processes of interest. For all volatile organic compound/semivolatile organic compound (VOC/sVOC) sensors, a vital question is sensitivity to false alarms resulting from the presence of background clutter. To assess this sensitivity, “normal background” must be understood in terms of parameters such as: physical position (latitude, longitude, nearby geographical feature such as lakes, mountains, deserts); temporally varying factors (season, time of day, weather); and industrial features (surrounding population density, nearby presence of known or unknown activities that emit characteristic patterns of chemical effluent). It is only by understanding the scope and variability of the normal background that one can determine if it is possible for a given sensor modality to discriminate a specific industry or classes of industries from normal background either absolutely, through specific chemical indicators or indicator patterns, or as change detection, if the baseline of an area or area class is known. The Intelligence Advanced Research Projects Activity (IARPA) is conducting full VOC/sVOC mapping of a number of representative sites with varying climates, urban density, and local geographical features, as well as nearby contributing industries such as gas stations, a bakery, a brewery, a sewer treatment plant, and agricultural activities. This presentation will discuss the choice of sites, sampling methodology used, and preliminary comparative results.

Test and evaluation (T&E) of bioaerosol detectors presents unique measurement challenges, especially when evaluating systems designed to operate in complex battlefield environments. Equipment used to referee such T&E operations must be able to characterize the operating environment, including complex bioaerosol backgrounds, and the bioaerosol challenge presented to the detector. Test aerosols often represent only a fraction of the total atmospheric aerosol load. Therefore, selection of effective measurement equipment involves first developing an understanding of the natural background in environments where bioaerosol detectors may be used. It is also helpful to have a single platform for common sampling, so that multiple technologies may be evaluated under the same conditions for effective comparison. Such a platform was designed, characterized, and deployed under a cooperative development effort between the United States and Norway executed by the US Army Chemical Biological Center (CBC) and the Norwegian Defence Research Establishment (FFI). The testbed design is presented along with results from aerosol tests performed to document the sampling efficiency of the common inlet and isokinetic sampling manifold across a wide particle size range. The current testbed implementation hosts one Aerodynamic Particle Sizer (APS) and two ultraviolet light induced fluorescence (UVLIF) based bioaerosol particle counting and sizing instruments: the Ultra-Violet Aerodynamic Particle Sizer (UV-APS), and the Wideband Integrated Bioaerosol Sensor (WIBS). A comparative performance analysis of the two UV-LIF systems will be presented, as well as results describing the natural bioaerosol background at locations in both the US and Norway, including particle concentration, size distribution and fluorescence trends.

Detection, Identification and Monitoring (DIM) of hazardous chemical, biological, and radiological material is a critical component to Situational Awareness. Timely generated information just before and following a positive detection will lead to the most appropriate Course of Action (COA). The Technical Cooperation Program (TTCP) orchestrated a series of experiments to understand the operational limitations of new technologies in a Contested Urban Environment (CUE). One of the urban challenges occurred in Montreal, Canada in September 2018 where several technologies including a suite of biological DIM sensors were deployed. The urban environment adds complexity to the already challenging DIM task with potential line-of-sight limitations, changing wind conditions, complex communication spectrum, limited maneuverability, etc. The biological DIM suite deployed at this event included standoff, fixed point, mobile point and sampling, and identification sensing assets. The event revealed that the combination of various types of technologies might increase the overall system effectiveness. BioSense, a standoff technology, demonstrated its capacity to perform bio threat surveillance in urban environments having different constraints: short to long ranges; day and night operation; presence of various background sources; multiple surveillance areas without a deployment site having a line-of-sight on all of them and GPS-denied environment. The dedicated Chemical/Biological (CB) Sensor Data Viewer generated an integrated view of the available information from all sensors in real-time and provided a subset of this information to a central common operating software. The Class I mini UAS was equipped with an optical particle counter and filter collector membrane that was targeted to the appropriate location based on the cloud detected by the standoff sensor; and then, material classification obtained in near-real-time from the standoff spectral Laser Induced Fluorescence (LIF) interrogation was confirmed by simple post-processing of samples collected by the UAS.

A standoff biothreat detection and identification system for scanning large areas was designed, built and tested. The sensor is based on two wavelength ultraviolet light induced fluorescence (UVLIF) measured from a distance. The concept calls for multiple sensor modalities, fused to give the required overall performance. It makes use of multiple cameras, ambient light reflectance, high optical power and wavelength modulated UV LED illumination and synchronized fluorescence detection. A two-step operational mode is described along with results from independent demonstrations for each step. The first step is screening of the scene to recognize the surfaces that maximize the chances of biothreat detection and classification. This step used computer vision and artificial intelligence (semantic segmentation) for automation. The material constituting the surface is identified from color images. A second monochrome camera gives total “fluorescence” images excited with an intensity modulated 368nm UV illuminator. The second demonstration is scanning of slides (the “scene” in this case) from 1.2m away, threat detection (the spots on the slides) and classification via active multispectral fluorescence imaging at two different excitation wavelengths (280 and 368nm) and ambient light reflectance at up to 0.5m2/min. It is primarily the surface characteristics that drive the difficulty of the detection and classification of biological warfare agents (BWAs) on surfaces, along with the amount of BWA present on the surface. This presentation details the results obtained, the lessons learned and the envisioned way ahead.

Spore-forming bacteria that cause diseases pose a danger in our society. When in spore form, bacteria can survive high temperatures and resist a plethora of disinfection chemicals. Effective disinfection approaches are thus critical. Since a population of bacterial spores is heterogeneous in many aspects, single spore analyzing methods are suitable when heterogeneous information cannot be neglected. We present in this work a highresolution Laser Raman optical tweezers that can trap single spores and characterize their Raman spectra. We first evaluate our system by measuring Raman spectra of spores, and purified DNA and DPA. Thereafter, we expose Bacillus thuringiensis spores to peracetic acid, chlorine dioxide, and sodium hypochlorite, which are common disinfection chemicals. The data reveals how these agents change the constitutes of a spore over time, thus improving on the mode of action of these disinfection chemicals.

The ever-present threat of the accidental or nefarious release of hazardous chemicals on civilian and military personnel highlights the need for rapid, standoff detection systems. Our research was motivated by a class of hydrogen-bond acidic sorbents that have shown tremendous potential for use as preconcentrator materials for organophosphorus-based chemical warfare agents (CWA). In this work, a series of functionalized bisphenol AF (BPAF)-based hydrogen-bond (HB) acidic sorbents were developed with the aim of providing highly selective and rapid-uptake vapor-collection materials for infrared or other standoff-based detection techniques. Quartz crystal microbalance (QCM) absorption studies of the nerve agent simulant, dimethyl methyl phosphonate (DMMP) revealed strong but reversible binding where gas–sorbent partition coefficients (K) greater than 1 × 10⁸ (Log(K) >8) were observed. In addition, infrared spectroscopic techniques were used to evaluate the hydrogen-bonding characteristics of each sorbent in both dry air and upon exposure to nerve agent simulants vapors. Undesirable intermolecular sorbent–sorbent hydrogen bonding was eliminated in the BPAF sorbent containing bulky alkyl groups present at positions ortho- to the phenolic hydroxyls. Furthermore, we found that the BPAF-based sorbents exhibited unique infrared (IR) spectral signatures upon exposure to different chemicals, notably a characteristic redshift of the ν(O–H) mode, which was found to correlate to the hydrogen-bond basicity of the analyte vapor. By use of stand-off infrared-based sensing techniques coupled with the sorptive power of our hydrogen-bond acidic sorbents, we envision a simple path to the rapid detection of particularly hazardous chemicals with both class specificity as well as unique chemical identification capabilities.

Big Data processing tools have become increasingly powerful and have been applied to the area of personal chemical monitoring by companies such as Plume Labs and Rubix, requiring low-cost, capable sensors. The ubiquity of cell phone imagers has allowed for a revisiting of colorimetry as a viable chemical detection method. There has been a great deal of effort put into making colorimetric sensor arrays that can discriminate between a variety of analytes, but mainly in a qualitative sense with limited discussion regarding improving the performance of the sensor as a whole. However, these imaging devices have inherent limitations on their ultimate sensitivity. Other sensor configurations are being evaluated that can greatly enhance the sensitivity to a color change. Dye development continues in an effort to increase the specificity of the sensing event using currently available readout mechanisms, but what has been lacking has been a critical analysis of the readout mechanism for these molecular transducers. This work takes a quantitative approach to encourage a more rational design of colorimetric sensors with specific targets.

Chemical detection is a priority for the Intelligence Community (IC) with applications such as forensic analysis, border/facility protection, and stockpile/production monitoring. In particular, the IC has an interest in long term unattended monitoring for chemicals in the environment. The technology necessary for this type of monitoring must provide high sensitivity, selectivity, and accuracy, be robust in the presence of complex chemical mixtures, and be contained in a small, ruggedized package for autonomous operation. The Intelligence Advanced Research Projects Activity (IARPA) Molecular Analyzer for Efficient Gas-phase Low-power Interrogation (MAEGLIN) program is developing an ultra-low-power chemical analysis capability for the detection and identification of explosives, chemical weapons, industrial toxins and pollutants, narcotics, and nuclear materials in environments with significant background and interferent chemicals. In Phase 1 of the MAEGLIN program, performers developed individual component technologies for chemical collection, separation, and identification tasks, and in Phase 2, the integrated prototypes are being demonstrated. This presentation will discuss key advancements to date, including development of an array of micro ion trap mass spectrometers with dual photoelectric and electron impact ionization, a sector mass spectrometer with a charge coupled device (CCD) detector that uses permanent magnets for ion bending, a miniature tandem ion mobility spectrometer system with a wire grid fragmenter, adaptive multi-channel three dimensional gas chromatography, various active and passive microfabricated preconcentrators including one with a dedicated “hold and fire” stage for <0.25 second injection, and miniature electrostatic and Knudsen pumps. Three complementary end-to-end systems designs for VOC collection, analysis, and automated identification will also be described.

Combinatorial colorimetry has detected chemical compounds and uniquely identifies toxic industrial compounds at the ppmv level. We investigate colorimetric dyes, coated with polymers, on long fibers for lightweight remote/standoff fiber-based sensing (unmanned monitoring points, UAVs) exploiting optical fibers’ quality, length, low weight; retaining performance and low power needs. We model/ investigate an advanced longitudinal lightweight polymer fiber colorimetric sensing platform, sensitivity to fiber length, light propagation, and loss of light and sensitivity due to losses in the cladding, the spatial transient’s effect, and predict long lightweight fiber sensor sensitivity/selectivity. Visible light from illuminating LEDs travels along the polymer or silica fiber core and evanescently interacts with colorimetric dyes. Dyes are in porous, mechanically strong polymers with optimized thickness in order to enable analytes to reach the cladding/core interface yet mechanically strong.

The threat of exposure to toxic chemicals is of great concern. In order to provide a chemical situational awareness, we are developing a new type of chemical sensor based on a novel fabric spectrometer-based colorimetric chemical sensor that is low size, weight, and power (SWaP). We are exploring the key design principles for photonic transducers to enable a new approach to chemical threat sensing. The fabric spectrometer is based on a functional fiber platform in which the semiconductor-containing fiber is miniature in two dimensions and extendable in the third dimension (along the fiber length). By exploring fibers, and films that can be scaled to a fiber geometry, we will enable a new fiber-based chemical threat detector that can serve in textiles as well as other interesting form factors.

Metal oxides based materials are often employed for harsh environment applications since they tend to be stable at high-temperature and high-pressure conditions. In this work, TiO₂ and ZrO₂ based materials coated fiber optic pH sensors were evaluated with respect to pH sensitivity and stability by measuring the pH-dependent transmission at elevated temperature and high pH range, which are relevant to the wellbore cement monitoring. The TiO₂ thin film coated fiber optic sensor showed reversible pH sensitivity during the pH cycling between DI water and pH 12 at room temperature and 80 °C. It demonstrated improved stability and reversibility as compared to the SiO₂ or ZrO₂ based materials at 80 °C. Au-nanoparticles incorporated TiO2 coating was showed to maintain the pH sensing capability for about 30 hours at 80 °C. This finding is beneficial for future development and deployment of robust distributed optical pH sensors for harsh environment applications in the wellbores.

The gaseous (by)products generated from molten salt reactors need to be monitored to prevent the release of potentially toxic gases to the environment. In particular, ¹²⁹I has a long half-life and its toxicity and persistence in the environment make iodine and iodine-containing compounds (such as ICl from chloride containing molten salt systems) of great concern. Optical spectroscopy tools, including Raman and Fourier-transform infrared (FTIR) spectroscopies, are ideal for monitoring and quantifying such off-gas products. Iodine (I₂) has a strong and distinct Raman signature and the change in signal with a change in concentration can be used for quantification of these byproducts. Iodine monochloride (ICl) has distinct signatures in both the Raman and the infrared, and its spectrum can also potentially be used for quantitative measurement of this species. In this paper we discuss our recent results on the quantification of iodine monochloride using infrared spectroscopy, in particular first reports of the absolute infrared band strength of ICl.

A high performance infrared (HPIR) system was developed and demonstrated for the infrared absorption analysis of ²³⁵U and ²³⁸U isotopes in uranium hexafluoride gas samples. Sweeping the quantum cascade laser light source over the spectral range and sampling via a high-rate analog-to-digital converter provided 0.0005 cm^-1 spectral resolution, which allowed for high-precision measurements of the isotopic peak shift. A data analysis method was developed using principal component analysis to predict the isotope weight % content of 235U. The HPIR precision, accuracy, and error were evaluated for a wide range of isotopic ratio samples (0.287 – 93.7 weight % ²³⁵U), and the results were compared to the International Target Values (ITVS) set forth by the International Atomic Energy Agency (IAEA) for non-destructive and destructive analytical techniques. The method meets or surpasses the IAEA ITVs for non-destructive analysis of samples with isotopic content of depleted to highly enriched. The results also demonstrated the capability of the HPIR system to correctly predict the ²³⁵U weight % content of a mislabeled sample whose isotopic distribution was validated by mass spectroscopic measurements. The HPIR measurement is nondestructive and, thus, allows for confirmatory analyses of the exact sample at a designated IAEA lab if higher-resolution or a certified analysis is needed.

Identification of vegetation species and type is important in many chemical, biological, radiological, nuclear, and explosive sensing applications. For instance, emergence of non-climax species in an area may be indicative of anthropogenic activity which can complement prompt signatures for underground nuclear explosion detection and localization. To explore signatures of underground nuclear explosions, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of 274 visible and near-infrared wavebands over 4.3 km² of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. Previous work has shown that a vegetation spectral derivative can be more indicative of species than the measured value of each band. However, applying a spectral derivative amplifies any noise in the spectrum and reduces the benefit of the derivative analysis. Fitting the spectra with a polynomial can provide the slope information (derivative) without amplifying noise. In this work, we simultaneously capture slope and curvature information and reduce the dimensionality of remotely sensed hyperspectral imaging data. This is performed by employing a 2^nd order polynomial fit across spectral bands of interest. We then compare the classification accuracy of a support vector machine classifier fit to the polynomial dimensionality reduction technique and the same support vector machine fit to the same number of components from principle component analysis.

Raman hyperspectral imaging (RHSI) is an emerging chemical imaging technique that provides spectral and spatial information simultaneously in one measurement, and therefore can be a valuable tool for the detection and analysis of targets located in complex backgrounds. In particular, RHSI is useful for the detection and identification of threat materials (i.e., homemade and military-grade explosives) on the surfaces, where the concentration of target of interest could be very low and is typically found within complex scenery. Raman spectroscopy has the capability to provide a distinct molecular fingerprint of a threat material for unambiguous identification, can work at standoff distances (up to 100+ meters), and is capable of being conducted remotely, which makes it beneficial for installation into a stationary vehicle screening assembly. In spite of its numerous advantages, the implementation of Raman instrumentation for hyperspectral imaging is rather challenging owing to the low Raman scattering efficiency, potentially high SWaP constraints, and the need for a tightly focused laser spot that may result into the photo or thermal degradation of the sample. Some limitations can be overcome by utilizing the deep UV laser excitation, as the Raman cross section increases exponentially, i.e., ν4 with laser frequency. However, current generation UV Raman spectrometers require very narrow slit widths and long focal length optics, which means they have very low optical throughput, can be physically large and heavy, and can only probe an area the size of a tightly focused laser beam, eliminating the option to investigate large areas with defocused excitation. In addition, the use of focused laser excitation creates eye-safety concerns that restricts the usage of Raman sensors for most real-world applications To address these issues, ChemImage Sensor Systems (CISS) is developing a hyperspectral Raman imaging system capable of yielding high spectral resolution in a small form factor while using eye-safe, defocused laser excitation. This innovation combines a spatial heterodyne spectrometer (SHS), a slit-less spectrometer that operates similar to Michelson interferometer without requiring moving parts, with a fiber array spectral translator (FAST) fiber array, a twodimensional imaging fiber that provides spatial information of the target area. This combination of technologies, known as FAST-SHS that is compatible with deep UV excitation and creates a high throughput Raman hyperspectral imager capable of yielding very high spectral resolution measurements while simultaneously providing expanded area coverage and a faster search rate than traditional Raman systems. Recently, we have developed a FAST-SHS system consisting of fiber bundles with numerous fibers. FAST-SHS is the first spatial heterodyne Raman spectrometer to incorporate FAST technique that is capable of performing hyperspectral measurements at various standoff distances using defocused laser excitation. This paper will discuss the background of FAST-SHS technology for Raman hyperspectral imaging, the initial setup and design of the sensor, and provide initial detection results for wide area detection capabilities for the identification and analysis of threat targets.

The use of rapid scanning quantum cascade lasers in the detection of trace amounts of explosive materials is presented. This technique, infrared backscatter imaging spectroscopy (IBIS), utilizes an array of quick tuning infrared quantum cascade lasers (QCLs) to illuminate targets with mid-IR light, 6 – 11 μm in wavelength, to perform measurements in less than one second. The backscattered signal from targets is collected with a liquid nitrogen cooled MCT focal plane array. This information is stored in a hyperspectral image cube which is then run through a detection algorithm which has been trained on synthetic reflectance spectra of analytes of interest. We discuss the experimental parameters used with the QCLs and the focal plane array to generate and collect the infrared backscatter signal. The performance of the fast scanning QCL is presented in detail along with the experimental protocol used to collect high quality data from targets at proximal standoff distance. Camera frames are collected as the laser wavelength is swept and then are binned and assigned discrete wavelength steps. Spectra are extracted from the binned frames on a pixel by pixel basis. When run at full frame imaging, this results in over 16,000 individual spectra.

Threat chemicals such as explosives may persist on surfaces, enabling them to be detected by non-contact or standoff optical methods such as diffuse IR reflectance. However, due to particle size effects and optical coupling to the substrate, their IR spectral signatures will differ from laboratory reference measurements of bulk materials. This study presents an inverse analysis of diffuse IR reflectance from sparsely surface-distributed particles of the explosive PETN. A methodology using spectrum templets is applied for inverse analysis of measured spectra. The methodology is based on a generalization of extended multiplicative signal correction (EMSC). The results of this study demonstrate application of the inverse analysis methodology for extraction of spectral features for surface-distributed particles of specified dielectric response.

State-of-the-art CBRNe detection systems are predominantly available as standalone detectors, rarely offering the potential of networking and data fusion. This paper presents a novel CBRNe detection and identification system based on the network of heterogeneous sensor nodes. The system uses a novel data fusion algorithm combining data from the sensors, advanced machine-learning and modelling algorithms to significantly reduce false alarm rates. The situational awareness tools and training compounds supplement the system to provide innovative real capabilities for CBRNe practitioners.

Polarity is very important in developing materials with colossal dielectric. To meet the demands for the tunable devices and high dielectric parallel plate capacitors, several perovskites such as CaCu₃Ti₄O₁₂ (CCTO), La_2/3Cu₃Ti₄O₁₂ (LCTO) Pr_2/3Cu₃Ti₄ O₁₂ (PCTO) and several other materials of this class have been studied all over the world. Detailed studies showed that results vary a lot based on processing methods, such as powder vs. multi crystals and single crystals. In spite of great progress in processing, low resistivity and process driven variables in properties remain a big hurdle for its applications as a dielectric capacitor. We observed that dielectric values are significantly changed when these materials are exposed to chemicals and biological agents. We used parallel plate capacitor design for making chemical and biological sensors from CCTO member of this group. The data indicated huge difference in the dielectric and resistivity of the exposed samples.