This PDF file contains the front matter associated with SPIE Proceedings Volume 11749, including the Title Page, Copyright information, and Table of Contents.

Chemical and biological agents continue to pose a threat to U.S. national security. The development of next generation of devices to sense, detect, filter, and remove these threats remains a priority of the U.S. Department of Defense. Zr(OH)₄ is currently being developed as a decontaminant and is being engineered into suitable forms capable of removing chemical warfare agents. As such, having a detailed understanding of the local structure of amorphous Zr(OH)₄ and heat treated ZrO₂ analogs is a crucial step in developing these compounds as suitable countermeasures against chemical threats. In this study, zirconium hydroxide powders were calcined at various temperatures to study the effects of porosity, surface area, structure, and electronic properties as a function of temperature. Different characterization techniques were used to demonstrate that the surface area and porosity decreased as the material changed from an amorphous phase to a monoclinic crystal structure. Analysis of X-ray pair distribution function data provided a detailed representation of the local structure of amorphous Zr(OH)₄ and its thermal decomposition into ZrO₂. Impedance analysis showed both the dielectric constants and capacitance decreased by two orders of magnitude as crystallinity increased, correlating to a lowered concentration of defects, such as surface hydroxyl groups that contribute to leakage current.

Publisher’s Note: This paper, originally published on 12 April 2021, was withdrawn on 5 May 2021 per author request.

This presentation describes implementations of remote chemical detection capabilities developed by Pendar Technologies, allowing safe identification of explosives and toxic materials by military personnel and first responders using unmanned ground and air vehicles. These integration efforts are based on the Pendar X10 system, a handheld standoff Raman chemical identification system. Following an update on recent improvements to the Pendar X10 platform, integration to both air and ground vehicles will be discussed. Our presentation will highlight the key design elements enabling successful remote chemical identification, as well as solutions developed specifically to facilitate integration to unmanned platforms.

We report chemical warfare agent (CWA and) Toxic Industrial Compound (TIC) detection with colorimetric dyes, dispersed in porous polymer coatings on long (>1 m) fibers, for lightweight remote fiber-based sensing exploiting optical fibers’ quality, length, and low weight/power requirements. Illuminating LED light travels longitudinally within the polymer fiber core and evanescently interacts with the colorimetric dyes, dispersed in a porous polymer cladding. Reflectivity and absorption, measured by a precise COTS sensor, change with small amounts of TICs or CWAs. The cladding regenerates its signal after ~15 minutes. Minimum Detectable Concentration is below 1 ppm for ammonia. We report chemical testing at 2 different facilities against 3 CWAs, 2 agent-relevant TICs, and ammonia as a control, and field trials. We plan applications in remote sensing, reconnaissance, perimeter and airborne sensing, detecting other toxic materials, and manufacturing. We acknowledge funding from DTRA.

Publisher’s Note: This paper, originally published on 12 April 2021, was withdrawn on 5 May 2021 per author request.

Building on our previous development of a compact, portable, and low SWaP gas analyzer (11” x 6.7” x 5.1”, 7.8 lbs) based on photoacoustic spectroscopy and using broadband quantum cascade laser arrays, we demonstrate here compositional analysis of airborne aerosols using this instrument. With an integration time of 330-ms per laser, and ~70 seconds for a spectrum covering 950-1500 cm^-1, our instrument showed a detection sensitivity at the mg/m³ level for solid and liquid-loaded solid aerosols. Additionally, Malathion-loaded aerosols can be discriminated from pure Syloid aerosols based on their absorption features. The preliminary results show a potential path for developments of a portable real-time aerosol composition analyzer.

Terrorists increasingly target crowded places, such as sporting and entertainment venues, visitor attractions and transport hubs, when planning mass-casualty attacks. The organisations responsible for these venues must therefore consider how to efficiently screen visitors and their carried bags for potential threats. In particular, screening at these sites needs to be carried out quickly, at low cost and with minimal interruption to the normal flow of commerce. We describe a multi-sensor threat detection system based on hybrid electromagnetic, ultrasound, microwave and optical detection techniques, which provides new screening capabilities and automated detection at low hardware and operating costs. Its compact size also enables deployment in many different locations and application areas. This system is one of very few technologies that can screen both people and their carried bags simultaneously for the presence of both metallic and non-metallic threats.

Publisher’s Note: This paper, originally published on 12 April 2021, was withdrawn on 5 May 2021 per author request.

Trace quantities of particles are generally left behind during the transport of explosive materials or from fingerprints of their handlers. We use a Deep-ultraviolet resonance Raman explosive detection (DURRED) sensor developed at the High Technology Foundation to study explosive trace samples with varying fill factors. In this study we show high sensitivity detection of very low-fill factor traces of KNO3, KClO3 and PETN. Receiver operating characteristics generated from samples (>20% fill) at a moderate signal-to-noise ratio of ~7 showed a probability of detection greater than >99.9%, a false acceptance rate of less than 2×10-4.

We report on an investigation addressing the challenge of the rapid detection of in-theatre surface chemical, biological and explosive (CBE) contaminants at a stand-off distance (<1m). The techniques we will describe are fundamentally underpinned by highly characteristic, molecule-specific Raman scattering. The implementation of Raman-at-range is problematic due to the extremely weak scattering cross-sections associated with this process, particularly when undertaken at the near-infrared wavelengths usually mandated by the need to suppress fluorescence. Excitation at shorter (near-UV) wavelengths can result in a two-order increase in scatter and this, combined with the extremely high throughput associated with Spatial Heterodyne Spectrometer (SHS) instrumentation, proves a viable route to Raman-at-range. We then implement time resolved spectral measurements on the ~100ps time scale to exploit the difference in generation timescale associated with Raman scatter and fluorescence generation; once so divorced the characteristics (both temporal and spectral) of the previously-troublesome fluorescent light can be embraced as an additional detection tool. We will show how SHS instrumentation, coupled with low-noise detector technology, can offer over four orders of magnitude improvement in spectral signal-to-noise level compared to conventional Czerny-Turner ‘slitted’ spectrometers using lower-cost linear CCD detectors. Finally, we show how a move to the deep-UV “Resonance-Raman” excitation region of sub- 250nm excitation leads both to enormous improvements in generated Raman signal, and spectral separation of the precious Raman from the troublesome fluorescence signal. We show the viability of this approach with biological spore simulant samples provided by DSTL.

Ultrafast 2D-IR spectroscopy has proved to be a powerful analytical tool for the detection and differentiation of Bacillus spores as dry films on surfaces. Here, we expand on these findings by employing 2D-IR spectroscopy to study spores from B. atrophaeus (BG) in aqueous solution. Specific vibrational modes attributable to the calcium dipicolinate trihydrate biomarker for spore formation were observed alongside distinctive off-diagonal spectral features that can be used to differentiate spores from different Bacillus species, indicating that 2D-IR has potential for use as a sensing platform with both solid and liquid phase samples. The ability of 2D-IR to enhance the protein amide I band relative to the overlapping water bending vibration was exploited to compare the nature of the protein component of spores to that of solution phase protein molecules. The vibrational lifetime for the amide I band of the BG spore in H₂O was 1.4 ± 0.1 ps, longer than those reported for the proteins in H₂O solution. The nature of a band at 1710 cm^-1 was also investigated. Collectively these results show the potential advantages of 2D-IR spectroscopy, with successful detection and classification of spores under different conditions being based on detailed molecular understanding of the spore state.

We report on a compact laser system for detection of hazardous biological agents by standoff coherent anti-Stokes Raman spectroscopy (CARS). The system is based on ytterbium-laser technology featuring broad spectral coverage and high sensitivity. High-quality CARS spectra have been obtained for NaDPA powder, a substitute for CaDPA, which is the Raman marker of bacterial spores. In addition, endospores of B. atrophaeus deposited over a glass substrate have been detected by their CARS signature at a standoff distance of 1 m and an integration time of 1 s. The system will be further developed for imaging of bacterial spores deposited over wide surface areas at standoff distances.

This presentation introduces advances realized at Pendar Technologies on the development of fieldable IR and Raman spectroscopy systems for the detection of residues on surfaces. In the first part of the presentation, we will focus on active standoff infrared hyperspectral imaging of small amounts of biological materials (BG and BT spores) deposited on surfaces using a compact quantum cascade laser array-based system. In the second part, we will discuss the development of a fieldable Raman microscopy system to autonomously analyze the composition of single particles deposited on surfaces. The compact system we developed can be used in the field by non-expert users.

Detection of antibodies to upper respiratory pathogens is critical to surveillance, assessment of the immune status of individuals, vaccine development, and basic biology. The urgent need for antibody detection tools has proven particularly acute in the COVID-19 era. Array-based tools are desirable as methods for assessing broader patterns of antigen-specific responses, as well as providing information on SARS-CoV-2 immunity in the context of pre-existing immunity to other viruses. Also, methods that rapidly and quantitatively detect antibody responses to SARS-CoV-2 antigens using small (fingerstick) quantities of blood are essential for monitoring immunity at a global scale. This talk will describe the development of two optical sensor platforms (Arrayed Imaging Reflectometry, and an integrated photonics platform fabricated at AIM Photonics) for quantifying antibodies to SARS-CoV-2 and other upper respiratory pathogens, and oriented towards the needs of multiplex detection and speed.

Access to medical monitoring in the field is primarily limited to physical or electrical measurements such as temperature, heart rate or respiration that readout a soldier’s immediate health. The ability to monitor biomarkers in combination with these measurement can uncover pathways leading to a health event and catch problems before the onset of symptoms. Our goal is to develop a fieldable, wearable PIC device for continuously monitoring small molecules and proteins. Continuous monitoring of biomarkers and physiological analytes is necessary for detecting abnormal fluctuations from baseline and provides real-time responses of human performance or injury. Wearable sensors for continuous monitoring require a combination of traits not seen in sample-based sensor systems including compact size, reversibility and sample replenishment. To accomplish this, we combine our expertise in sensors and photonic integrated circuits to generate a refractive index sensor platform. This approach takes advantage of sensing components and PIC devices to increase their sensitivity and selectivity for biomarkers while maintaining a small footprint required for practicality in the field. Here we report progress in striving to achieve a wearable sensor for continuously monitoring analytes in blood and sweat.

Monitoring electrochemical impedance changes due to aptamer/ligand binding on nanochannel surface provides an attractive sensing mechanism for chemical and biological species. We report a surface charge modulation based sensing mechanism for the detection of small molecules – amodiaquine and homoserine lactone with their respective specifically binding aptamers. The change in charge distribution due to aptamer/ligand binding modifies ionic transport across the nanochannel, results in transmembrane impedance changes proportional to the amount of target present, providing a quantitative response. A sensor reader based on an analog devices chip ADuCM355 was developed and used to monitor the nanoporous membrane's impedance changes over the desired frequency range. These results show that the combination of a low cost sensor reader and aptamer functionalized membrane will enable the development of a portable and inexpensive sensor system.

Alakai Defense Systems has recently developed what we believe is the first one-handed UV Raman sensor for standoff detection of chemicals, which we refer to as the Situational Awareness for First Responders (SAFR). SAFR detects bulk and residue quantities of material up to a range of 5 m. Since it is lightweight, SAFR can also be deployed on Unmanned Ground Vehicles (UGV’s) or Unmanned Aerial Vehicles (UAVs). A short description of the instrument’s design and performance will be presented.

We present a cart-based system based on infrared backscatter imaging spectroscopy (IBIS) for detecting and analyzing trace amounts of hazardous materials as particles on solid substrates. A system comprising four quantum cascade lasers rapidly scans through the mid-LWIR (6 μm – 11 μm) wavelength range to illuminate samples containing target analytes. The infrared backscatter signal is collected as a series of images to form a hyperspectral image cube. Each image is collected at a specified excitation wavelength using a liquid nitrogen cooled MCT focal plane array. The experimental results of this cart-based infrared illumination and backscatter detection are presented. Results compare imaged spectra over a range of different wavelength tuning speeds and different combinations of substrates and analytes. Camera frames are collected while the laser is sweeping through its wavelength range. A single complete analysis can be completed in less than 1 second. In every camera frame, each pixel of the 128x128 pixel camera array produces an individual intensity. These frames are then binned and assigned a discrete wavelength in steps, typically 0.01 μm, to produce a spectrum over 6 – 11 μm for each camera pixel. Target samples are prepared by sieving particles or by a dry transfer technique, to mimic particle size distributions associated with real world threats at trace levels, for explosives and illicit drugs on relevant substrates.

A key driver for the development of next-generation chemical sensors is the need to detect chemical hazards on various surfaces at a distance. By removing the operator from the immediate vicinity of the hazard, personnel safety is substantially improved. Such sensors need to correctly detect the presence of a hazardous target chemical, with minimal false alarms from common environmental interferents. These sensors ideally will be low cost, and able to provide rapid and reliable results with minimal processing. Here we describe recent advances in development of an infrared-based chemical sensor capable of detecting liquid chemical hazards on surfaces at proximal stand-off distances. This sensor, inspired by human color vision, uses only the response through three broadband infrared optical filters to discriminate between target chemicals and background interferents. We show computationally that a single set of three optical filters enable discrimination between several potential chemical nerve agent targets, simulants for these hazards, and common interferents. Finally, we demonstrate the capability of a bioinspired infrared optical sensor, incorporating a series of three infrared optical filters, to discriminate between chemicals for which the system was trained and those for which it was not. This bioinspired laboratory sensor utilizes only low-cost commercially available components, and can rapidly provide actionable detection results.

“Intelligent Systems”, machines that respond to the world around them and machines that interact with humans to change the dynamic of physical or social interaction. In the chemical, biological, radiological, nuclear, and explosives (CBRNE) detection world the focus is currently on the former definition with the advent of what some call “smart systems” based upon the common goal of creating CBRNE sensors that can respond and adapt to the environment in which they operate. Responses can be as simple as tipping and cuing of additional assets or resources to address changes in environment or operating conditions. Or on deeper level, the control systems and algorithms that operate/control these systems autonomously adapting to changes in both the operational characteristics and current conditions. Ideally a system could self-monitor its inherent capabilities and, for example, adjust dwell or sampling times base upon learned or defined characteristics. The concept of self-learning or machine learning within a sensor aligns with the current popularism of artificial intelligence (AI). However, within the CBRNE sensor community there is an inherent lack of the depth and breadth of data to actualize a functional AI to address these problems. In reality the information or data could be quite limited and the need to be able to operate anywhere in the world without long periods of acclimation must be stressed. Therefore CBRNE Intelligent Systems must be able to operate in a traditional sense, turn it on and function, and be adaptable to “long term” operations adjusting to both environmental and operational characteristic changes.

The detection of bulk materials is well-understood and many transduction methodologies exist. In contrast the detection of distributed or dispersed materials is still under study due the unique sequence of events under which this this occurs. For dispersed materials the problem is twofold, first you need to intercept or sample a location containing an analyte of interest and second you must be able to detect and identify that analyte. In addition, intercepting or sampling from sparsely contaminated areas is a more difficult problem as there is more background clutter due to less analyte available for interrogation and identification. Potential dispersed threats may include IED residues or disseminated materials dispersed in order contaminate an area with harmful chemicals. Using technologies such as Raman spectroscopy can provide real-time unique chemical-specific information to detect dispersed materials. However, understanding adequate sampling methods based on the instrument physical operation characteristics can help reduce false negatives and improve maneuverability through contested areas by bounding operational limitations. Since disseminated materials are deposited on a surfaces in a log-Normal fashion, the deposition pattern can be modeled and the potential ability to detect can be determined by understanding the probability of intercept of an analyte by the sampling method., i.e., for Raman the potential of a focused laser to illuminate an analyte containing location. The operating characteristics in question are the area of interrogation, repetition rate of the sampling method, and the speed at which the sampling is completed. In this paper, deposition patterns are modeled, and a CW Raman instrument is used to determine probability of intercept for several area-based concentrations, at different speeds, and with different interrogation areas. The data is analyzed based on both a predicted model and actual data. Determining and understanding these operating characteristics will aid in understanding of the necessary sampling, i.e., laser intercept, in order to provide desired confidence levels for detection.

Detecting chemical agents in outdoor environments such as a battlefield is made challenging by not only the spurious signatures from background chemicals and surfaces (e.g. asphalt, dirt, concrete), but also by the chemical transformation of the actual agents. The change of CW agents to other species can be catalyzed by other chemicals present in the scene, by different substrates, as well as by local weather conditions. Some of the final environmental transformation products are known (e.g. for the G agents methylphosphonic acid), but many of the intermediate chemical states are not, nor are the rates of transformation to the other intermediates or the end products. In this study we have made preliminary optical investigations into the degradation products of a G-agent intermediate, namely methylphosphonic anhydride and its rate of conversion to the more stable methylphosphonic acid. Using infrared and Raman spectroscopies, we have found that the relative humidity greatly affects the rate of change and we report first results from these studies.

The inherent wealth of information associated with hyperspectral data provides a data stream that could be leveraged for situational awareness or providing immediate user feedback. However, the enormous amount of data that is produced by some system’s data stream requires longer processing times and often post-processing techniques. Therefore, it is prudent to develop real-time hyperspectral processing techniques that are capable of operating at maneuver speeds. Anomaly detection techniques applied to higher order statistics of the hyperspectral data can provide immediate user feedback for awareness. Determining capabilities prior to applying directly to a system is also informative and provides an in silico point of reference. In this paper, we show, through the use of a real-time simulator (RTS) in the MATLAB environment, a method for simulating the processing speed of a data stream based on how data is received from the instrument. In this work, the RTS provides sub 100ms capabilities based on non-optimized code within the MATLAB environment and is largely limited by the write speed in MATLAB. Utilizing virtual memory and the flexibility of MATLAB allows for simulating real-time capabilities of already obtained hyperspectral data prior to implementing it on a device. Additionally, applying the algorithm to a simulated ground truth data provides a theoretical limit of anomaly detection (LOAD). We further compare theoretical LOADs with actual anomaly detection capabilities in a laboratory environment.

Binary mixtures of liquids may be encountered in industrial or remote sensing scenarios and present challenges to positive identification compared to neat single-component liquids. Our investigation examines whether one can predict the optical properties of the mixture, i.e. its complex index of refraction, by assuming a linear superposition of the real and imaginary components of the index of refraction in proportion to the ratio of each constituent. To investigate this hypothesis various liquid mixtures were created using mass ratios. The mixtures were then characterized as to their complex index of refraction and used in numerical modeling calculations of thin liquid mixture films on surfaces and compared with composed mixtures using linear n and k synthetic mixtures where the n and k components of the complex index of refraction were combined in similar ratios. The comparison of modeling and experimental results is presented with recommendations for further investigation.

A method for deriving the optical constants of organic powdered materials in the mid-infrared spectral range is introduced using both variable angle spectroscopic ellipsometry and transmission spectroscopy. The approach uses pressed pellets of the powder and is applied to organic solids, which have both strong and weak infrared absorption features. Many powders have significant voids and do not press into smooth, homogenous pellets. To account for pellet non-idealities and to accurately measure both n and k, three different forms of pellets were pressed and measured: A pure analyte pellet, a mixed analyte/KBr pellet with a large analyte percentage, and a KBr transmission pellet with only a small analyte percentage. Using all three pellets in a multi-sample analysis involving both ellipsometric and transmission data, the complex refractive index (n/k) values can be derived for many organic compounds. This method is illustrated to calculate the optical constants for anhydrous lactose from 6000-400 cm^-1. The transmission measurements improve the spectral fitting of weak absorption features, and the multi-sample analysis enables a better determination of the significant void space that is present in the pure pellet, leading to lower values for both n and k if not properly accounted for in the multi-oscillator model used to fit the ellipsometric data.

Knowledge of the bulk optical constants n and k of solids or liquids allows researchers to accurately predict the absorption, reflection, and scattering properties of materials for different physical forms. Indeed, chemically complex materials such as minerals can have an almost limitless variety of morphologies, particle sizes, shapes, and compositions, and the optical properties of such species can be predicted if the optical constants are known. For species such as minerals, there can be additional challenges due to e.g. hydration or dehydration during the course of the optical constants measurement. Here, we describe the protocols to obtain the bulk optical constants n and k of uranium-bearing minerals and ores such as uraninite or autunite. If quality n and k data are at hand, the (infrared) reflectance spectra can be predicted for different particle sizes and morphologies and the modeling results for various scenarios can be derived.

The development of alarm algorithms in ion mobility spectrometry (IMS) based chemical vapor detection is challenged by the presence of overlapping chemical peaks. IMS technology identifies a chemical through hard-coded alarm windows. Alarm windows are designed as range of reduced mobility values, and act as an IF-THEN statement. Where if a peak forms in the region it then assigns a preset alarm label. A majority of IMS alarm algorithm design has relied on setting boundary conditions based on a statistical variance in product ion peak positions. To develop these alarm windows for IMS detectors the variance in peak position had to be captured through extensive laboratory testing. These windows are determined through time consuming and rigorous laboratory testing across multiple detectors under multiple conditions. Machine learning (ML) is a field of science that intersects with computer science and mathematics to “teach” a computer using large amounts of data. The development of traditional alarm algorithms IMS has left a plethora of data available to be explored by ML techniques. Presented here is a random forest (RF) classification model along with a long short-term memory (LSTM) based neural network model to label the spectra of IMS data with high accuracy.

This study describes inverse spectral analysis of diffuse reflectance for surface-distributed material particles on substrates. In particular, an algorithm for extraction of target spectral features for surface-distributed materials of specified dielectric response. This algorithm is based on diffuse-reflectance theory and linear combinations of basis functions representing response characteristics of different types of scattering processes. The basis functions are constructed using absorbance functions and analytical models of Mie-type scattering. Prototype inverse spectral analysis of diffuse reflectance for surface-distributed explosive particles on substrates are described, which demonstrate characteristics of the algorithm.