This PDF file contains the front matter associated with SPIE Proceedings Volume 9073, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

We present the first demonstration of stand-off Fourier transform infrared spectroscopy using a broadband mid-infrared femtosecond optical parametric oscillator, with spectral coverage over 2700–3200 cm^-1. Remote spectroscopy and chemical detection from 2700–3100 cm^-1 is demonstrated for a thiodiglycol drop on concrete and anodized aluminum surfaces at a stand-off distance of 2 meters, as well as open-path spectroscopy of atmospheric water vapor from a concrete target at the same range. Comparison of the measured stand-off spectra with archived reference spectra for thiodiglycol and water vapor show good agreement. This technique provides greater spatial coherence and spectral brightness than a thermal source, and wider spectral coverage than a typical quantum-cascade laser, thereby presenting opportunities for application in the detection of industrial pollutants and the environmental identification of chemical warfare agents, explosives or other hazardous materials.

Methods for making total and diffuse directional/hemispherical reflectance measurements in the shortwave to longwave infrared using an integrating sphere are described. The sphere is a commercial, off-the-shelf optical device with its sample port at the bottom, which is essential for examining powdered samples without using a cover glass. The reflectance spectra of recently-developed National Institute of Standards and Technology (NIST, USA) infrared reflectance standards have been measured using the sphere. Reflectance spectra of other materials such as Spectralon and Infragold were also measured. The relative systematic error for the total reflectance measurements is estimated to be on the order of 3%, and random measurement error for multiple samples of each material is on the order of 0.5%.

Laser-based stand-off sensing of threat agents (e.g. explosives, toxic industrial chemicals or chemical warfare agents), by detection of distinct infrared spectral absorption signature of these materials, has made significant advances recently. This is due in part to the availability of infrared and terahertz laser sources with significantly improved power and tunability. However, there is a pressing need for a versatile, high performance infrared sensor that can complement and enhance the recent advances achieved in laser technology. This work presents new, high performance infrared detectors based on III-V barrier diodes. Unipolar barrier diodes, such as the nBn, have been very successful in the MWIR using InAs(Sb)-based materials, and in the MWIR and LWIR using type-II InAsSb/InAs superlattice-based materials. This work addresses the extension of the barrier diode architecture into the SWIR region, using GaSb-based and InAs-based materials. The program has resulted in detectors with unmatched performance in the 2-3 μm spectral range. Temperature dependent characterization has shown dark currents to be diffusion limited and equal to, or within a factor of 5, of the Rule 07 expression for Auger-limited HgCdTe detectors. Furthermore, D* values are superior to those of existing detectors in the 2-3 μm band. Of particular significance to spectroscopic sensing systems is the ability to have near-background limited performance at operation temperatures compatible with robust and reliable solid state thermoelectric coolers.

Photoacoustic spectroscopy (PAS) is a useful monitoring technique that is well suited for trace detection of gaseous and condensed media. We have previously demonstrated favorable PAS gas detection characteristics when the system dimensions are scaled to a micro-system design. This design includes quantum cascade laser (QCL)-based microelectromechanical systems (MEMS)-scale photoacoustic sensors that provide detection limits at parts-per-billion (ppb) levels for chemical targets. Current gas sensing research utilizes an ultra compact QCL, SpriteIR, in combination with a MEMS-scale photoacoustic cell for trace gas detection. At approximately one tenth the size of a standard commercially available QCL, SpriteIR is an essential element in the development of an integrated sensor package. We will discuss these results as well as the envisioned sensor prototype. Finally, expanding on our previously reported photoacoustic detection of condensed phase samples, we are investigating standoff photoacoustic chemical detection of these materials and will discuss preliminary results.

This work presents the development of a multi-input, multi-output neural network structure to predict the time dependent concentration of chemical agents as they participate in chemical reaction with environmental substrates or moisture content within these substrates. The neural network prediction is based on a computationally or experimentally produced database that includes the concentration of all chemicals presents (reactants and products) as a function of the chemical agent droplet size, wind speed, temperature, and turbulence. The utilization of this prediction structure is made userfriendly via an easy-to-use graphical user interface. Furthermore, upon the knowledge of the time-varying environmental parameters (wind speed and temperature that are usually recorded and available), the time varying concentration of all chemicals can be predicted almost instantaneously by recalling the previously trained network. The network prediction was compared with actual open air test data and the results were found to match.

A general-purpose multi-phase and multi-component computer model capable of solving the complex problems encountered in the agent substrate interaction is developed. The model solves the transient and time-accurate mass and momentum governing equations in a three dimensional space. The provisions for considering all the inter-phase activities (solidification, evaporation, condensation, etc.) are included in the model. The chemical reactions among all phases are allowed and the products of the existing chemical reactions in all three phases are possible. The impact of chemical reaction products on the transport properties in porous media such as porosity, capillary pressure, and permeability is considered. Numerous validations for simulants, agents, and pesticides with laboratory and open air data are presented. Results for chemical reactions in the presence of pre-existing water in porous materials such as moisture, or separated agent and water droplets on porous substrates are presented. The model will greatly enhance the capabilities in predicting the level of threat after any chemical such as Toxic Industrial Chemicals (TICs) and Toxic Industrial Materials (TIMs) release on environmental substrates. The model’s generality makes it suitable for both defense and pharmaceutical applications.

The very low Raman scattering cross section and the fluorescence background limit the measuring range of Raman based instruments operating in the visible or infrared band. We are exploring if laser excitation in the middle ultraviolet (UV) band between 200 and 300 nm is useful and advantageous for detection of persistent chemical warfare agents (CWA) on various kinds of surfaces. The UV Raman scattering from tabun, mustard gas, VX and relevant simulants in the form of liquid surface contaminations has been measured using a laboratory experimental setup with a short standoff distance around 1 meter. Droplets having a volume of 1 μl were irradiated with a tunable pulsed laser swept within the middle UV band. A general trend is that the signal strength moves through an optimum when the laser excitation wavelength is swept between 240 and 300 nm. The signal from tabun reaches a maximum around 265 nm, the signal from mustard gas around 275 nm. The Raman signal from VX is comparably weak. Raman imaging by the use of a narrow bandpass UV filter is also demonstrated.

A new method of deciphering obliterated writing is proposed in this paper. Inks with a peak at 1620 cm-1 were used for writing characters on paper, and information protection stamps were then used to obliterate them. Conventional detection methods consisting of visible and near-infrared spectroscopic photography and fluorescent photography of wavelength between 0.4–1.0 μm were compared with a novel method using infrared spectroscopic imaging with a 16-element mercury cadmium telluride array detector. The samples of obliterated writing could not be detected by the conventional methods, but could by the new method. Therefore, this new method is very useful for deciphering obliterated writing.

Photoacoustic spectroscopy (PAS) is a useful monitoring technique that is well suited for trace gas detection. This method routinely exhibits detection limits at the parts-per-million (ppm) or parts-per-billion (ppb) level for gaseous samples. PAS also possesses favorable detection characteristics when the system dimensions are scaled to a microelectromechanical system (MEMS) design. One of the central issues related to sensor miniaturization is optimization of the photoacoustic cell geometry, especially in relationship to high acoustical amplification and reduced system noise. Previous work relied on a multiphysics approach to analyze the resonance structures of the MEMS scale photo acoustic cell. This technique was unable to provide an accurate model of the acoustic structure. In this paper we describe a method that relies on techniques developed from musical instrument theory and electronic transmission line matrix methods to describe cylindrical acoustic resonant cells with side branches of various configurations. Experimental results are presented that demonstrate the ease and accuracy of this method. All experimental results were within 2% of those predicted by this theory.

In the last decades there have been several terroristic attacks with improvised explosive devices (IED) that have raised the need for new instrumentation, for homeland security applications, to obtain a reliable and effective fight against terrorism. Public transportation has been around for about 150 years, but terroristic attacks against buses, trains, subways, etc., is a relatively recent phenomenon [1]. Since 1970, transportation has been an increasingly attractive target for terrorists. Most of the attacks to transport infrastructures take place in countries where public transportation is the primary way to move. Terrorists prefer to execute a smaller-scale attack with certainty of success rather than a complex and demanding operation to cause massive death and destruction. [1]. Many commonly available materials, such as fertilizer, gunpowder, and hydrogen peroxide, can be used as explosives and other materials, such as nails, glass, or metal fragments, can be used to increase the amount of shrapnel propelled by the explosion. The majority of substances that are classified as chemical explosives generally contain oxygen, nitrogen and oxidable elements such as carbon and hydrogen [2]. The most common functional group in military explosives is NO2. That functionality can be attached to oxygen (ONO₂) in the nitrate esters (PETN), to carbon (C-NO₂) in the nitroarenes (TNT) and nitroalkanes (Nitromethane), and to nitrogen (N-NO₂) as in the nitramines (RDX). Some organic peroxides, such as TATP and HMTD, are popular amongst terrorists because they are powerful initiators that can be easily prepared from easily available ingredients. Azides are also powerful primary explosives commonly used as initiators (commercial detonators) in civilian and military operations, therefore they could be potentially used by terrorists as initiators for IEDs.

The following paper presents a realistic forensic capability test of an imaging Raman spectroscopy based demonstrator system, developed at FOI, the Swedish Defence Research Agency. The system uses a 532 nm laser to irradiate a surface of 25×25mm. The backscattered radiation from the surface is collected by an 8” telescope with subsequent optical system, and is finally imaged onto an ICCD camera. We present here an explosives trace analysis study of samples collected from a realistic scenario after a detonation. A left-behind 5 kg IED, based on ammonium nitrate with a TNT (2,4,6-trinitrotoluene) booster, was detonated in a plastic garbage bin. Aluminum sample plates were mounted vertically on a holder approximately 6 m from the point of detonation. Minutes after the detonation, the samples were analyzed with stand-off imaging Raman spectroscopy from a distance of 10 m. Trace amounts could be detected from the secondary explosive (ammonium nitrate with an analysis time of 1 min. Measurement results also indicated detection of residues from the booster (TNT). The sample plates were subsequently swabbed and analyzed with HPLC and GC-MS analyses to confirm the results from the stand-off imaging Raman system. The presented findings indicate that it is possible to determine the type of explosive used in an IED from a distance, within minutes after the attack, and without tampering with physical evidence at the crime scene.

A key challenge for standoff explosive sensors is to distinguish explosives, with high confidence, from a myriad of unknown background materials that may have interfering spectral peaks. To meet this challenge a sensor needs to exhibit high specificity and high sensitivity in detection at low signal-to-noise ratio levels. We had proposed a Dual-Excitation- Wavelength Resonance-Raman Detector (DEWRRED) to address this need. In our previous work, we discussed various components designed at WVHTCF for a DEWRRED sensor. In this work, we show a completely assembled laboratory prototype of a DEWRRED sensor and utilize it to detect explosives from two standoff distances. The sensor system includes two novel, compact CW deep-Ultraviolet (DUV) lasers, a compact dual-band high throughput DUV spectrometer, and a highly-sensitive detection algorithm. We choose DUV excitation because Raman intensities from explosive traces are enhanced and fluorescence and solar background are not present. The DEWRRED technique exploits the excitation wavelength dependence of Raman signal strength, arising from complex interplay of resonant enhancement, self-absorption and laser penetration depth. We show measurements from >10 explosives/pre-cursor materials at different standoff distances. The sensor showed high sensitivity in explosive detection even when the signalto- noise ratio was close to one (~1.6). We measured receiver-operating-characteristics, which show a clear benefit in using the dual-excitation-wavelength technique as compared to a single-excitation-wavelength technique. Our measurements also show improved specificity using the amplitude variation information in the dual-excitation spectra.

Photon Systems in collaboration with JPL is continuing development of a new technology robot-mounted or hand-held sensor for reagentless, short-range, standoff detection and identification of trace levels chemical, biological, and explosive (CBE) materials on surfaces. This deep ultraviolet CBE sensor is the result of Army STTR and DTRA programs. The evolving 10 to 15 lb, 20 W, sensor can discriminate CBE from background clutter materials using a fusion of deep UV excited resonance Raman (RR) and laser induced native fluorescence (LINF) emissions collected is less than 1 ms. RR is a method that provides information about molecular bonds, while LINF spectroscopy is a much more sensitive method that provides information regarding the electronic configuration of target molecules. Standoff excitation of suspicious packages, vehicles, persons, and other objects that may contain hazardous materials is accomplished using excitation in the deep UV where there are four main advantages compared to near-UV, visible or near-IR counterparts. 1) Excited between 220 and 250 nm, Raman emission occur within a fluorescence-free region of the spectrum, eliminating obscuration of weak Raman signals by fluorescence from target or surrounding materials. 2) Because Raman and fluorescence occupy separate spectral regions, detection can be done simultaneously, providing an orthogonal set of information to improve both sensitivity and lower false alarm rates. 3) Rayleigh law and resonance effects increase Raman signal strength and sensitivity of detection. 4) Penetration depth into target in the deep UV is short, providing spatial/spectral separation of a target material from its background or substrate. 5) Detection in the deep UV eliminates ambient light background and enable daylight detection.

A stand-off Raman imaging system for the identification of explosive traces was modified for the analysis of substances in containers which are non-transparent to the human eye. This extends its application from trace detection of threat materials to the investigation of suspicious container content. Despite its limitation to containers that are opaque to the facilitated laser, the combination of Spatial Offset Raman Spectroscopy (SORS) with stand-off Raman imaging allows to collect spectral data from a broad range of different spatial offsets simultaneously. This is a significant advantage over SORS with predefined offset, since the ideal offset is unknown prior to the measurement and depends on the container material as well as the sample content. Here the detection of sodium chlorate in a white plastic bottle is shown. A 532nm-laser (pulse length 5ns, repetition 50kHz) was focused to a diameter of 10mm at 10m. A 1500mm Schmidt-Cassegrain telescope with a 152.4mm diameter collected the scattered light. An edge filter removed inelastically scattered laser light and a liquid crystal tunable filter was used to select 0.25nm broad wavelength ranges between 480 and 720nm. The sample area of 50×50mm was imaged on 1024×1024 pixels of an ICCD camera. For the conducted experiments an ICCD gate time of 5ns was selected and 70μJ-laser pulses were accumulated during 1s for each wavelength.

Rapid identification and source attribution of homemade explosives (HMEs) is vital to national defense and homeland security efforts. Since HMEs can be prepared in a variety of methods with different component ingredients, telltale traces can be left behind in the final structural form of the material. These differences manifest as polymorphs, isomers, conformers or even contaminants that can all impact the low energy vibrational modes of the molecule. Conventional Raman spectroscopy systems confine their measurements to the “chemical fingerprint” region and are unable to detect low frequency Raman signals (<200cm^-1) where these low energy modes are found. This gap in sensitivity limits the conclusions that can be drawn from a single Raman measurement and creates the need for multiple measurement techniques to confirm any results. We present results from a new rugged, portable approach that is capable of extending the range of Raman to include these low frequency signals down to ~5cm^-1, plus complementary anti-Stokes spectra, with measurement times on the order of seconds. We demonstrate the diversity of signals that lie in this region that directly correlate to the molecular structure of the material, resulting in a new Raman “structural fingerprint” region. By correlating the measured results with known samples from a spectral library, rapid identification of the specific method of manufacture can be made.

Current stand-off hyperspectral imaging detection solutions that operate in the mid-wave infrared (MWIR), nominally 2.5 – 5 μm spectral region, are limited by the number of absorption bands that can be addressed. This issue is most apparent when evaluating a scene with multiple absorbers with overlapping spectral features making accurate material identification challenging. This limitation can be overcome by moving to the long wave IR (LWIR) region, which is rich in characteristic absorption features, which can provide ample molecular information in order to perform presumptive identification relative to a spectral library. This work utilises an instrument platform to perform negative contrast imaging using a novel LWIR optical parametric oscillator (OPO) as the source. The OPO offers continuous tuning in the region 5.5 – 9.5 μm, which includes a number of molecular vibrations associated with the target material compositions. Scanning the scene of interest whilst sweeping the wavelength of the OPO emission will highlight the presence of a suspect material and by analysing the resulting absorption spectrum, presumptive identification is possible. This work presents a selection of initial results using the LWIR hyperspectral imaging platform on a range of white powder materials to highlight the benefit operating in the LWIR region compared to the MWIR.

We report our preliminary results on numerical modeling of IR back-scattering (reflectivity) and absorption (photothermal) IR signatures of micron-size (5-20 μm) particles. We use the Mie scattering theory which is an exact solution of the scattering problem for spherical particles of arbitrary size. In this paper, we approximate the particles as spheres with an equivalent volume. While we expect particle shape to influence IR spectra (albeit to a lesser extent), in this paper, we restrict ourselves to the effect of size (i.e. particle diameter) only. We also study the effect of air, solvent and other additive inclusions on the IR spectra. Finally, we address the effect of particle surface roughness. We show that all these parameters (size, inclusions, roughness) affect the scattering process which results in distortions to the IR spectra as compared to library values for bulk material. The effect of substrate on the IR spectra is not studied due to the limitations of the Mie scattering theory which was developed for isolated particles only. In addition to particle-related effects on IR spectra, the presence of substrate will have additional effects as well and this was studied previously by other research groups. We expect that the results of this study will help improve the performance of various detection algorithms by accounting for changing IR spectra.

In order to combat the threat of emplaced explosives (land mines, etc.), ChemImage Sensor Systems (CISS) has developed a multi-sensor, robot mounted sensor capable of identification and confirmation of potential threats. The system, known as STARR (Shortwave-infrared Targeted Agile Raman Robot), utilizes shortwave infrared spectroscopy for the identification of potential threats, combined with a visible short-range standoff Raman hyperspectral imaging (HSI) system for material confirmation. The entire system is mounted onto a Talon UGV (Unmanned Ground Vehicle), giving the sensor an increased area search rate and reducing the risk of injury to the operator. The Raman HSI system utilizes a fiber array spectral translator (FAST) for the acquisition of high quality Raman chemical images, allowing for increased sensitivity and improved specificity. An overview of the design and operation of the system will be presented, along with initial detection results of the fusion sensor.

Passive, standoff detection of chemical, explosive and narcotic threats employing widefield, shortwave infrared (SWIR) hyperspectral imaging (HSI) continues to gain acceptance in defense and security fields. A robust and user-friendly portable platform with such capabilities increases the effectiveness of locating and identifying threats while reducing risks to personnel. In 2013 ChemImage Sensor Systems (CISS) introduced Aperio, a handheld sensor, using real-time SWIR HSI for wide area surveillance and standoff detection of explosives, chemical threats, and narcotics. That SWIR HSI system employed a liquid-crystal tunable filter for real-time automated detection and display of threats. In these proceedings, we report on a next generation device called VeroVision™, which incorporates an improved optical design that enhances detection performance at greater standoff distances with increased sensitivity and detection speed. A tripod mounted sensor head unit (SHU) with an optional motorized pan-tilt unit (PTU) is available for precision pointing and sensor stabilization. This option supports longer standoff range applications which are often seen at checkpoint vehicle inspection where speed and precision is necessary. Basic software has been extended to include advanced algorithms providing multi-target display functionality, automatic threshold determination, and an automated detection recipe capability for expanding the library as new threats emerge. In these proceedings, we report on the improvements associated with the next generation portable widefield SWIR HSI sensor, VeroVision™. Test data collected during development are presented in this report which supports the targeted applications for use of VeroVision™ for screening residue and bulk levels of explosive and drugs on vehicles and personnel at checkpoints as well as various applications for other secure areas. Additionally, we highlight a forensic application of the technology for assisting forensic investigators in locating bone remains in a cluttered background environment.

Fast and safe detection methods of explosive substances are needed both before and after actualized explosions. This article presents an experiment of the detection of three selected explosives by the ATR FTIR spectrometer and by three different IR hyperspectral imaging devices. The IR spectrometers give accurate analyzing results, whereas hyperspectral imagers can detect and analyze desired samples without touching the unidentified target at all. In the controlled explosion experiment TNT, dynamite and PENO were at first analyzed as pure substances with the ATR FTIR spectrometer and with VNIR, SWIR and MWIR cameras. After three controlled explosions also the residues of TNT, dynamite and PENO were analyzed with the same IR devices. The experiments were performed in arctic outdoor conditions and the residues were collected on ten different surfaces. In the measurements the spectra of all three explosives were received as pure substances with all four IR devices. Also the explosion residues of TNT were found on cotton with the IR spectrometer and with VNIR, SWIR and MWIR hyperspectral imagers. All measurements were made directly on the test materials which had been placed on the explosion site and were collected for the analysis after each blast. Measurements were made with the IR spectrometer also on diluted sample. Although further tests are suggested, the results indicate that the IR spectrography is a potential detection method for explosive subjects, both as pure substances and as post-blast residues.

In this work we deposit particles of explosives by different techniques including sieving, inkjetting, and pipetting and monitor their sublimation by photo/video-microscopy. We compare and contrast the effect of deposition technique on particle fabrication and subsequent sublimation or related effects influencing particle lifetime. Analysis of 2D microscopic images is used to compute and track particle size and geometrical characteristics as functions of time and experimental test conditions. In this preliminary work a limited set of test conditions were used including variable airflow rates and fixed humidity, temperature and substrate type.

We perform laser-induced photodissociation fluorescence spectroscopy on mononitrotoluenes (MNTs) and dinitrotoluenes (DNTs) in the vapor phase and observe the spectrally overlapping fluorescence from nitric oxide (NO) and carbon (C). Energy-dispersive x-ray spectroscopy (EDS) and Raman spectroscopy of deposits found in the sample chamber confirm the presence of carbon. By comparing the observed fluorescence intensities with the Franck-Condon factors for NO, we are able to identify the presence or absence of fluorescence from carbon. 2-nitrotoluene and 4- nitrotoluene show carbon fluorescence for gate delays of up to 500 ns, while 2,4-dinitrotolune, 3,4-dinitrotolune, and 2,6-dinitrotolune show carbon fluorescence for gate delays of at least up to 1500 ns. The spectroscopic signal from atomic carbon in the vapor phase is observed at concentrations as low as 10 ppt. Based upon the observed S/N, detection at even lower concentrations appears feasible. Several non-nitrotoluene molecules including nitrobenzene, benzene, toluene, and CO₂, are tested under identical conditions, but do not show any carbon emission. The presence of extra NO (simulation of NO pollutants) in the samples improves the S/N ratio for the detection of carbon. Energy transfer from laser-excited molecular nitrogen to NO, multiple decomposition channels in the electronic excited state of the nitrotoluene molecules, and interaction of NO with the excited-state decomposition process of the nitrotoluene molecules may all play a role.

Nonstandard and home-made explosives may constitute a considerable threat and as well as a potential material for terrorist activities. Mobile analytical devices, particularly Raman, or also FTIR spectrometers are used for the initial detection. Various sorts of phlegmatizers (moderants) to decrease sensitivity of explosives were tested, some kinds of low viscosity lubricants yielded very good results. If the character of the substance allows it, phlegmatized samples are taken in the amount of approx.0.3g for a laboratory analysis. Various separation methods and methods of concentrations of samples from post-blast scenes were tested. A wide range of methods is used for the laboratory analysis. XRD techniques capable of a direct phase identification of the crystalline substance, namely in mixtures, have highly proved themselves in practice for inorganic and organic phases. SEM-EDS/WDS methods are standardly employed for the inorganic phase. In analysing post-blast residues, there are very important techniques allowing analysis at the level of separate particles, not the overall composition in a mixed sample.

Transport Canada (TC), the Canadian Armed Forces, and other public security agencies have an interest in the assessment of the potential utility of advanced explosives detection technologies to aid in the detection and interdiction of commercial grade, military grade, and homemade or improvised explosives (HME or IE). The availability of suitable, non-hazardous, non-toxic, explosive simulants is of concern when assessing the potential utility of such detection systems. Lack of simulants limits the training opportunities, and ultimately the detection probability, of security personnel using these systems. While simulants for commercial and military grade explosives are available for a wide variety of detection technologies, the design and production of materials to simulate improvised explosives has not kept pace with this emerging threat. Funded by TC and the Canadian Safety and Security Program, Defence Research and Development Canada (DRDC), Visiontec Systems, and Optosecurity engaged in an effort to develop inert, non-toxic Xray interrogation simulants for IE materials such as ammonium nitrate, potassium chlorate, and triacetone triperoxide. These simulants were designed to mimic key X-ray interrogation-relevant material properties of real improvised explosives, principally their bulk density and effective atomic number. Different forms of the simulants were produced and tested, simulating the different explosive threat formulations that could be encountered by front line security workers. These simulants comply with safety and stability requirements, and as best as possible match form and homogeneity. This paper outlines the research program, simulant design, and validation.

Scattering patterns are made available by the TAOS (Two-dimensional Angle-resolved Optical Scattering) method, which consists of detecting micrometer-sized single airborne aerosol particles and collecting the intensity of the light they scatter from a pulsed, monochromatic laser beam. TAOS patterns have been classified by a learning machine, the training stage of which depends on many control parameters. Patterns due to single bacterial spores (Bq class) have to be discriminated from those produced by outdoor aerosol particles (Kq set) and diesel soot aggregates (sq set), where both Kq and sq are assumed not to contain patterns of bacterial origin. This work describes two directions along which classification continues to develop: the enlargement of the control parameter set and the simultaneous processing of two areas (sectors) selected from the TAOS pattern. The latter algorithm is meant to make the classifier sensitive to simmetry exhibited by some patterns. The available classification scheme is summarized, as well as the rule by which discrimination is rated off-line. Discrimination based on one pattern sector alone scores fewer than 15% false negatives (misclassified Bq patterns) and false positives from Kq and sq. Discrimination based on the symmetry of two pattern sectors fails to recognize 30% of the Bq (bacterial) patterns, whereas < 5% Kq (environmental) patterns are assigned to the Bq class; false positives from sq (diesel) patterns drop to zero. The issue of false positives is briefly discussed in relation to the fraction of airborne bacteria found in aerosols.

The challenges of detecting hazardous biological materials are manifold: Such material has to be discriminated from other substances in various natural surroundings. The detection sensitivity should be extremely high. As living material may reproduce itself, already one single bacterium may represent a high risk. Of course, identification should be quite fast with a low false alarm rate. Up to now, there is no single technique to solve this problem. Point sensors may collect material and identify it, but the problems of fast identification and especially of appropriate positioning of local collectors are sophisticated. On the other hand, laser based standoff detection may instantaneously provide the information of some accidental spillage of material by detecting the generated thin cloud. LIF technique may classify but hardly identify the substance. A solution can be the use of LIF technique in a first step to collect primary data and – if necessary- followed by utilizing these data for an optimized positioning of point sensors. We perform studies on an open air laser test range at distances between 20 and 135 m applying LIF technique to detect and classify aerosols. In order to employ LIF capability, we use a laser source emitting two wavelengths alternatively, 280 and 355 nm, respectively. Moreover, the time dependence of fluorescence spectra is recorded by a gated intensified CCD camera. Signal processing is performed by dedicated software for spectral pattern recognition. The direct comparison of all results leads to a basic classification of the various compounds.

Man has used poisons for assassination purposes ever since the dawn of civilization, not only against individual enemies but also occasionally against armies. According to (Frischknecht, 2003)11 article on the History of Biological Warfare, during the past century, more than 500 million people died of infectious diseases. Several tens of thousands of these deaths were due to the deliberate release of pathogens or toxins. Two international treaties outlawed biological weapons in 1925 and 1972, but they have largely failed to stop countries from conducting offensive weapons research and large-scale production of biological weapons. Before the 20th century, biological warfare took on three main forms: (1) deliberate poisoning of food and water with infectious material, (2) use of microorganisms or toxins in some form of weapon system, and (3) use of biologically inoculated fabrics (Dire, 2013)8. This action plan is aimed at the recognition of the lack of current processes in place under an unidentified lead agency to detect, identify, track, and contain biological agents that can enter into the United States through a human host. This action plan program has been identified as the Consumer of Concern Early Entry Program or a simpler title is C-CEEP.

The ability to integrate complete assays on a microfluidic chip helps to greatly simplify instrument requirements and allows the use of lab-on-a-chip technology in the field. A core application for such field-portable systems is the detection of pathogens in a CBRN scenario such as permanent monitoring of airborne pathogens, e.g. in subway stations or hospitals etc. An immunological assay was chosen as method for the pathogen identification. The conceptual approach was its realization as a lab-on-a-chip system, enabling an easy handling of the sample in an automated manner. The immunological detection takes place on an antibody array directly implemented in the microfluidic network. Different immobilization strategies will be presented showing the performance of the system. Central elements of the disposable microfluidic device like fluidic interface, turning valves, liquid introduction and waste storage, as well as the architecture of measurement and control fluidic network, will be introduced. Overall process times of about 30 minutes were achieved and assays for the detection of Francisella tularensis and Yersinia pestis are presented. An important feature of the integrated lab-on-a-chip approach is that all waste liquids remain on-chip and contamination risks can be avoided.

Results related to laser induced breakdown spectroscopy (LIBS) as an analytical tool for applications regarding CWA and BWA detection/monitoring will be presented and discussed in this paper. A ‘real-time’ aerosol analysis set-up using LIBS on single μm-sized particles (sampled from ambient air into a particle stream) has been developed and evaluated. Here, a two-stage triggering unit ensures a high hit-rate of the sampled aerosol particles and the optical emission from the laser induced plasma is collected and coupled into an echelle spectrometer equipped with an intensified CCD detector. Each CCD image (echellogram), optimally originating from a single μm-sized particle, is then analyzed and the result treated by an alarm algorithm built from a database using multivariate data analysis. The database signatures of simulant agents and interferents were obtained in controlled atmospheres (aerosol chamber/wind tunnel) as well as from measurements in different ambient background. The LIBS bioaerosol system with alarm algorithm was also tested in ‘real-life’ settings (subway station) during simulant dispersions. Painted surfaces have also been analyzed by LIBS to obtain information about residues of organophosphates on, or within, the paint. Depth analysis has been performed, which illustrated the possibility to monitor diffusion and penetration behavior of neat CWAs and simulant chemicals in the paint layer by following the intensity of phosphorous emission lines in single shot LIBS spectra as function of number of laser pulses. In addition, LIBS analysis was also performed after simple ethanol decontamination procedures, after which P emission lines still could be observed. The possibilities and challenges associated with the different set-ups and applications will be briefly discussed in connection with the presented results.

Since the distribution of Bacillus anthracis-Ames spores through the US Postal System, there has been a persistent fear that biological warfare agents will be used by terrorists against our military abroad and our civilians at home. While there has been substantial effort since the anthrax attack of 2001 to develop analyzers to detect this and other biological warfare agents, the analyzers remain either too slow, lack sensitivity, produce high false-positive rates, or cannot be fielded. In an effort to overcome these limitations we have been developing a surface-enhanced Raman spectroscopy system. Here we describe the use of silver nanoparticles functionalized with a short peptide to selectively capture Bacillus anthracis spores and produce SER scattering. Specifically, measurements of 100 B. anthracis-Ames spores/mL in ~25 minutes performed at the US Army’s Edgewood Chemical Biological Center are presented. The measurements provide a basis for the development of systems that can detect spores collected from the air or water supplies with the potential of saving lives during a biological warfare attack.

Technology development efforts seek to increase the capability of detection systems in low Signal-to-Noise regimes encountered in both portal and urban detection applications. We have recently demonstrated significant performance enhancement in existing Advanced Spectroscopic Portals (ASP), Standoff Radiation Detection Systems (SORDS) and handheld isotope identifiers through the use of new advanced detection and identification algorithms. The Poisson Clutter Split (PCS) algorithm is a novel approach for radiological background estimation that improves the detection and discrimination capability of medium resolution detectors. The algorithm processes energy spectra and performs clutter suppression, yielding de-noised gamma-ray spectra that enable significant enhancements in detection and identification of low activity threats with spectral target recognition algorithms. The performance is achievable at the short integration times (0.5 – 1 second) necessary for operation in a high throughput and dynamic environment. PCS has been integrated with ASP, SORDS and RIID units and evaluated in field trials. We present a quantitative analysis of algorithm performance against data collected by a range of systems in several cluttered environments (urban and containerized) with embedded check sources. We show that the algorithm achieves a high probability of detection/identification with low false alarm rates under low SNR regimes. For example, utilizing only 4 out of 12 NaI detectors currently available within an ASP unit, PCS processing demonstrated P_d,ID > 90% at a CFAR (Constant False Alarm Rate) of 1 in 1000 occupancies against weak activity (7 - 8μCi) and shielded sources traveling through the portal at 30 mph. This vehicle speed is a factor of 6 higher than was previously possible and results in significant increase in system throughput and overall performance.

Muon tomography uses naturally occurring cosmic rays to detect nuclear threats in containers. Currently there are no systematic image characterization metrics for muon tomography. We propose a set of image characterization methods to quantify the imaging performance of muon tomography. These methods include tests of spatial resolution, uniformity, contrast, signal to noise ratio (SNR) and vertical smearing. Simulated phantom data and analysis methods were developed to evaluate metric applicability. Spatial resolution was determined as the FWHM of the point spread functions in X, Y and Z axis for 2.5cm tungsten cubes. Uniformity was measured by drawing a volume of interest (VOI) within a large water phantom and defined as the standard deviation of voxel values divided by the mean voxel value. Contrast was defined as the peak signals of a set of tungsten cubes divided by the mean voxel value of the water background. SNR was defined as the peak signals of cubes divided by the standard deviation (noise) of the water background. Vertical smearing, i.e. vertical thickness blurring along the zenith axis for a set of 2 cm thick tungsten plates, was defined as the FWHM of vertical spread function for the plate. These image metrics provided a useful tool to quantify the basic imaging properties for muon tomography.

Image resolution of muon tomography is limited by the range of zenith angles of cosmic ray muons and the flux rate at sea level. Low flux rate limits the use of advanced data rebinning and processing techniques to improve image quality. By optimizing the limited angle data, however, image resolution can be improved. To demonstrate the idea, physical data of tungsten blocks were acquired on a muon tomography system. The angular distribution and energy spectrum of muons measured on the system was also used to generate simulation data of tungsten blocks of different arrangement (geometry). The data were grouped into subsets using the zenith angle and volume images were reconstructed from the data subsets using two algorithms. One was a distributed PoCA (point of closest approach) algorithm and the other was an accelerated iterative maximal likelihood/expectation maximization (MLEM) algorithm. Image resolution was compared for different subsets. Results showed that image resolution was better in the vertical direction for subsets with greater zenith angles and better in the horizontal plane for subsets with smaller zenith angles. The overall image resolution appeared to be the compromise of that of different subsets. This work suggests that the acquired data can be grouped into different limited angle data subsets for optimized image resolution in desired directions. Use of multiple images with resolution optimized in different directions can improve overall imaging fidelity and the intended applications.

Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.

Plant electricity was discovered about 100 years ago. Until recent two decades, researchers started to notice that the electricity play a key role for plant’s communications and defense. Recently, we have demonstrated a wound-generated electrical signal, up to a few hundred mV, can be produced and propagate through the whole plant. As plants defense reactions the wound signal will activate genes and induce subsequent molecular biology responses. In this study, we further investigate the electrical response of plants when they are under nuclear radiation. We discovered nuclear radiation could produce internal voltage gradient in living trees, resulting in measureable voltage and current signals. The results was measured by attaching one of electrodes to a lower branch, close to the roots and attaching the other one to an upper branch. During irradiating, trees were set up at 1-meter far from a NIST-certified ²⁴¹AmBe neutron source (30 mCi). It will produce a neutron field of about 13 mrem/h, corresponding to an actual absorbed dose of ~ 1 mrad/h by assuming the tissue is primarily water content. Once the radioactive source is pulled up from a shielded container below the tree, the system potential starts to drop and in about 6-7 hours it drops down to -220mV, eventually stabilizing at around -250mV after 10 hours of radiation. We have further observed plant electricity changes caused by x-ray, gamma-ray, and beta-ray radiations. After the sources were removed, the terminal voltage recovered and eventually returned to the original value.