This PDF file contains the front matter associated with SPIE Proceedings Volume 8018, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

To provide useful information during military operations, or as part of other security situations, a biological aerosol detector has to respond within seconds or minutes to an attack by virulent biological agents, and with low false alarms. Within this time frame, measuring virulence of a known microorganism is extremely difficult, especially if the microorganism is of unknown antigenic or nucleic acid properties. Measuring "live" characteristics of an organism directly is not generally an option, yet only viable organisms are potentially infectious. Fluorescence based instruments have been designed to optically determine if aerosol particles have viability characteristics. Still, such commercially available biological aerosol detection equipment needs to be improved for their use in military and civil applications. Air has an endogenous population of microorganisms that may interfere with alarm software technologies. To design robust algorithms, a comprehensive knowledge of the airborne biological background content is essential. For this reason, there is a need to study ambient live bacterial populations in as many locations as possible. Doing so will permit collection of data to define diverse biological characteristics that in turn can be used to fine tune alarm algorithms. To avoid false alarms, improving software technologies for biological detectors is a crucial feature requiring considerations of various parameters that can be applied to suppress alarm triggers. This NATO Task Group will aim for developing reference methods for monitoring biological aerosol characteristics to improve alarm algorithms for biological detection. Additionally, they will focus on developing reference standard methodology for monitoring biological aerosol characteristics to reduce false alarm rates.

Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This sensor called SINBAHD demonstrated the capability to detect and characterize bioaerosols from a stand-off position. The sensor sensitivity and false alarm rate directly depend on the background characteristics since these later will dictate the threshold levels to be used. SINBAHD was used to characterize the background aerosols in a maritime environment close to Halifax, Canada in May 2008. The characterization of the LIF signal from the background aerosols included spectral, temporal and spatial aspects over 8 nights of continuous data collection. The local environmental conditions in addition to the aerosol concentration and particle size distribution were recorded during the entire trial period. From the 64 LIF trials, only five showed specific spectral features. The spectral variability was encountered either at short range, thus closer to the shore, or during a night having a specific prevalent wind direction. Indeed, the detected anomalies were in most cases directly related to the climatic conditions. The integrated LIF signal was also processed to assess the use of LIF intensity to identify aerosol anomalies in a maritime environment.

Laboratory measurements demonstrating the effects of ozone on aerosolized B. thuringiensis, as revealed by fluorescence spectra, are reported. The fluorescence emission peak around 330 nm (excited at 263nm) decreases somewhat in intensity and becomes slightly blue-shifted. Further, the fluorescence emission around 400 nm-550 nm is less affected by the ozone than is the 330 nm (tryptophan) peak.

Light detection and ranging (LIDAR) systems have demonstrated some capability to meet the needs of a fastresponse standoff biological detection method for simulants in open air conditions. These systems are designed to exploit various cloud signatures, such as differential elastic backscatter, fluorescence, and depolarization in order to detect biological warfare agents (BWAs). However, because the release of BWAs in open air is forbidden, methods must be developed to predict candidate system performance against real agents. In support of such efforts, the Johns Hopkins University Applied Physics Lab (JHU/APL) has developed a modeling approach to predict the optical properties of agent materials from relatively simple, Biosafety Level 3-compatible bench top measurements. JHU/APL has fielded new ground truth instruments (in addition to standard particle sizers, such as the Aerodynamic particle sizer (APS) or GRIMM aerosol monitor (GRIMM)) to more thoroughly characterize the simulant aerosols released in recent field tests at Dugway Proving Ground (DPG). These instruments include the Scanning Mobility Particle Sizer (SMPS), the Ultraviolet Aerodynamic Particle Sizer (UVAPS), and the Aspect Aerosol Size and Shape Analyser (Aspect). The SMPS was employed as a means of measuring smallparticle concentrations for more accurate Mie scattering simulations; the UVAPS, which measures size-resolved fluorescence intensity, was employed as a path toward fluorescence cross section modeling; and the Aspect, which measures particle shape, was employed as a path towards depolarization modeling.

Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.

Cao et al.¹ published a paper where differentiating bioaerosols (pollens) appeared feasible when linear depolarization ratio signature at multiple wavelengths could be obtained. The measurements were performed at 4 wavelengths. The bioaerosols were disseminated in a controlled environment and the discrimination analysis was based on Mahalanobis distances. Poor discrimination was obtained for single wavelength measurement while acceptable and good discrimination was reported for two and three wavelengths. This innovative work has raised the following question: to which extent does the addition of circular polarization signature to the existing linear polarization increase the overall discrimination capability? In order to answer that question, the measurements of Cao et al. were repeated for linear and circular depolarization ratios. We demonstrate experimentally that the linear and circular depolarization ratios are related to each other via a known simple theoretical mathematical expression in the case of randomly oriented particles. Hence, by measuring one, you obtain the other and consequently there is no additional information that is gained by doing measurements with the two polarization states. This suggests that there is no need for full Mueller matrix measurement systems for detection and discrimination of bioaerosols.

Two outbreaks of legionellosis occurred in the Sarpsborg/Fredrikstad region southeast of Norway in 2005 and 2008 where more than 60 exposed individuals were infected and 10 case patients died. The air scrubber at Borregaard, a wood-based chemical factory, was identified as the outbreak source. High concentration levels of Legionella species, including the etiological agent L. pneumophila SG1 was found in the aeration ponds, which belongs to Borregaard's biological treatment plant. Results showed that these ponds were able to generate Legionella-containing aerosols that were transported by the wind as such aerosols were measured up to 200 meters downwind of the pond. Our studies did not detect L. pneumophila SG1 isolates, only L. pneumophila SG4 during the air sampling measurement campaign. Furthermore, the operational conditions of the air scrubber proved to be harsh for Legionella growth as the outbreak L. pneumophila strains were not able to grow at 45ºC and pH8 (conditions during the outbreaks). These results, together, lead us to suggest that the aeration pond should be regarded as the primary amplifier and disseminator of Legionella and L. pneumophila and thereby most likely being the outbreak source.

Considering their potential implications for human health, agricultural productivity, and ecosystem stability, surprisingly little is known about the composition or dynamics of the atmosphere's biological aerosols. The few studies that have examined phylogenetic diversity in China focused on a single sampling period, whereas this study spans 3 months and includes over 300 samples. The 300+ samples were categorized by month and direction of their back-trajectory. DNA extraction was carried out on the pooled samples in a quantitative manner to allow for comparison between the amount of extracted material and the amount of initial total aerosol mass. Within an individual month, samples originating from similar land types and approximately equidistant to the sampling location exhibited similar diversity, whereas samples originating from much greater distances and from different land types included phyla unique to that location. Phyla from the same origin also varied from one month to the next. The biological diversity found from the Phylochips reinforces the hypothesis that air samples carry a biological record of their history.

Bacterial infectious diseases remain one of the major health hazards nation- and worldwide. The expedience of detection and identification of bacterial pathogens determines how early the diagnosis is, and hence, what the treatment and the outcome of the illness would be. As we have previously reported, the dynamics of fluorescence staining provides venues for the development of expedient assays for detection and identification of bacterial species[1]. We measured the kinetics of bacterial staining with cyanine and thioflavin dyes and investigated their photophysical properties. We demonstrated that the pseudo first-order kinetic constants of the fluorescence staining processes have species specificity without contrition dependence. Combining the dynamics of staining with real-time fluorescence microscopy we characterized the fluorescence staining process at the single-cell level with improved sensitivity and contrast.

This work details a proof of concept study for vapor phase selective sensing using a strategy of biorecognition elements (BRE) integrated into a zinc oxide field effect transistor (ZnO FET). ZnO FETs are highly sensitive to changes to the environment with little to no selectivity. Addition of a biorecognition element retains the sensitivity of the device while adding selectivity. The DNA aptamer designed to bind the small molecule riboflavin was covalently integrated into the ZnO FET and detects the presence of 116 ppb of riboflavin in a nitrogen atmosphere by a change in current. The unfunctionalized ZnO FET shows no response to this same concentrations of riboflavin, showing that the aptamerbinding strategy may be a promising strategy for vapor phase sensing.

Typical bioterrorism prevention scenarios assume well-known and well-characterized pathogens like anthrax or tularemia, which are serious public concerns if released into food and/or water supplies or distributed using other vectors. Common governmental contingencies include rapid response to these biological threats with predefined treatments and management operations. However, bioterrorist attacks may follow a far more sophisticated route. With the widely known and immense progress in genetics and the availability of molecular biology tools worldwide, the potential for malicious modification of pathogenic genomes is very high. Common non-pathogenic microorganisms could be transformed into dangerous, debilitating pathogens. Known pathogens could also be modified to avoid detection, because organisms are traditionally identified on the basis of their known physiological or genetic properties. In the absence of defined primers a laboratory using genetic biodetection methods such as PCR might be unable to quickly identify a modified microorganism. Our concept includes developing a nationwide database of signatures based on biophysical (such as elastic light scattering (ELS) properties and/or Raman spectra) rather than genetic properties of bacteria. When paired with a machine-learning system for emerging pathogen detection these data become an effective detection system. The approach emphasizes ease of implementation using a standardized collection of phenotypic information and extraction of biophysical features of pathogens. Owing to the label-free nature of the detection modalities ELS is significantly less costly than any genotypic or mass spectrometry approach.

We are developing a high data gamma/neutron spectrometer suitable for active interrogation of special nuclear materials (SNM) activated by a single burst from an intense source. We have tested the system at Naval Research Laboratory's (NRL) Mercury pulsed-power facility at distances approaching 10 meters from a depleted uranium (DU) target. We have found that the gamma-ray field in the target room "disappears" 10 milliseconds after the x-ray flash, and that gamma ray spectroscopy will then be dominated by isomeric states/beta decay of fission products. When a polyethylene moderator is added to the DU target, a time-dependent signature of the DU is produced by thermalized neutrons. We observe this signature in gamma-spectra measured consecutively in the 0.1-1.0 ms time range. These spectra contain the Compton edge line (2.2 MeV) from capture in hydrogen, and a continuous high energy gamma-spectrum from capture or fission in minority constituents of the DU.

The concept of detection of thermal neutrons using gadolinium oxide nanocrystals is explored. Gadolinium is an element with by far the highest thermal neutron capture cross section among all stable isotopes. Colloidal synthesis of Gd₂O₃ nanocrystals, Gd₂O₃ nanocrystals doped with Ce, Gd₂O₃ nanocrystals doped with Eu, and Gd₂O₃ nanocrystals co-doped with Ce and Eu is reported. The nanocrystals were characterized by transmission electron microscopy, energy-dispersive X-ray spectroscopy, dynamic light scattering analysis, and steady-state UV-VIS optical absorption and photoluminescence spectroscopy. Neutron detection has been modeled with MCNPX and confirmed in experiments with Gd-containing nanocrystalline samples irradiated with ²⁵²Cf neutron source.

On-site detection and measurement of the activity and extent of alpha (α) contamination presents a significant challenge to radiation detection personnel. Due to the short range of these particles, conventional detection techniques involve bringing a probe within a few centimetres of the suspect area. Performing a thorough survey of an area is a time consuming, painstaking, and potentially dangerous task, as personnel may be exposed to harmful radiation. Conventional detectors may have fragile Mylar windows which are highly prone to breakage. The instrumentation under development employs a novel approach: instead of detecting the radiation directly, it detects radiation-induced air fluorescence surrounding the contaminated area. Optical imaging is used to determine the spatial extent of the contamination, providing a much more rapid, accurate and robust tool for in-situ contamination measurements. A mobile, near-field, wide-angle, fast optical system has been designed and constructed to detect and image this radiation-induced air fluorescence. It incorporates large-area position-sensitive photo-multiplier tubes, UV filters, a specially constructed fast electronic shutter, and an aspherical phase mask to significantly increase the instrument's depth-of-field. First tests indicate that a 0.3 μCi α source can be detected in less than 10 seconds at a standoff distance of 1.5 meters.

The optical and electronic properties of Tl-chalcogenide, wide gap semiconductors, TlGaSe₂, Tl₆I₄Se, and Tl₂Au₄S₃ for x-ray and γ ray detection were characterized. The semiconductor crystals are grown by the modified Bridgman method. The optical absorption and band gap energy of the materials were determined from UV-Vis-near IR transmission and reflection spectra. The mobility-lifetime products were measured. For Tl₆I₄Se the values were comparable to those of CdZnTe. We measured room temperature detector response to x-ray and γ ray radiations. Under ⁵⁷Co radiation, Tl₆I₄Se has a well-resolved spectral response and peak FWHM comparable to those of Cd_0.9Zn_0.1Te.

Homeland security agencies have a requirement to locate and identify nuclear material. Compton cameras [1, 2] offer a more efficient method of gamma-ray detection than collimated detector systems. The resolution of the interaction positions within the detectors greatly influences the accuracy of a reconstructed Compton image. Utilizing digital electronics and applying pulse shape analysis [3] allows the spatial resolution to be enhanced beyond the pixel granularity in three dimensions. Analytically reconstructed Compton images from a range of radiation sources shall be presented with and without pulse shape analysis showing the improvements gained along with a discussion of our analysis methods.

We describe the use of a wind tunnel for conducting controlled passive hyperspectral imaging experiments. In recent years, passive hyperspectral detection of solids, minerals and ores has emerged as a very useful technique, for example for classifying land types, mineral deposits, and agricultural practices. Such techniques are also potentially useful for detecting explosives, solid-phase chemicals and other materials of interest from a distance so as to provide operator safety. The Pacific Northwest National Laboratory operates a wind tunnel facility that can generate and circulate artificial atmospheres whereby certain environmental parameters can be controlled such as lighting, humidity, temperature, aerosol and obscurant burdens. By selecting the appropriate fore-optics and sample size, one can conduct meaningful experiments under controlled conditions at relatively low cost when compared to typical field deployments. We will present recent results describing optimized sensing of solids over tens of meters distance using both visible and near-infrared cameras, as well as the effects of certain environmental parameters on data retrieval.

We studied various liquids using a vertical attenuated total reflection (ATR) liquid sampling assembly in conjunction with Infrared Variable Angle Spectroscopic Ellipsometry (IR-VASE), to determine the infrared optical constants of several bulk liquids related to chemical warfare. The index of refraction, n, and the extinction coefficient, k, of isopropyl methylphosphonofluoridate (Sarin or GB), isopropyl alcohol (IPA) (a precursor of GB), and dimethyl methylphosphonate (DMMP)-a commonly employed simulant for GB, measured by our vertical ATR IR-VASE setup are closely matched to those found in other studies. We also report the optical constants of cyclohexyl methylphosphonofluoridate (GF), 2-(diisopropylamino)ethyl methylphosphonothioate (VX), bis-(2-chloroethyl) sulfide (HD), and 2-chlorovinyl dichloroarsine (L, Lewisite). The ATR IR-VASE technique affords an accurate measurement of the optical constants of these hazardous compounds.

We investigated the signature phenomenology of long-wave infrared (LWIR) reflectance of contaminated surfaces using a quantum-cascade laser (QCL) that tunes from λ = 9.1 to 9.8 μm and a HgCdTe focal-plane-array (FPA) with custom read-out integrated circuit (ROIC). A liquid chemical, diethyl phthalate (DEP), was applied to a variety of substrates such as diffusely reflecting gold, concrete, asphalt, and sand. Multispectral image-cubes of the scattered radiation were generated over 81 wavelengths in steps of 1 cm^-1 at standoff distances ranging from 0.1 to 5 meters. For idealized substrates such as diffusely reflecting gold, the experimentally measured signatures are in good agreement with theoretical calculations. Clear signatures were also obtained for contaminated concrete, asphalt, and sand. These measurements demonstrate the potential of this technique for detecting and classifying chemicals on native outdoor surfaces.

The polarization modulation infrared reflection absorption spectroscopy (PMIRRAS) spectra of the nerve agents GB (O-isopropyl methylphosphonofluoridate) and GF (cyclohexyl methylphoshonofluoridate) were recorded for the first time. A comparison of these spectra with the nerve agent VX (ethyl S-2-diisopropylaminoethyl methylphosphonothiolate) and the spectra of some trialkyl phosphates indicates that it is easy to distinguish between chemical warfare agents and simulants on militarily-relevant surfaces using PMIRRAS.

Surface-enhanced Raman scattering (SERS)-based techniques are increasingly used for Army, first responder and defense relevant applications. Commonly SERS is used for the identification and characterization chemical and biological hazards, energetic materials and medical applications. SERS, like most Raman-based techniques is an beneficial analytical method to use in theatre as it offers many distinct advantages. SERS advantages include: straightforward sample identification, little to no sample preparation requirements, applicability in many environments, freedom from water interference, ability to be used with a variety of laser sources, and specific sample spectrum directly link to vibrations within the molecule constituents of the target. Despite all of these advantages, the ubiquitous use of SERS remains challenging in part due to the lack of a commercially available reproducible, high sensitivity and uniform substrates. In this paper, we discuss the need for more reproducible and sensitive SERS substrates and report on the characterization of next-generation commercial SERS substrates. The substrates analyzed are non-optimized prototypes, and demonstrate great potential for Army relevant sensing. The next-generation Klarite substrates are designated as the 308's and 309's. Within our testing parameters, the 308 demonstrates performance up to four orders of magnitude better than the standard Klarite when measuring a common SERS active analyte. For application to biological samples, all substrates demonstrate similar performance.

We describe a photofragment laser-induced fluorescence (PF-LIF) method that can be applied to the short-range-standoff detection of low-volatility organophosphonate chemical warfare agents (OP-CWAs) on surfaces. It operates by photofragmenting a surface-bound analyte and then actively interrogating a released phosphorous monoxide (PO) fragment using LIF. We demonstrate a single-pulse-pair (pump = 500 μJ @ 266 nm; probe = 20 μJ @ 248 nm) surface detection sensitivity of 30 μg/cm² for the organophosphonate diisopropyl isothiocyanate phosphonate (DIPP) on aluminum and 210 μg/cm² for the same analyte on a more porous concrete surface. By detecting the PO photofragment, the method indicates the presence of organophosphonates; however, we show that it also responds to other phosphorouscontaining compounds. Because of its limited specificity, we believe that the method may have most immediate use as a mapping tool to rapidly identify "hotspots" of OP-CWAs. These would then be confirmed using a more specific tool. As one method of confirming the presence of OP-CWAs (and identifying the agent), we demonstrate that the probe beam can be used to acquire Raman-scattering spectra of the target area.

We report in this paper the feasibility of standoff chemical detection using quantum cascade lasers (QCLs) and photoacoustic technique. In the experiment, we use a QCL with an emission wavelength near 7.9 μm, an electret condenser microphone, and isopropanol (IPA) vapor as a safer experimental substitute of the explosive, RDX. The QCL is operated at pulsed mode and the laser beam focused on the IPA vapor sample. The photoacoustic sound wave is generated and detected by the microphone at a remote distance. With less than 40 mW laser power, standoff photoacoustic chemical detection distance over 35 cm is achieved.

Laser Induced Breakdown Spectroscopy (LIBS) utilizes a diversity of standard spectroscopic techniques for classification of materials present in the sample. Pre-excitation processing sometimes limits the analyte to a short list of candidates. Prior art demonstrates that sparsity is present in the data. This is sometimes characterized as identification by components. Traditionally, spectroscopic identification has been accomplished by an expert reader in a manner typical for MRI images in the medicine. In an effort to automate this process, more recent art has emphasized the use of customized variations to standard classification algorithms. In addition, formal mathematical proofs for compressive sensing have been advanced. Recently the University of Memphis has been contracted by the Spectroscopic Materials Identification Center to advance and characterize the sensor research and development related to LIBS. Applications include portable standoff sensing for improvised explosive device detection and related law enforcement and military applications. Reduction of the mass, power consumption and other portability parameters is seen as dependent on classification choices for a LIBS system. This paper presents results for the comparison of standard LIBS classification techniques to those implied by Compressive Sensing mathematics. Optimization results and implications for portable LIBS design are presented.

The DLR laser test range at Lampoldshausen is designed for a wide field of laser application studies under central European atmospheric conditions. Micrometeorological measurements are performed simultaneously and nearby to the laser propagation. The infrastructure is very suitable for the development of laser based standoff detection systems of biological, chemical, and explosive hazardous substances. In a first approach, laser-induced breakdown spectroscopy (LIBS) has been introduced for investigation of surface contaminants at distances up to 135 m. A basic LIBS set-up and LIBS spectra of selected samples using different excitation wavelengths from IR to UV are presented for detection at different distances. A Nd:YAG laser beam was focussed by a Cassegrain type telescope onto different samples. The light of the generated plasma plume was collected by a Newtonian telescope, analysed and detected by a broadband CCD-spectrometer system. The Nd:YAG laser yields pulse energies up to 800 mJ at a wavelength of 1064 nm and a pulse width of 8 ns. Optionally the second and third harmonics can be extracted at reduced energy. LIBS spectra produced on gold layers as thin as 5 nm deposited on silicon wafers were recorded for test of detection sensitivity and comparison of wavelengths effects. In addition, black powder as ordinary substance representing explosives was detected by LIBS technology. Spectra were recorded in single and repetitive pulsed scheme of the Nd:YAG laser at various daylight and atmospheric conditions.

The need for small, robust, and highly selective sensors, for both military and homeland security requirements, is driving the development of portable detectors for hazardous materials. Infrared spectroscopy exhibits high selectivity because the infrared vibrational transitions correlate to the molecular structure and functional groups within the molecule. Small FTIR systems exist as COTS items; however, these systems still require precise moving components to generate the interferogram. A more desirable approach is to build a solid state system with no precision moving parts as required by a typical moving mirror interferometer. This work will describe the design aspects of an optical fiber based mid-infrared FTIR, and focus specifically on the stateof- the-art mid-infrared transmitting optical fibers and the use of an optical fiber supercontinuum source for efficient coupling of light into the system.

We describe the development and testing of hollow core glass waveguides (i.e., fiber optics) for use in Mid-Wave Infrared (MWIR) and Long-Wave Infrared (LWIR) spectroscopy systems. Spectroscopy measurements in these wavelength regions (i.e., from 3 to 14 μm) are useful for detecting trace chemical compounds for a variety of security and defense related applications, and fiber optics are a key enabling technology needed to improve the utility and effectiveness of detection and calibration systems. Hollow glass fibers have the advantage over solid-core fibers (e.g., chalcogenide) in that they are less fragile, do not produce cladding modes, do not require angle cleaving or antireflection coatings to minimize laser feedback effects, and effectively transmit deeper into the infrared. This paper focuses on recent developments in hollow fiber technology geared specifically for infrared spectroscopy, including single mode beam delivery with relatively low bending loss. Results are presented from tests conducted using both Quantum Cascade Lasers (QCL) and CO₂ lasers operating in the LWIR wavelength regime. Single-mode waveguides are shown to effectively deliver beams with relatively low loss (~ 1 dB/m) and relatively high beam quality. The fibers are also shown to effectively mode-filter the "raw" multi-mode output from a QCL, in effect damping out the higher order modes to produce a circularly symmetric Gaussian-like beam profile.

Nano-cavity photothermal spectroscopy is a novel technique for ultra-sensitive chem-bio detection. We illustrate that through simultaneous localization of optical and thermal interactions in a planar nano-cavity, detection sensitivity can be improved by > 10⁴ compared to state-of-the-art. Key to nano-cavity photothermal sensing is the use of novel infraredtransparent chalcogenide glasses for resonant cavity fabrication, as these glasses feature a photothermal figure-of-merit over two orders of magnitude higher than conventional materials. We demonstrate planar optical resonant cavity devices in these glasses with record cavity quality factors up to 5 × 10⁵, leading to high photothermal detection sensitivity.

We report the integration of a nanomechanical sensor consisting of 16 silicon microcantilevers and polydimethylsiloxane (PDMS) microfluidics. With our recently developed in-plane photonic transduction method we routinely achieve microcantilever transduction responsivities in the range of 0.5-1.1 μm^-1, which is comparable to the best reported for the laser reflection readout method used in atomic force microscopy (AFM). Prior work has established that differential surface stress as low as 0.23 mN/m is readily measurable with our arrays. In this paper we show biotin-streptavidin sensing with a differential surface stress of ~2.3 mN/m as a first step toward characterizing integrated microcantilever array/microfluidic sensors.

This paper describes an organic data fusion and sensor cueing approach for Chemical, Biological, Radiological, and Nuclear (CBRN) sensors. The Joint Warning and Reporting Network (JWARN) uses a hardware component referred to as the JWARN Component Interface Device (JCID). The Edgewood Chemical and Biological Center has developed a small footprint and open architecture solution for the JCID capability called JCID-on-a-Chip (JoaC). The JoaC program aims to reduce the cost and complexity of the JCID by shrinking the necessary functionality down to a small single board computer. This effort focused on development of a fusion and cueing algorithm organic to the JoaC hardware. By embedding this capability in the JoaC, sensors have the ability to receive and process cues from other sensors without the use of a complex and costly centralized infrastructure. Additionally, the JoaC software is hardware agnostic, as evidenced by its drop-in inclusion in two different system-on-a-chip platforms including Windows CE and LINUX environments. In this effort, a partnership between JPM-CA, JHU/APL, and the Edgewood Chemical and Biological Center (ECBC), the authors implemented and demonstrated a new algorithm for cooperative detection and localization of a chemical agent plume. This experiment used a pair of mobile Joint Services Lightweight Standoff Chemical Agent Detector (JSLSCAD) units which were controlled by fusion and cueing algorithms hosted on a JoaC. The algorithms embedded in the JoaC enabled the two sensor systems to perform cross cueing and cooperatively form a higher fidelity estimate of chemical releases by combining sensor readings. Additionally, each JSLSCAD had the ability to focus its search on smaller regions than those required by a single sensor system by using the cross cue information from the other sensor.

Torch Technologies Inc., is actively involved in chemical sensor networking and data fusion via multi-year efforts with Dugway Proving Ground (DPG) and the Defense Threat Reduction Agency (DTRA). The objective of these efforts is to develop innovative concepts and advanced algorithms that enhance our national Chemical Warfare (CW) test and warning capabilities via the fusion of traditional and non-traditional CW sensor data. Under Phase I, II, and III Small Business Innovative Research (SBIR) contracts with DPG, Torch developed the Advanced Chemical Release Evaluation System (ACRES) software to support non real-time CW sensor data fusion. Under Phase I and II SBIRs with DTRA in conjunction with the Edgewood Chemical Biological Center (ECBC), Torch is using the DPG ACRES CW sensor data fuser as a framework from which to develop the Cloud state Estimation in a Networked Sensor Environment (CENSE) data fusion system. Torch is currently developing CENSE to implement and test innovative real-time sensor network based data fusion concepts using CW and non-CW ancillary sensor data to improve CW warning and detection in tactical scenarios.

The Chemical and Biological Detection Early Warning System (CBDEWS) is a multi-modal sensing approach to realtime chemical and biological detection and warning. The system employs a diverse set of standoff sensors (including LIDAR, IR, and acoustic) and UAV-borne point sensors for the purpose of providing sufficient warning and actionable information in response to a chemical or biological event. This paper describes the data fusion algorithm, which fused individual detection data from all sensors in terms of both cloud location and classification. Additionally, numerous sensors were cued to areas of interest in their field of regard based on detections and their fused result. The fusion algorithm employs grid-based recursive Bayesian estimation to compute an estimate of the cloud's location and classification over a discrete spatial grid. Results from a field test performed at the Dugway Proving Ground in October of 2008 and July of 2009 are presented.

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 microsystem 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. Current work utilizes finite element analysis software to develop a model for the characterization of a photoacoustic cell that has provided favorable vapor detection capabilities in a sensor platform. The model is used to predict the acoustic resonance frequency of the cell and the results are compared to experimental data.

We present findings of the DYCE project, which addresses the needs of military and blue light responders to provide a rapid, reliable on-scene analysis of the dispersion of toxic airborne chemical threat agents following their release into the environment. We describe the development and experimental results for a small network of ad-hoc deployable chemical and meteorological sensors capable of identifying and locating the source of the contaminant release, as well as monitoring and estimating the dispersion characteristics of the plume. We further present deployment planning methodologies to optimize the data gathering mission given a constrained asset base.

A Telops Hyper-Cam midwave infrared (1.5 - 5.5μm) imaging Fourier-transform spectrometer (IFTS) was used to estimate industrial smokestack total effluent mass flow rates by combining spectrally-determined species concentrations with flow rates estimated via analysis of sequential images in the raw interferogram cube. Measurements of the coalburning smokestack were made with the IFTS at a stand-off distance of 350m. 185 hyperspectral datacubes were collected on a 128(W)×64(H) pixel sub-window (11.4×11.4cm² per pixel) at a 0.5cm^-1 spectral resolution. Strong emissions from H₂O, CO₂, CO, SO₂, and NO were observed in the spectrum. A previously established single-layer radiative transfer model was used to estimate gas concentrations immediately above stack exit, and results compared reasonably with in situ measurements. A simple temporal cross-correlation analysis of sequential imagery enabled an estimation of the flow velocity at center stack. The estimated volumetric flow rate of 106±23m/s was within 4% of the reported value. Final effluent mass flow rates for CO₂ and SO₂ of 13.5±3.8kg/s and 71.3±19.3g/s were in good agreement with in situ rates of 11.6±0.1kg/s and 67.8±0.5g/s. NO was estimated at 16.1±4.2g/s, which did not compare well to the total NO_x (NO +NO₂) reported value of 11.2±0.2g/s. Unmonitored H₂O, HCl , and CO were also estimated at 7.76±2.25kg/s, 7.40±2.00g/s, and 15.0±4.1 g/s respectively.

iCATSI is a combination of the CATSI instrument, a standoff differential FTIR optimised for the characterisation of chemicals, and of the MR-i, the hyperspectral imaging spectroradiometer of ABB Bomem based on the proven MR spectroradiometers. The instrument is equipped with a dual-input telescope to perform optical background subtraction. The resulting signal is the differential between the spectral radiance entering each input port. With that method, the signal from the background is automatically removed from the signal of the object of interest. The instrument is capable of sensing in the VLWIR (cut-off near 14 μm) to support research related to standoff chemical detection.

A sensitive, ground-based thermal imaging spectrometer was deployed at the Army's Dugway Proving Ground to remotely monitor explosively released chemical-warfare-agent-simulant clouds from stand-off ranges of a few kilometers. The sensor has 128 spectral bands covering the 7.6 to 13.5 micron region. The measured cloud spectra clearly showed scattering of high-elevation-angle sky radiance by liquid aerosols or dust in the clouds: we present arguments that show why the scattering is most likely due to dust. This observation has significant implications for early detection of dust-laden chemical clouds. On one hand, detection algorithms must properly account for the scattered radiation component, which would include out-of-scene radiation components as well as a dust signature; on the other hand, this scattering gives rise to an enhanced "delta-T" for detection by a ground-based sensor.

Laser induced breakdown spectroscopy (LIBS) can provide rapid, minimally destructive, chemical analysis of substances with the benefit of little to no sample preparation. Therefore, LIBS is a viable technology for the detection of substances of interest in near real-time fielded remote sensing scenarios. Of particular interest to military and security operations is the detection of explosive residues on various surfaces. It has been demonstrated that LIBS is capable of detecting such residues, however, the surface or substrate on which the residue is present can alter the observed spectra. Standard chemometric techniques such as principal components analysis and partial least squares discriminant analysis have previously been applied to explosive residue detection, however, the classification techniques developed on such data perform best against residue/substrate pairs that were included in model training but do not perform well when the residue/substrate pairs are not in the training set. Specifically residues in the training set may not be correctly detected if they are presented on a previously unseen substrate. In this work, we explicitly model LIBS spectra resulting from the residue and substrate to attempt to separate the response from each of the two components. This separation process is performed jointly with classifier design to ensure that the classifier that is developed is able to detect residues of interest without being confused by variations in the substrates. We demonstrate that the proposed classification algorithm provides improved robustness to variations in substrate compared to standard chemometric techniques for residue detection.

In order to stop the transportation of materials used for IED manufacture, a standoff checkpoint explosives detection system (CPEDS) has recently been fabricated. The system incorporates multi-wavelength Raman spectroscopy and laser induced breakdown spectroscopy (LIBS) modalities with a LIBS enhancement technique called TEPS to be added later into a single unit for trace detection of explosives at military checkpoints. Newly developed spectrometers and other required sensors all integrated with a custom graphical user interface for producing simplified, real-time detection results are also included in the system. All equipment is housed in a military ruggedized shelter for potential deployment intheater for signature collection. Laboratory and performance data, as well as the construction of the CPEDS system and its potential deployment capabilities, will be presented in the current work.

Deep-ultraviolet resonance Raman spectroscopy (DUVRRS) is a potential candidate for stand-off detection of explosives. A key challenge for stand-off sensors is to distinguish explosives, with high confidence, from a myriad of unknown background materials that may have interfering spectral peaks. To address this, we have investigated a new technique that simultaneously detects Raman spectra from multiple DUV excitation wavelengths. Due to complex interplay of resonant enhancement, self-absorption and laser penetration depth, significant intensity variation is observed between corresponding Raman bands with different excitation wavelengths. These variations with excitation wavelength provide a unique signature that complements the traditional Raman signature to improve specificity relative to singleexcitation- wavelength techniques. We have measured these signatures for a wide range of explosives using amplitudecalibrated Raman spectra, obtained sequentially by tuning a frequency-doubled Argon laser to 229, 238, 244 and 248 nm. For nearly all explosives, these signatures are found to be highly specific. An algorithm is developed to quantify the specificity of this technique. To establish the feasibility of this approach, a multi-wavelength DUV source, based on Nd:YAG harmonics and hydrogen Raman shifting, and a compact, high throughput DUV spectrometer, capable of simultaneous detection of Raman spectra in multiple spectral windows, are being investigated experimentally.

Stand-off detection of potentially hazardous small molecules at distances that allow the user to be safe has many applications, including explosives and chemical threats. The Naval Surface Warfare Center, Crane Division, with EYZtek, Inc. of Ohio, developed a prototype stand-off, eye-safe Raman spectrometer. With a stand-off distance greater than twenty meters and scanning optics, this system has the potential of addressing particularly difficult challenges in small molecule detection. An overview of the system design and desired application space is presented.

We examined photochemical degradation of energetic molecules upon UV resonance Raman (UVRR) excitation of the 229 nm UVRR spectra of solid HMX, TNT and RDX. Comparisons of the UVRR spectra of these photodegraded samples to those of different carbon samples indicate some features similar to carbon compounds with sp² bonding, vaguely reminiscent of graphitic carbon as well as amorphous carbon. Spinning the energetic material samples minimizes the per molecule photon flux which decreases the photochemistry. We very roughly estimated photochemical degradation quantum yields of <10^-6.

Ultraviolet resonance Raman spectroscopy (UVRRS) has been used to examine a variety of different isomers of nitroaromatic molecules. Due to the large cross section enhancements possible, UVRRS has the potential to be a sensitive means for detecting trace quantities of explosives at standoff distances. Since it probes both the electronic and vibrational states of the molecules, it can also be a selective means for differentiating between similar molecules. Resonance Raman spectra will be discussed, along with the different trends that are observed, for the different positional isomers of dinitrobenzene. In addition, spectra for the common explosive 2,4,6-trinitrotoluene will be presented.

We are utilizing control of molecular processes at the quantum level via the best capabilities of recent laser technology and recent discoveries in optimal shaping of laser pulses to significantly enhance the standoff detection of explosives. Optimal dynamic detection of explosives (ODD-Ex) is a methodology whereby laser pulses are optimally shaped to simultaneously enhance the sensitivity and selectivity of any of a wide variety of spectroscopic methods for explosives signatures while reducing the influence of noise and environmental perturbations. We discuss here recent results using complementary ODD-Ex methods.

Spatially Offset Raman Spectroscopy (SORS) is a novel technique used to identify the chemical Raman signature of threat materials within a few seconds through common non-metallic containers, including those containers which may not yield to inspection by conventional backscatter Raman. In particular, some opaque plastic containers and coloured glass bottles can be difficult to analyze using conventional backscatter Raman because the signal from the contents is often overwhelmed by the much stronger Raman signal and/or fluorescence originating from the container itself. SORS overcomes these difficulties and generates clean Raman spectra from both the container and the contents with no prior knowledge of either. This is achieved by making two, or more, Raman measurements at various offsets between the collection and illumination areas, each containing different proportions of the fingerprint signals from the container and content materials. Using scaled subtraction, or multivariate statistical methods, the two orthogonal signals can be separated numerically, thereby providing a clean Raman spectrum of the contents without contamination from the container. Consequently, SORS promises to significantly improve threat detection capability and decrease the falsealarm rate compared with conventional Raman spectroscopy making it considerably more suitable as an alarm resolution methodology (e.g. at airports). In this paper, the technique and method are described and a study of offset value optimization is described illustrating the difference between one and two fixed spatial offsets. It is concluded that two fixed offsets yield an improvement in the SORS measurement which will help maximize the threat detection capability.

The existing assortment of reference sample preparation methods presents a range of variability and reproducibility concerns, making it increasingly difficult to assess chemical detection technologies on a level playing field. We are investigating a drop-on-demand table-top printing platform which offers precise liquid sample deposition and is well suited for the preparation of effective reference materials. Current research includes the development of a sample preparation protocol for explosive materials testing based on drop-on-demand technology. A quartz crystal microbalance and Raman spectroscopy are used to investigate droplet and sample uniformity and reproducibility. Results are compared to samples prepared using a drop and dry method.

In this paper we describe the application of mass spectrometry (MS) to the detection of trace explosives. We begin by reviewing the issue of explosives trace detection (ETD) and describe the method of mass spectrometry (MS) as an alternative to existing technologies. Effective security screening devices must be accurate (high detection and low false positive rate), fast and cost effective (upfront and operating costs). Ion mobility spectrometry (IMS) is the most commonly deployed method for ETD devices. Its advantages are compact size and relatively low price. For applications requiring a handheld detector, IMS is an excellent choice. For applications that are more stationary (e.g., checkpoint and alternatives to IMS are available. MS is recognized for its superior performance with regard to sensitivity and specificity, which translate to lower false negative and false positive rates. In almost all applications outside of security where accurate chemical analysis is needed, MS is usually the method of choice and is often referred to as the gold standard for chemical analysis. There are many review articles and proceedings that describe detection technologies for explosives. ^1,2,3,4 Here we compare MS and IMS and identify the strengths and weaknesses of each method. - Mass spectrometry (MS): MS offers high levels of sensitivity and specificity compared to other technologies for chemical detection. Its traditional disadvantages have been high cost and complexity. Over the last few years, however, the economics have greatly improved and MS is now capable of routine and automated operation. Here we compare MS and IMS and identify the strengths and weaknesses of each method. - Ion mobility spectrometry (IMS): ⁵ MS-ETD Screening System IMS is similar in concept to MS except that the ions are dispersed by gas-phase viscosity and not by molecular weight. The main advantage of IMS is that it does not use a vacuum system, which greatly reduces the size, cost, and complexity relative to MS. However, the trade-off is that the measurement accuracy is considerably less than MS. This is especially true for complex samples or when screening for a large number of target compounds simultaneously.

In the framework of the research project "Xsense" at the Technical University of Denmark (DTU) we are developing a simple colorimetric sensor array which can be useful in detection of explosives like DNT, TATP, HMX, RDX and identification of reagents needed for making homemade explosives. The technology is based on an array of chemoselective compounds immobilized on a solid support. Upon exposure to the analyte in suspicion the colorimetric array changes color. Each chosen compound reacts chemo-selectively with analytes of interest. A change in a color signature indicates the presence of unknown explosives and volatile organic compounds (VOCs). We are working towards the selection of compounds that undergo color changes in the presence of explosives and VOCs, as well as the development of an immobilization method for the molecules. Digital imaging of the colorimetric array before and after exposure to the analytes creates a color difference map which gives a unique fingerprint for each explosive and VOCs. Such sensing technology can be used for screening relevant explosives in a complex background as well as to distinguish mixtures of volatile organic compounds distributed in gas and liquid phases. This sensor array is inexpensive, and can potentially be produced as single use disposable.

The detection of specific chemicals when concealed behind a layer of clothing is reported using near infrared (NIR) spectroscopy. Concealment modifies the spectrum of a particular chemical when recorded at stand-off ranges of three meters in a diffuse reflection experiment. The subsequent analysis to identify a particular chemical has involved employing calibration models such as principal component regression (PCR) and partial least squares regression (PLSR). Additionally, detection has been attempted with good results using neural networks. The latter technique serves to overcome nonlinearities in the calibration/training dataset, affording more robust modelling. Finally, lock-in amplification of spectral data collected in through-transmission arrangement has been shown to allow detection at SNR as low as -60dB. The work has been shown to both allow detection of specific chemicals concealed behind a single intervening layer of fabric material, and to estimate the concentration of certain liquids.

In the wake of recent terrorist attacks, such as the 2008 Mumbai hotel explosion or the December 25^th 2009 "underwear bomber", our group has developed a technique (US patent #7368292) to apply differential reflection spectroscopy to detect traces of explosives. Briefly, light (200-500 nm) is shone on a surface such as a piece of luggage at an airport. Upon reflection, the light is collected with a spectrometer combined with a CCD camera. A computer processes the data and produces in turn a differential reflection spectrum involving two adjacent areas of the surface. This differential technique is highly sensitive and provides spectroscopic data of explosives. As an example, 2,4,6, trinitrotoluene (TNT) displays strong and distinct features in differential reflectograms near 420 nm. Similar, but distinctly different features are observed for other explosives. One of the most important criteria for explosive detection techniques is the limit of detection. This limit is defined as the amount of explosive material necessary to produce a signal to noise ratio of three. We present here, a method to evaluate the limit of detection of our technique. Finally, we present our sample preparation method and experimental set-up specifically developed to measure the limit of detection for our technology. This results in a limit ranging from 100 nano-grams to 50 micro-grams depending on the method and the set-up parameters used, such as the detector-sample distance.

A physics-based empirical model is developed to characterize the time varying temperature profile from post-detonation combustion. Fourier-transform infrared signatures are collected from field detonations of RDX-based aluminized high explosives surrounded by an aluminized plastic-bonded spin-cast liner. The rate of change of temperature in the postdetonation combustion fireballs are modeled using a radiative cooling term and a double exponential combustion source term. Optimized nonlinear least-squares fit of the numerical solution of the empirical model to the temperature data yields peak temperatures of 1290-1850. The observed heat released in the secondary combustion is well correlated with the high explosive and liner heat of combustion with an average efficiency of 54%.

The passive standoff detection of vapors from particular explosives and precursors emanating from a location under surveillance can provide early detection and warning of illicit explosives fabrication. DRDC Valcartier recently initiated the development and field-validation of a novel R&D prototype, MoDDIFS (Multi-Option Differential and Imaging Fourier Spectrometer) to address this security vulnerability. The proposed methodology combines the clutter suppression efficiency of the differential detection approach with the high spatial resolution provided by the hyperspectral imaging approach. This consists of integrating the imaging capability of the Hyper-Cam IR imager with a differential CATSI-type sensor. This paper presents the MoDDIFS sensor methodology and the first investigation results that were recently obtained.

OPTRA is developing a compact, wide field standoff diffuse reflectance spectrometer for trace explosive detection from a safe standoff. This system is comprised of two key components: a Risley scanner and an infrared tunable laser based spectrometer. The Risley scanner is a mature technology, which uses a pair of matched prisms to steer a laser beam anywhere inside a cone. The compact size, low operating power, and large field of view of the Risley scanner make it the ideal solution for rapidly scanning the laser over the field. The infrared tunable laser spectrometer utilizes a low-cost quartz crystal tuning fork (QCTF) in place of a traditional infrared detector. The large Q-factor of the QCTF enables high sensitivity, low noise detection of explosive signatures even for low concentrations and large standoffs. By coupling this demonstrated technology with a mature Risley scanner design, the field can be scanned both spatially and spectrally. Pairing this data with sophisticated algorithms results in a map of explosives in the field. This paper presents OPTRA's breadboard spectrometer design along with the TNT and RDX spectra it produced.

Laser-induced breakdown spectroscopy (LIBS) has shown great promise for applications in chemical, biological, and explosives (CBE) sensing and has significant potential for real time standoff detection and analysis. We have studied LIBS emissions in the mid-infrared (MIR) spectral region for potential applications in CBE sensing. Detailed MIR-LIBS studies were performed for several energetic materials for the first time. In this study, the IR signature spectral region between 4 - 12 um was mined for the appearance of MIR-LIBS emissions that are directly indicative of oxygenated breakdown products as well as partially dissociated and recombination molecular species.

The performance of an open-path, multi-chemical detector designed for continuous, long line-of-sight monitoring is described. The detector system is comprised of an infrared source that projects a collimated broad-spectrum beam towards a detector, which can be located up to 45 m away from the source. The detector spectrally analyzes the beam with an array of room-temperature pyroelectric detectors integrated with bandpass filters. When chemicals intercept the beam, they are detected and identified by a non-linear Mahalnobis distance based detection and identification algorithm, which matches each newly recorded IR absorption spectrum against chemical signatures stored in the detector's onboard, remotely updatable database. Using this algorithm, multiple chemicals can be detected and identified under high humidity conditions and in the presence of interfering chemicals. The sensor and algorithm were tested in the laboratory and field deployments, including continuous operation trials at public transportation centers, office buildings, and chemical storage facilities. In laboratory tests, the detector was presented with various chemicals at known optical densities in a gas containment cell. Different environmental conditions were simulated by varying the relative humidity of the air in the cell and introducing interferent gases. The laboratory tests were used to establish minimum detection sensitivities under varying conditions. Field test data were used to evaluate false negative and false positive rates, and field operation characteristics. These tests demonstrated below IDLH concentration sensitivity for the chemicals tested, no false positive identifications, and no false negatives to field test challenges.

We present our experiments with state-of-the-art equipment to dispense threat-representative (3 - 90 nL) freely falling droplets of viscous chemical material (< 500 cP) at room temperature and measure their time-dependent interactions with realistic surface substrates (road surfaces). A direct displacement droplet dispenser is used to generate the droplets and a goniometer/tensiometer is used to analyze the surface interaction of the free-falling droplets after surface impact. The advanced goniometer system is able to characterize the surfaces, capture images of the impact and time dependent droplet morphology after impact, and is able to calculate the average contact angle and droplet volume as a function of time. By coupling these instruments, a free-falling threat-representative droplet of viscous material can be created on demand and the behavior of the droplet on a surface can be monitored as a function of time. Knowledge of how these droplets behave on surfaces is critical in understanding an entire chemical threat scenario and directly impacts the design, testing, and success of standoff surface chemical sensor technology and modeling efforts alike. We are currently working to address this knowledge gap by recording 'cradle-to-grave' droplet dissemination and surface interaction events.

Progress in standoff detection of surface-bound explosives residue using photothermal and photoacoustic (PT/PA) imaging and spectroscopy has been reported recently. Photothermal/photoacoustic interferometry (PTI), a variation of the aforementioned techniques, is a candidate for standoff detection as a result of its non-contact and non-destructive approach. In PTI, the transient PT/PA hydrodynamic response produced by impulsive infra-red laser excitation(s) are detected by an overlapping focused probe laser beam. The return back-scattered/reflected probe laser beam is collected and coupled into a single-mode optical fiber. The PT/PA-induced perturbation on the return probe laser, in the form of phase or amplitude modulation or both, is extracted interferometrically. The resulting quadrature signals are digitized and processed to recover the minute PT/PA dynamics above background noise. Characteristic spectra for materials can be obtained by quantifying the PT response as a function of excitation(s) wavelength. The CW probe laser, operating in the 1550 nm range, and the constituents of the coherent detection system are commercial off-the-shelf components. A commercially available and continuously tunable quantum cascade laser (QCL) with output pulse energies up to 50 nJ was employed to generate the PT/PA spectra in the 8.8-10.2 μm range. PTI detected absorption spectra were collected for HMX, RDX, and PETN, with the probe laser system positioned 5 meters away from the explosives targets. In addition, PTI measurements of the stimulated Raman (SR) spectra of ammonium nitrate and 2,4,6-trinitrotoluene obtained using a near-IR OPO laser are described. We believe this is the first-ever application of photothermal techniques to the measurement of the SR effect on solid explosive materials at meaningful standoff distances.

Port and harbor security has rapidly become a point of interest and concern with the emergence of new improvised explosive devices (IEDs). The ability to provide physical surveillance and identification of IEDs and unexploded ordnances (UXO) at these entry points has led to an increased effort in the development of unmanned underwater vehicles (UUVs) equipped with sensing devices. Traditional sensors used to identify and locate potential threats are side scan sonar/acoustic methods and magnetometers. At the Naval Research Laboratory (NRL), we have developed an immunosensor capable of detecting trace levels of explosives that has been integrated into a REMUS payload for use in the marine environment. Laboratory tests using a modified PMMA microfluidic device with immobilized monoclonal antibodies specific for TNT and RDX have been conducted yielding detection levels in the low parts-per-billion (ppb) range. New designs and engineered improvements in microfluidic devices, fluorescence signal probes, and UUV internal fluidic and optical components have been investigated and integrated into the unmanned underwater prototype. Results from laboratory and recent field demonstrations using the prototype UUV immunosensor will be discussed. The immunosensor in combination with acoustic and other sensors could serve as a complementary characterization tool for the detection of IEDs, UXOs and other potential chemical or biological threats.

Emerging threats of improvised explosive devices (IEDs) and homemade explosives (HMEs) have created a demand for reliable and unambiguous recognition of constituent analytes. Triacetone triperoxide (TATP), a cyclic peroxide based explosive has become a weapon of choice [1] in the hands of resourceful urban insurgents mainly because of ease of manufacture with readily available precursor constituents (acetone and concentrated hydrogen peroxide). Failure of conventional EDDs due to absence of nitrogen compounds coupled with the fact that TATP exhibits no significant absorption in UV region and does not demonstrate fluorescence has confined its detection to IR and Raman spectroscopy besides some enzyme-based tests and mass spectrometry [2]. Hence there is an urgent need for highly sensitive technique with a fast response speed that can detect presence of TATP at extremely low vapour pressure and purposely camouflaged physically or under cross-contamination with interfering compounds. In the present work trace level (20 ppm) acetone (precursor of TATP) sensing characteristics of rf sputtered semiconducting SnO₂ thin films having embedded Pt interdigital electrodes have been investigated. Specifically a fast response speed of 08 seconds is noted and sensing characteristics of bare SnO2 and catalyst-SnO₂ hetero-structures are compared. Innovative catalyst dispersal technique is shown to enhance sensor response as also reduce response times. Novel sensing hetero-structures with reversible acetone detection capabilities are shown to provide a feasible alternative for real-field operation along with remote detection with limited sample size.

The fingerprint region of most gases is within 3 to 14μm. A mid-wave or long-wave infrared thermal imager is therefore commonly applied in gas detection. With further influence of low gas concentration and heterogeneity of infrared focal plane arrays, the image has numerous drawbacks. These include loud noise, weak gas signal, gridding, and dead points, all of which are particularly evident in sequential images. In order to solve these problems, we take into account the characteristics of the leaking gas image and propose an enhancement method based on adaptive time-domain filtering with morphology. The adaptive time-domain filtering which operates on time sequence images is a hybrid method combining the recursive filtering and mean filtering. It segments gas and background according to a selected threshold; removes speckle noise according to the median; and removes background domain using weighted difference image. The morphology method can not only dilate the gas region along the direction of gas diffusion to greatly enhance the visibility of the leakage area, but also effectively remove the noise, and smooth the contour. Finally, the false color is added to the gas domain. Results show that the gas infrared region is effectively enhanced.

This work presents specially designed radiation monitoring system for baggage screening at airports and border crossing points for the presence of radioactive and Special Nuclear Materials (SNM). Border monitoring equipment plays a key role in combating illicit trafficking. The conveyor monitor is designed to meet the detection level determined by the standard for Radiation Portal Monitors (RPM). The obtained sensitivity results of the system and an analytical analysis of the implemented algorithms contributing to the detection performances are presented. The system consists of highly sensitive gamma and neutron detectors, electronic data-processing unit, computer interface and unique algorithms. The system's electronic unit interfaces with the conveyor control system using two signals, an input signal for the conveyor operation status and an output signal for stopping the conveyor in case of alarm. This interface and the implemented algorithm reduce the number of false alarms and improve the detection level by considering the background variation. Further significant improvement in the detection level is achieved by implementing an advanced algorithm based on the detector reading profile versus time. The online computer software provides the user with friendly interface for retrieving the archived data and analyzing the history of alarms.

The explosive material known as Composition C4, or simply C4, is an RDX based military grade explosive. RDX itself possesses a negligible vapor pressure at room temperature suggesting it is not a good target for conventional instruments designed to detect vapor phase chemical compounds. Recent research with canines has indicated that a better approach for detecting explosive vapors such as C4 is to focus on a characteristic mixture of impurities associated with the material. These characteristic mixtures of impurity vapors are referred to by canine researchers as the explosive bouquet and are fairly unique to the specific energetic material. In this paper, we will examine and report rotationally resolved infrared spectral signatures for the known compounds comprising the explosive bouquet for C4 based explosives including isobutylene, 2-ethyl-1-hexanol and cyclohexanone.