We report characteristic changes in fluorescence of amyloid-binding dyes Thioflavin T (TfT), pinacyanol (PIN) and related dyes, caused by their interaction with suspended Bacillus spore cultures (B. subtilis, B thuringiensis). The gain in TfT emission in the presence of spores allowed their immediate detection in aqueous suspensions, with a sensitivity limit of < 105 spores per ml. The spectroscopic signatures are consistent with a large number of binding sites for the two dyes on spore coats. The possible structural relationship of these dye binding loci with characteristic motifs (β-stacks) of amyloid deposits and other misfolded protein formations suggests new designs for probing biocontamination and also for clinical studies of non-microbial human pathogens (e.g., amyloid-related protein aggregates in prion-related transmissible encephalopathies or in Alzheimer's disease). Also reported is a special screening technique that was designed and used herein for calibration of new detection probes and assays for spore detection. It employed spectroscopic interactions between the candidate amyloid stains and poly(vinylpyrrolidone)-coated colloid silica (Percoll) nanoparticles that also display remarkable parallelism with the corresponding dye-amyloid and dye-spore reactivities. Percoll may thus find new applications as a convenient non-biological structural model mimicking the putative probe-targeted motifs in both classes of bioanalytes. These findings are important in the design of new probes and assays for important human pathogens (i.e. bacterial spores and amyloidogenic protein aggregates).

The early detection of biological warfare (BW) agents before any symptoms are present is critical for saving lives and reducing cost of therapy. Protein expression in T-cells represents one of the earliest detectable cellular signaling events to occur in response to the exposure to various toxins or BW agents. In order to fully understand a cellular response to a particular BW agent, it is often necessary to monitor the expression of specific proteins. Therefore, we have developed a novel class of surface enhanced Raman scattering (SERS) immuno-nanosensors for the real-time monitoring of protein expression within individual living cells. In this work, we have developed and optimized novel nanosphere-based silver coated SERS nanosensors for the detection of proteins at cellular levels. SERS nanosensors were optimized in terms of nanosphere size, silver coating methods, number of silver layers, antibody binding and affinity. These nanosensors are capable of being inserted into individual cells and non-invasively positioned to the sub-cellular location of interest using optical tweezers. They were constructed from monodisperse silica nanospheres. These nanospheres were condensed from tetraalkoxysilanes in an alcoholic solution of water and ammonia. Accurate control of the silica nanospheres’ diameter was achieved by varying the reaction conditions. Nanosphere-based SERS immuno-nanosensors were then prepared by depositing multiple layers of silver on silica spheres, followed by binding of the antibody of interest to the silver. In binding the antibodies, different cross linker agents were characterized and compared. On one end, each of these cross linker agents contained sulfur or isothiocyanate groups which bound to the silver surface, while the other end contained a carboxylic or primary amine group which reacted readily with the antibodies. In order to improve sensitivity of these nanosensors, optimal silver surface coverage with crosslinkers was determined. Following binding of antibodies, evaluation of the nanosensors was performed by monitoring the SERS spectra of the nanosensors prior to and following exposure to the antigen of interest. These results showed reproducible differences in the SERS spectra upon exposure to the antigens confirming their ability to monitor trace amounts of antigen. In particular, these SERS-based nanosensors were shown successfully detect human insulin at trace levels.

The surface enhanced Raman scattering (SERS) spectra of vegetative whole-cell bacteria were obtained using in-situ grown gold nanoparticle cluster-covered silicon dioxide substrates excited at 785 nm. SERS spectra of Gram-negative bacteria; E. coli and S. typhimurium, and Gram-positive bacteria; B. subtilis, B. cereus, B. thuringeinsis and B. anthracis Sterne, have been observed. Raman enhancement factors of ~10⁴-10⁵ per cell are found for both Gram positive and Gram negative bacteria on this novel SERS substrate. The bacterial SERS spectra are species specific and exhibit greater species differentiation and reduced spectral congestion than their corresponding non-SERS (bulk) Raman spectra. Fluorescence observed in the 785 nm excited bulk Raman emission of Bacillus species is not apparent in the corresponding SERS spectra. The surface enhancement effect allows the observation of Raman spectra at the single cell level excited by low incident laser powers (< 3 mW) and short data acquisition times (~20 sec.). Comparison with previous SERS studies suggests that these SERS vibrational signatures are sensitively dependent on the specific morphology and nature of the SERS active substrate. Exposure to biological environments, such as human blood serum, has an observable effect on the bacterial SERS spectra. However, reproducible, species specific SERS vibrational fingerprints are still obtained. The potential of SERS for detection and identification of bacteria with species specificity on these gold nanoparticle coated substrates is demonstrated by these results.

Surface-Enhanced-Raman-Scattering (SERS) is potentially a very sensitive spectroscopic technique for the detection of biological agents (i.e., proteins, viruses or bacteria). However, since initial reports, its utility has not been realized. Its limited acceptance as a routine analysis technique for biological agents is largely due to the lack of reproducible SERS-active substrates. Most established SERS substrate fabrication schemes are based on self-assembly of the metallic (typically, Au, Ag, Pt, Pd or Cu) particles responsible for enhancement. Further, these protocols do not lend themselves to the stringent control over the enhancing feature shape, size, and placement on a nanometer scale. SERS can be made a more robust and attractive spectroscopic technique for biological agents by developing quantifiable, highly sensitive, and highly selective SERS-active substrates. Electron Beam Lithography (EBL), a semiconductor fabrication technique, can be utilized to address many of the obstacles which have limited the broad acceptance of SERS. Specifically, EBL can be employed to precisely control the shape, size and position (on a nanometer scale) of the SERS substrate enhancing features. Since Ashkin's seminal work in the early 1970s, the optical trapping phenomenon has been broadly accepted as a powerful tool to study micrometer-scale biological particles. Recently, research in our laboratory has demonstrated that it is possible to combine the Optical Trapping phenomenon and SERS to develop a high sensitivity spectroscopic technique for the detection of individual bacterial spores. Highly reproducible SERS-active substrates fabricated using EBL have been utilized with this novel spectroscopic technique to investigate the utility of SERS technique for the spectral discrimination of bacterial spores. The SERS substrate fabrication methodology, substrate reproducibility and SERS spectral reproducibility will be discussed.

The preliminary data presented here suggests that direct coating of biological agent with DNA capture elements and organic semiconductor (DALM) with chelated rare earths such as scandium, europium or neodymium can be used to track the agent, even when the biological components have been subsequently destroyed. The use of these three taggant components in conjunction with each other affords the opportunity to determine the presence of the biological agent by several methods---laser induced plasma spectroscopy, thermochemiluminescence, mass spectroscopy, polymerase chain reaction (PCR; if the primers are left on the DCEs or the agent's own DNA is used as the source of the amplicon). The specific DCE-labeling or PCR allows for confirmation of physical measurement results as specific to the agent.

There is an urgent need for new chemical sensors for defense and security applications. In particular, sensors are required that can provide higher sensitivity and faster response in the field than existing baseline technologies. We have been developing a new solid-state chemical sensor technology based on microscale polymer composite fiber arrays. The fibers consist of an insulating polymer doped with conducting particles and are electrospun directly onto the surface of an interdigitated microelectrode. The concentration of the conducting particles within the fiber is controlled and is near the percolation threshold. Thus, the electrical resistance of the polymer fiber composite is very sensitive to volumetric changes produced in the polymer by vapor absorption. Preliminary results are presented on the fabrication and testing of the new microsensor. The objective is to take advantage of the very high surface to volume ratio, low thermal mass and linear geometry of the composite fibers to produce sensors exhibiting an extremely high vapor sensitivity and rapid response. The simplicity and low cost of a resistance-based chemical microsensor makes this sensing approach an attractive alternative to devices requiring RF electronics or time-of-flight analysis. Potential applications of this technology include battlespace awareness, homeland security, environmental surveillance, medical diagnostics and food process monitoring.

Ion Mobility Spectroscopy (IMS) is the most widespread detection technique in use by the military for the detection of chemical warfare agents, explosives, and other threat agents. Moreover, its role in homeland security and force protection has expanded due, in part, to its good sensitivity, low power, lightweight, and reasonable cost. With the increased use of IMS systems as continuous monitors, it becomes necessary to develop tools and methodologies to ensure optimal performance over a wide range of conditions and extended periods of time. Namely, instrument calibration is needed to ensure proper sensitivity and to correct for matrix or environmental effects. We have developed methodologies to deal with the semi-quantitative nature of IMS and allow us to generate response curves that allow a gauge of instrument performance and maintenance requirements. This instrumentation communicates to the IMS systems via a software interface that was developed in-house. The software measures system response, logs information to a database, and generates the response curves. This paper will discuss the instrumentation, software, data collected, and initial results from fielded systems.

General Dynamics ATP (GDATP) and Sionex Corporation (Sionex) are carrying out a cooperative development for a handheld chemical agent detector, being called JUNO, which will have lower false positives, higher sensitivity, and improved interference rejection compared with presently available detectors. This enhanced performance is made possible by the use of a new principle of ion separation called Differential Mobility Spectrometry (DMS). The enhanced selectivity is provided by the field tunable nature of the Sionex differential mobility technology (microDMxTM) which forms the analytical heart of the JUNO system and enables fingerprinting of molecules by characterization of the ionized molecular behavior under multiple electric field conditions. This enhanced selectivity is valuable in addressing not only the traditional list of chemical warfare agents (CWA) but also the substantial list of Toxic Industrial Compounds (TICs) and Toxic Industrial Materials (TIMs) which may be released in warfare or terrorist situations. Experimental results showing the ability of the microDMx to reject interferences, detect and resolve live agents are presented. An additional breakthrough in the technology was realized by operating the device at a reduced pressure of around 0.5 atmospheres. This reduced pressure operation resulted in roughly doubling the spectrometers resolution over what has previously been reported [1]. Advances have also been made in power consumption and packaging leading to a device suitable for portable, handheld, applications. Experimental results illustrating the performance of the microDMx technology employed in JUNO are highlighted.

Biological Aerosol Warning Sensor (BAWS) detects suspect airborne particles of biological origin that are in a specified size range consistent with respiration using laser induced fluorescence. The system determines if the suspect particles are consistent with naturally existing particles of the operating environment. If the number of suspect particles detected is of significant quantity, over a sufficient amount of time, and the particles are not common to the environment; an alarm is issued. It does all of this in real time, issuing triggers within a minute of the onset of an event. Provided is an overview of the techniques employed by the BAWS for detecting biological aerosols and the functionality that BAWS provides to a multistage system like the Joint Biological Point Detection System JBPDS.

The US Army Edgewood Chemical Biological Center (ECBC) is the leader in development of military systems for chemical and biological defense, in collaboration with all Services, other Government laboratories, academia, and industry. Chemical and biological optical sensing principles, unique capabilities, state-of-the-art sensors, and emerging technologies will be discussed. In order to acquire highly quantified data, study the effects of variables such as particle size distribution on backscatter coefficients, perform iterative aerosol algorithm development, and characterize breadboards, a novel "windowless" Vortex Chamber utilizing air curtains was developed and built at ECBC. The chamber has been successfully shown to contain a cloud of known size, concentration, and particle size distribution for 10-15 minutes. Near-term plans are focused on characterization of breadboards for standoff bio discrimination and deducing absolute backscatter coefficients from Vortex Chamber data.

This paper presents a system-level description of the Joint Services Lightweight Standoff Chemical Agent Detector (JSLSCAD). JSLSCAD is a passive Fourier Transform InfraRed (FTIR) based remote sensing system for detecting chemical warfare agents. Unlike predecessor systems, JSLSCAD is capable of operating while on the move to accomplish reconnaissance, surveillance, and contamination avoidance missions. Additionally, the system is designed to meet the needs for application on air and sea as well as ground mobile and fixed site platforms. The core of the system is a rugged Michelson interferometer with a flexure spring bearing mechanism and bi-directional data acquisition capability. The sensor is interfaced to a small, high performance spatial scanner that provides high-speed, two-axis area coverage. Command, control, and processing electronics have been coupled with real time control software and robust detection/discrimination algorithms. Operator interfaces include local and remote options in addition to interfaces to external communications networks. The modular system design facilitates interfacing to the many platforms targeted for JSLSCAD.

A method for the passive remote monitoring of chemical vapours by differential Fourier-transform infrared (FTIR) radiometry is presented for determining the characteristics of chemical vapour plumes released from stacks at various horizontal distances from the FTIR sensor. The measurement technique is based on the use of a double-beam FTIR spectrometer that is optimized for optical subtraction. A description is given of the customized interferometer, along with a discussion of the analysis algorithm that has been developed for the on-line detection, identification and quantification of chemical vapour plumes. Experimental results are presented from a number of open-air trials that demonstrate the passive detection, identification and quantification of vapour plumes, which consist of DMMP, ammonia, methanol and sulphur hexafluoride gases probed at standoff distances of up to 1.5 km. In addition, recent results obtained at the open-air range at Defence Research and Development Canada (DRDC)-Valcartier are presented for the detection of a vapour plume of sulphur hexafluoride (SF₆) measured at a standoff distance of 5.7 km. This work represents the first such measurement reported in the open literature for such a large standoff distance. These results clearly demonstrate the applicability of the differential radiometry approach for successfully detecting and identifying chemical vapour clouds located at long distances from the sensor.

Eutrophication disturbs the ecological balance in the Lake Okeechobee due to high concentration of phosphorus emanated from the regions in the lake's drainage basin. Ability of measuring phosphorus (P) concentrations of water in the Lake Okeechobee itself is very important. Furthermore, monitoring P in its drainage basins is crucial in order to find the cause of P loading and contributing regions. Also, inexpensive real-time sensing capability for a large area in a short time would help scientist, government agents, and civilians to understand the causes, spot the high-risk areas, and develop management practices for restoring the natural equilibrium. In order to measure P concentrations in the Lake Okeechobee drainage basin, airborne hyperspectral images were taken from five representative target sites by deploying a modified queen air twin engine aircraft. Each flight line covered a swath of approximately 365 m wide. Spatial resolution was about 1 m. Spectral range covered was between 412.65 and 991.82 nm with an approximate of 5 nm spectral resolution. Ground truthing was conducted to collect soil and vegetation samples, GPS coordinates of each location, and reflectance measurement of each sample. On the ground, spectral reflectance was measured using a handheld spectrometer in 400-2500 nm. The samples were sent to a laboratory for chemical analysis. Also diffuse reflectance of the samples was measured in a laboratory setting using a spectrophotometer with an integrating sphere. Images were geocorrected and rectified to reduce geometric effect. Calibration of images was conducted to obtain actual reflectance of the target area. Score, SAM (Spectral Angle Mapping), SFF (Spectral Feature Fitting) were computed for spectral matching with image derived spectral library.

This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and other, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-speciific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncatalogued agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.

Remote detection of chemical vapors in the atmosphere has a wide range of civilian and military applications. In the past few years there has been significant interest in the detection of effluent plumes using hyperspectral imaging spectroscopy in the 8-12- m atmospheric window. A major obstacle in the full exploitation of this technology is the fact that everything in the infrared is a source of radiation. As a result, the emission from the gases of interest is always mixed with emission by the more abundant atmospheric constituents and by other objects in the sensor field of view. The radiance fluctuations in this background emission constitute an additional source of interference which is much stronger than the detector noise. The purpose of this paper is threefold. First, we review the thin plume approximation, the resulting additive signal model, and the key differences between reflective and emissive radiance signal models. Second, based on the additive signal model we derive two families of detection algorithms using the generalized likelihood ratio test. The first family models the background using a multivariate normal distribution whereas the second family models the background using a linear subspace. Finally, we present a taxonomy of the available algorithms and show that some other ad-hoc approaches, like orthogonal background suppression, are simplified special cases of optimally derived detectors.

Despite the wide spread need for optical cross-section data on single spore bio-aerosols, available databases are sparse and unreliable. Information reported is based on short path measurements on high concentration media containing particle clusters. This represents an upper bound to the single spore cross-section. Measurements on single spore aerosolized media demand long path lengths and moderate particle concentration. Transmittance measurements need to be in the single scatter limit as well. These requirements are often difficult to meet. We present a procedure that leads to aerosol extinction and backscatter cross-sections in a straightforward manner. Transmittance measurements of thin films of bio-aerosols are used to obtain the bulk refractive index. This result and the measured size distribution can be used in a T-matrix calculation to yield the desired cross-sections. To illustrate this technique, infrared cross-sections are obtained for Bacillus globigii.

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis are used to develop a robust classification algorithm that reliably discriminates between conventional and simulated chemical/biological artillery rounds via acoustic signals produced during detonation utilizing a generic acoustic sensor. Based on the transient properties of the signature blast distinct characteristics arise within the different acoustic signatures because high explosive warheads emphasize concussive and shrapnel effects, while chemical/biological warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. The ensuing blast waves are readily characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. Unique attributes can also be identified that depend upon the properties of the gun tube, projectile speed at the muzzle, and the explosive burn rates of the warhead. The algorithm enables robust classification of various airburst signatures using acoustics. It is capable of being integrated within an existing chemical/biological sensor, a stand-alone generic sensor, or a part of a disparate sensor suite. When emplaced in high-threat areas, this added capability would further provide field personal with advanced battlefield knowledge without the aide of so-called "sniffer" sensors that rely upon air particle information based on direct contact with possible contaminated air. In this work, the discrete wavelet transform is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 2km while maintaining temporal sequence of the data to keep relevance to the transient differences of the airburst signatures. Highly reliable discrimination is achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition the neural network then is capable of classifying new airburst signatures as Chemical/Biological or High Explosive.

Detection of trace gases by ion mobility spectroscopy (IMS) has become common in recent years. In fact, IMS devices are the most commonly deployed military devices for the detection of classical chemical warfare agents (CWA). IMS devices are protecting the homeland by aiding first responders in the identification of toxic industrial chemicals (TICs) and providing explosive and narcotic screening systems. Spurred by the asymmetric threat posed by new threat agents and the ever expanding list of toxic chemicals, research in the development, improvement, and optimization of IMS systems has increased. Much of the research is focused on increasing the sensitivity and selectivity of IMS systems. Ion optics is a large area of study in the field of mass spectrometry, but has been mostly overlooked in the design and development of IMS systems. Ion optics provides insight into particle trajectories, duty cycle, and efficiency of these systems. This paper will outline the role that ion optics can have in the development of IMS systems and introduce the trade space for traditional IMS as well as differential mobility spectroscopy.

The Receiver Operating Characteristic curve (ROC) has long been used in medical applications to compare screening and diagnostic methods. As the threshold used by any screening or diagnostic method is changed, the operating characteristics of the method, such as the number of true positive and false negative determinations changes as well. The ROC curve is one way to characterize the changes in order to compare different methods. This definition, however, is difficult to apply to chemical and biological sensors detecting the release of a toxic agent given that there is more than one ROC curve. There is a continuum of ROC curves corresponding to a continuum of release levels. A new definition of ROC curves has been adopted for chemical and biological sensors which will reduce the continuum of curves to a single curve. This paper presents a methodology to estimate ROC curves using this new definition.

There is wide variability in measured optical cross-sections for bio-aerosols. This variability may be due to a variety of causes, such as multiple scatter, particle agglomeration, etc. There are wide variations in numerically predicted cross-sections as well. In this case, the variability may be due to uncertainties in particle size distributions and complex refractive indices. Another source of variability in the numerical predictions that places them at odds with measured cross-sections is unrealistic assumptions about shape. For example, it is well known that spheres of a given volume are maximally efficient in backscatter. Thus, such an assumption produces unrealistically high backscatter cross-section estimates. In an attempt to elucidate some of the variability in measured and calculated data, we explore the sensitivity to the various parameters affecting these cross-sections. We explore the effects from the near into the far IR, of variations in particle size distribution, refractive index, and shape. Refractive index data are from the literature as well as our own laboratory. Numerical calculations are made using T-matrix algorithms for randomly oriented particles. Calculated results are compared with experimental measurements from the literature and with measurements in our own laboratory. Results of this sensitivity study are important in any remote measurement system designed to discriminate between particular bio-aerosol species and ambient aerosols.