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We are developing algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures when they are deposited as particles on surfaces. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The algorithm utilizes a convolutional neural network (CNN) that has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present an approach to generate large libraries of synthetic infrared reflectance spectra for use in training and testing the CNN. We demonstrate advancements in the number of analytes, a method to generate synthetic substrate spectra, and the benefits of subtracting the substrate “background” to train and test the CNN on the resulting differential spectra.

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

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

We present the development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) in the wavelength range of 7 to 12 microns, are directed to a target to interrogate its spectral features. The “backscatter” return signals from target chemicals are spectrally discriminated by an LWIR spatial heterodyne spectrometer (SHS). The SHS offers high throughput and full spectral coverage in each single frame from an IR imaging array. This presentation will cover the performance and optimization of FP-QCLs for this broadband spectroscopic application. We will also discuss the operation and processing of SHS images to extract spectral information. Finally, we will present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.

Far-field infrared (IR) spectroscopy techniques, such as ellipsometry and FTIR, can yield extremely accurate measurements of the optical constants (n,k) which characterize the electronic and lattice-structural degrees of freedom in novel materials and devices. However, large systematic uncertainty has plagued extensions of these techniques to small length scales. A low uncertainty embodiment would enable high quality studies of, for example, the newest correlated condensed matter systems - where typically only a small sample is available early on. Here we present mature far-field IR microscope. Most notably, an asymmetric interferometer is used to directly measure the infrared reflectance amplitude and absolute phase shift. The complex optical constants can then be extracted without the large uncertainty that arises with an amplitude measurement alone.

In this study, we have developed a simultaneous grating spectroscopy using a broadband IR laser source capable of detecting moving targets in real time. The broadband IR laser source operated in pulsed mode provides a broad spectral range, which covers absorption bands of many chemical analytes. The laser operating conditions were optimized to cover the broadest wavelength range spanning spectral features for the analytes of interest, based on a detailed understanding of the broadband source. This measured the signal from two samples, a 1% acetaminophen KBr pellet sample and toluene in a gas cell. These samples were characterized by illuminating them with the IR broadband source and collecting the transmitted or reflected signal through a grating spectrometer and onto an IR focal plane array (FPA). The results clearly show discrete peaks comparable to the FTIR reference spectra and the spectral features of the samples were successfully discriminated. We believe that the proof of concepts presented here are of broad applicability and will aid advanced real-time standoff detection research.

In this work, we are developing a custom-built broadband laser source in the Mid-LWIR range by combining several high power FP-QCLs for a single snap shot application. To minimize temperature variation or reduce the thermal load while the FP-QCL emits at high currents, the FP-QCL was operated in pulsed mode with varying diode temperatures and applied currents. The spectral outputs in pulsed mode were temporally resolved using a step scan FTIR spectrometer. FP mode peaks typically broaden by driving higher currents. FP mode hopping, emerging, and disappearing were observed during the laser pulse length (3000 ns) at different applied current values. The ideal spectral characteristics for a single snap shot application are discussed, with respect to a broad spectral bandwidth, a flat-top power profile, and high spectral power density.

We are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images. The QCL tunes across the full wavelength range from 6 – 11 μm. Full 128 X 128 pixel frames from the FPA are collected at up to 1610 frames per second and comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify traces of explosives. The algorithm utilizes a convolutional neural network (CNN) and has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present backscatter data and hyperspectral image mapping from a car panel substrate deposited with traces of the explosive RDX. We have used a mask to restrict the RDX analyte deposition to small 4 mm diameter areas. The results presented here were measured at 1 meter standoff.

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

Optically Super-resolved InfraRed Imaging micro-Spectroscopy (OSIRIS) permits the collection of fully broadband hyperspectral images at the maximum optical spatial resolution. Modulated long wavelength light is directed onto the sample, while a short wavelength probe beam senses the resultant modulation in local temperature. Thus, OSIRIS constitutes the full generalization of color vision to full spectral bandwidth with spatial resolutions up to the maximum achievable with far-field optics (λprobe/4NA). The non-contact nature of OSIRIS permits deep tomography and real-time imaging, while preserving the versatility and ease of use of optical microscopy.

This study describes parametric modeling of diffuse IR reflectance for sparsely surface-distributed particles of the explosive RDX. Diffuse IR spectra are modeled using a formulation that considers spectral features due to target-material reflectance, i.e., RDX, substrate reflectance and resonance scattering resulting from finite sizes of surface-distributed particles. The results of this study demonstrate an approach for parametric modeling of diffuse IR reflectance for sparsely surface-distributed particles. The mathematical formulation of this approach is that of a phenomenological scattering-matrix representation.

We report the time resolved characterization of Fabry-Perot quantum cascade lasers (FP-QCLs). We are developing a custom-built broadband laser source in the Mid-LWIR range by combining several high power FP-QCLs for a single snap shot application. This white light source would enable not only stand-off detection applications in a single snapshot but also new data collection modalities such as live, real-time chemical imaging, requiring extremely rapid measurements. In this study, the two FP-QCLs were operated in CW and pulsed modes with varying applied currents and diode temperatures to optimize the best laser operation condition to cover a broad spectral range including spectral features for the analytes of interest. To understand mode behavior of the FP-QCLs in a short period of time, the spectral output for each test condition was temporally resolved. Under most of the conditions, FP mode hopping was observed during the time evolution through the pulse length (3000 ns). Based on the time-resolved spectra, the ideal spectral characteristics for a single snap shot application are discussed, with respect to a broad spectral bandwidth, a flat-top power profile, and high spectral power density.

Reflectance (emittance) spectroscopy, especially at infrared wavelengths, continues to grow in utility as an analytical technique for contact, standoff, and remote sensing. The reflectance spectra of solids, however, are complex, depending on many parameters, even for the same material. Granule or powder particle size, crystal morphology, layer thickness, and substrate material all affect the spectral distribution of reflected light. However, such phenomena can all be modeled if the optical constants n(ν) and k(ν) are available. If the quantitative absorption coefficient K(ν) is known, the k value can be obtained via the relation k(ν) = 2.303K(ν)/4πν. The absorption coefficient can in turn be derived from a simple KBrpellet infrared absorption measurement, provided the pellet mass ratio is prepared quantitatively. The method requires the pellet’s mass and diameter, along with the analyte mass fraction and density. In this paper we demonstrate the requisite experimental details in preparing the pellets, as well as methods to reduce light scattering in order to obtain more quantitative values. Theoretical methods to derive the related optical constants will also be detailed, in particular the assumptions used to obtain the scalar refractive index n. Ideally, this value is known or measured separately, but in some cases we have found that it can be approximated (first approximation) for most organic chemicals by n~1.5 at the shortest wavelength. The results are presented for a couple of species.

Trace particle quantities of explosives left behind by those handling explosives materials present an opportunity to identify both the handlers, secondary handlers and the objects they have contacted. Understanding the nature of these particles is critical for tailoring optical detection strategies as well as non-optical contact harvesting methods. We are working towards developing a model to understand and quantify the nature of particles transferred from the hands to different substrate surfaces. In this preliminary paper we report on a newly developed finger test-bed to produce a robotically controlled series of fingerprints, with an artificial finger designed to mimic the physicochemical properties of the human finger. In an initial set of experiments, we examine the effect of a range of applied forces, the effect of a range of initial particle sizes, and the serial print number on the deposited mass and deposited particle sizes, for a surrogate explosive loaded as particles on gloved fingers which are subsequently pressed against a set of clean glass slides.

The results from infrared backscatter imaging spectroscopy on a mobile platform for stand-off detection of trace amounts of explosive materials on relevant substrates are presented. This technique utilizes an array of tunable infrared quantum cascade lasers to illuminate targets. The spectral range of the QCL system spans from 6 - 11 μm, which enables excitation of a wide variety of absorption bands present in analytes of interest. Targets are prepared by sieving particles through a 20 μm mesh onto substrates to simulate relevant qualities (particle size, fill factor, and mass loading) expected of real world targets. The backscatter signal from targets is collected with an IR focal plane array. This information is stored in a hyperspectral image cube to allow for post processing in a detection algorithm. We demonstrate the selectivity and sensitivity of the discussed technique down to the nanogram level for RDX and PETN on glass. Spectra are generated by extracting the signal from small regions of interest to simulate targets with miniscule coverage areas. Preliminary comparison of backscatter data with simulated data from a model that incorporates particle size, mass loading, and substrate response show good agreement. Confusant agents, such as sand, are introduced to the targets loaded with analyte to illustrate the selectivity of this technique. The results of these studies are presented, along with future improvements to the technique.

Machine learning based perception algorithms are increasingly being used for the development of autonomous navigation systems of self-driving vehicles. These vehicles are mainly designed to operate on structured roads or lanes and the ML algorithms are primarily used for functionalities such as object tracking, lane detection and semantic understanding. On the other hand, Autonomous/ Unmanned Ground Vehicles (UGV) being developed for military applications need to operate in unstructured, combat environment including diverse off-road terrain, inclement weather conditions, water hazards, GPS denied environment, smoke etc. Therefore, the perception algorithm requirements are different and have to be robust enough to account for several diverse terrain conditions and degradations in visual environment. In this paper, we present military-relevant requirements and challenges for scene perception that are not met by current state-of-the-art algorithms, and discuss potential strategies to address these capability gaps. We also present a survey of ML algorithms and datasets that could be employed to support maneuver of autonomous systems in complex terrains, focusing on techniques for (1) distributed scene perception using heterogeneous platforms, (2) computation in resource constrained environment (3) object detection in degraded visual imagery.

We are developing a cart-mounted platform for chemical threat detection and identification based on active LWIR imaging spectroscopy. Infrared backscatter imaging spectroscopy (IBIS) leverages IR quantum cascade lasers, tuned through signature absorption bands (6 - 11 μm) in the analytes while illuminating a surface area of interest. An IR focal plane array captures the time-dependent backscattering surface response. The image stream forms a hyperspectral image cube composed of spatial, spectral and temporal dimensions as feature vectors for detection and identification. Our current emphasis is on rapid screening. This manuscript also describes methods for simulating IBIS data and for training detection algorithms based on convolutional neural networks (CNN). We have previously demonstrated standoff trace detection at several meters indoors and in field tests, while operating the lasers below the eye-safe intensity limit (100 mW/cm2). Sensitivity to explosive traces as small as a single grain (~1 ng) has been demonstrated. Analytes tested include RDX, PETN, TNT, ammonium nitrate, caffeine and perchlorates on relevant glass, plastic, metal, and painted substrates.

Standoff detection of hazardous materials using infrared backscattering spectroscopy shows promise due to its speed of detection, sensitivity, chemical specificity, eye safety, and the ability to perform detection in a stealthy manner. However, infrared diffuse reflectance spectra of trace particles on substrates exhibit a strong dependence on substrate type, particle size, and mass loading. This has a negative impact on the performance of detection algorithms and false alarm rates when using commercially available spectral databases. We present two models that are quickly computed yet capture most of the observed spectral features. Model I is best suited for calculating the diffuse reflectance spectra of trace amounts of particles on relatively smooth substrates, while model II extends the applicability of model I to particles on very rough substrates. The models can be used for algorithm development and training. The main inputs to the models are the analyte and substrate optical constants as a function of wavelength, and the particle size distribution and average mass loading. The accuracy of the models was checked by comparing to experimentally measured diffuse reflectance spectra of several carefully prepared samples.

We are using active infrared (IR) spectroscopic imaging to detect trace explosives on surfaces at proximal distances up to a few meters. The technology comprises an IR quantum cascade laser (QCL) for illumination and an IR focal plane array (FPA) sensor to collect signal backscattered from surfaces of interest. By sweeping the wavelength of the QCL while collecting image frames with the FPA, we generate an active hyperspectral image (HSI) cube. The HSI cube contains both spatial and spectral information, where the spectrum of a pixel, or region of interest within the image, can be extracted and compared against a known threat library. These cubes are fed into a convolutional neural network (CNN) trained on purely synthetic data to identify chemicals in the field of view. The CNN identifies chemicals by their IR signature and identifies their location within the image.

We are developing a stand-off technique for the detection of trace amounts of explosive materials. The motivation behind this work is to prevent loss of life and injury to military and civilian personal by detecting threats at distance. The matured technique will allow for the facile identification of possible threats with minimum user effort and enough time to take appropriate action. This manuscript illustrates the results from our infrared backscatter imaging spectroscopy mobile stand-off method to detect trace amounts of explosive materials under laboratory conditions. The described technique uses tunable quantum cascade lasers, with full spectral coverage from 6-11 μm, to illuminate a target and an infrared focal plane array to collect the backscattered signal into hyperspectral images cubes. The quantum cascade lasers are operated under eye safe levels which allows for safe and stealthy probing of objects, vehicles, and even people. Experiments are performed on tilted substrates to simulate real world conditions where it is unlikely to collect the specular reflections. The collected hyperspectral image cubes contains spectral, spatial, and temporal information that can be fed to a detection algorithm.

Optical (visible) microscopy has established itself as an invaluable tool in Materials Science, and perhaps the canonical technique in Biology. Unfortunately, an optical image provides extremely limited information regarding chemical composition. A great deal of effort has been spent to circumvent this limitation through the use chemical dyes - most notably fluorescent markers - to achieve contrast between different chemical species. Infrared spectroscopy is an ideal technique to chemically fingerprint materials. Specifically, infrared microspectroscopy holds great promise as a labelless technique to achieve chemically specific microscopy. Unfortunately, the long wavelengths of the infrared lead to low spatial resolution in infrared microscopy, on the order of several microns. Traditionally, this limitation has been circumvented via scanning probe techniques such as s-SNOM and AFM-IR. While the scanning probe techniques provide excellent resolution, their contact nature and low signal levels limit the speed at which images can be acquired. We have developed a new technique to collect infrared hyperspectral images below the IR diffraction limit. Optically Sensed photothermal InfraRed Imaging micro-Spectroscopy (OSIRIS) permits the construction of infrared images on a resolution limited by the wavelength of the probe beam. In this technique, an infrared laser is used to excite the sample, while a short wavelength probe beam senses the resultant change in temperature. With this technique, hyperspectral images can be acquired orders of magnitude faster than the scanning probe techniques. Furthermore, a confocal setup permits tomography, which is extremely limited in the scanning probe techniques due to their surface nature.

Rapid scanning quantum cascade lasers are utilized in the detection of trace amounts of explosive materials. Infrared backscatter imaging spectroscopy employs a quick tuning infrared quantum cascade laser system to illuminate targets with mid-IR light, 6 – 11 μm in wavelength, and to perform spectroscopic measurements in less than one second. A narrow cone of the signal backscattered from targets at standoff distance is collected and imaged onto a liquid nitrogen cooled MCT focal plane array. This backscattered signal is processed into a hyperspectral image cube containing spectral and spatial information. The analysis of the experimental data measured with the system is discussed. This includes the processing of the raw camera frames (using signals from individual components of the system) into discrete wavelength bins, typically 0.01 μm in width. Spectra are generated by plotting the signal from regions of interest, typically clusters of adjacent pixels within the frames, as a function of the wavelength associated with the binned frames. These spectra are compared against the FTIR diffuse reflectance of the analytes on an equivalent substrate for identification. Methods to optimize signal to noise and produce identifications with high confidence are presented. For a single experiment, taking less than 1 second, with the camera running at full frame over 16,000 individual spectra are generated. Targets are prepared by sieving and also dry transfer to mimic real world threats, in trace amounts and on relevant substrates. Traces of explosives, as well as illicit drugs are investigated.

A significant remaining challenge in chemical detection is the ability to rapidly detect with high fidelity a full suite of CWAs and TICs in a single point-detection system. Gas chromatography (GC) is a proven laboratory technique that can achieve the stated detection goal, but not at the required speed and not in a wearable (or even portable) form factor. Efforts in miniaturizing GCs yielded small devices, but they remain slow as they retain the end-of-column detection paradigm which results in long elution times of CWAs and TICs. We describe a novel concept of in-column detection by probing the sorbent coating (stationary phase) of a micro-GC column through optical evanescent field interactions in the long-wave infrared (“chemical fingerprint”) spectral region (U.S. Patent US9599567B2). Detection closer to the injection port ensures a rapid response for slow-eluting analytes. Although this results in poor separation (i.e. poor ability to identify chemicals), this is more than compensated by having full IR absorbance spectra at each location. This orthogonal spectral signature (along with GC retention times) is used in a powerful algorithm to quickly identify components in a complex mixture under conditions of incomplete separation. We present results with an ATR-based system that uses a focused tunable quantum cascade laser beam directed by galvo mirrors at points along a molded micro-GC column whose bottom wall is the sorbent coated ATR prism. Efforts are under way to further miniaturize this device by employing novel long-wave-IR photonic waveguides for a truly portable integrated photonic chromatographic detector of CBRNE threats.

Knowledge of the persistence of trace explosives materials is critical to aid the security community in designing detection methods and equipment. The physical and environmental factors affecting the lifetimes of particles include temperature, airflow, interparticle distance, adlayers, humidity, particle field size and vapor pressure. We are working towards a complete particle persistence model that captures the relative importance of these effects to allow the user, with known environmental conditions, to predict particle lifetimes for explosives or other chemicals. In this work, particles of explosives are sieved onto smooth glass substrates using particle sizes and loadings relevant to those deposited by fingerprint deposition. The coupon is introduced into a custom flow cell and monitored under controlled airflow, humidity and temperature. Photomicroscopy images of the sample taken at fixed time intervals are analyzed to monitor particle sublimation and characterized as a size-independent radial sublimation velocity for each particle in the ensemble. In this paper we build on previous work by comparing the relationship between sublimation of different materials and their vapor pressures. We also describe the influence of a sebum adlayer on particle sublimation, allowing us to better model ‘real world’ samples.

Techniques which apply tunable quantum cascade lasers (QCLs) for target illumination suffer from fluctuations of the laser beam direction. This manuscript describes a method to stabilize the beam direction by using an active feedback loop. This approach corrects and stabilizes the laser pointing direction using the signal from a 4-element photo sensor as input to control an active 2 dimensional Galvo mirror system. Results are presented for measurements using known perturbations as well as actual mode hops intrinsic to external cavity QCL during wavelength tuning.

Due to their high brightness, infrared (IR) lasers (such as tunable quantum cascade lasers, QCLLs) are very attractive illumination sources in both stand-off spectroscopy and micro-spectroscopy. In fact, they are the enabling device for trace-level spectroscopy. However, due to their high coherence as laser beams, QCLLs can cause speckle, especially when illuminating a rough surface. This is highly detrimental to the signal-to-noise ratio (SNR) of thee collected spectra and can easily negate the gains from using aa high brightness source. In most cases, speckle reduction is performed at the expense of optical power. In this paper, we examine several speckle reduction approaches and evaluate them for their ability to reduce speckle contrast while at the same time preserving aa high optical throughput. We analyze multi-mode fibers, integrating spheres, and stationary and moving diffusers for their speckle reduction potential. Speckle-contrast is measured directly by acquiring beam profiles of the illumination beam or, indirectly, by observing speckle formation from illuminating a rough surface (e.g. Infragold® coated surface) with an IR micro-bolometer camera. We also report on a novel speckle-reducing device with increased optical throughput. We characterize speckle contrast reduction from spatial, temporal and wavelength averaging for both CWW and pulsed QCLs. Examples of effect of speckle-reduction on hyperspectral images in both standoff and microscopy configurations are given.

This manuscript describes a mobile stand-off detection and identification of trace amounts of hazardous materials, specifically explosives. The technique utilizes an array of tunable infrared quantum cascade lasers as an illumination source which spans wavelengths from 6 to 11 μm, operated at eye-safe power levels. This spectral range enables excitation of a wide variety of absorption bands present in analytes of interest. The laser is modulated to produce a 50% duty cycle, square wave pulses, and control the frequency of irradiation. The backscatter and photo-thermal signals from samples are measured via an IR focal plane array, which allows for the observation of spatial, temporal, and thermal surface processes. A discussion of how these signals are collected and processed for use in identification of hazardous materials is presented.

We are developing a proximal noncontact detection technique for trace amounts of hazardous materials using diffuse reflectance infrared spectral signatures. Spectroscopy is performed at a distance by tuning a quantum cascade laser system through its wavelength range (6-11 μm) as the active illumination source and collecting a portion of the diffusely reflected light from the target onto an MCT focal plane array. The signals from each collected frame are binned and processed into an image hypercube that contains spectral and spatial information. The primary motivation of this work is to protect the loss of life by detecting trace explosives on contaminated surfaces before attacks occur. Here, we present results from backscatter experiments on trace samples with low loading and fill factors on glass substrates and compare them with a conventional benchtop analysis technique, FTIR diffuse reflectance. The backscatter results illustrate the ability to detect explosives at 1 m distance at the nanogram level, which is beyond the capability of the benchtop FTIR diffuse reflectance measurement.

We experimentally demonstrate speckle noise reduction and beam wander mitigation by using a rotating diamond/KBr pellet and a multimode fiber (MMF). As the diamond/KBr diffuser is rotated, the reflected speckle images that are captured by an infrared camera are temporally averaged. We demonstrate 78% speckle noise reduction by averaging 25 frames, which is within 80% of the theoretical contrast reduction. Large beam position fluctuations are also significantly suppressed by adding the MMF. This combination of beam wander mitigation and speckle reduction offers significant benefits for emerging optical technologies that use quantum cascade lasers as illumination sources.

Motivated by new photothermal techniques to detect trace quantities of illicit substances, we are examining the heat transfer of small particles randomly distributed on surfaces that are selectively heated by a laser beam. For an optimal choice of laser power, illumination time and other parameters, we need formulas describing how the thermal signal depends on particle size, their distribution on the substrate surface and thermo-physical properties of the materials involved. In this manuscript we compare very simple physical models and computational simulations with experimental data from polyethylene microspheres on a polished copper surface, with diameters ranging from 20 to 200 μm. Heat transfer through air dominates the process, both for single particles and between particles in clusters. The influence of high particle densities per substrate area on the heat transfer process is factored into the simulation by using a cell with just one particle and symmetric boundary conditions. Further simulations where irregular-shaped carbon pieces were approximated as lying cylinders indicate that our simple model can also describe the thermal behavior of a wider class of realistic particles on solid surfaces.

Infrared active stand-off detection techniques often employ high power tunable quantum cascade lasers (QCLs) for target illumination. Due to the distances involved, any fluctuation of the laser beam direction and/or beam profile is amplified at the sample position. If not accounted for, this leads to diminished performance (both sensitivity and selectivity) of the detection technique as a direct result of uncertainties in laser irradiance at each imaged pixel of the sample. This is especially true for detection approaches which illuminate a relatively small footprint at the target since the laser beam profile spatial fluctuations are often comparable to the (focused) laser spot size. Also, there is often a necessary trade-off between high output QCL power and beam quality. Therefore, precise characterization of the laser beam profile and direction as a function of laser properties (tuning wavelength, current and operating mode: pulsed or CW) is imperative. We present detailed measurements of beam profiles, beam wander and power fluctuations and their reproducibility as function of laser wavelength and stand-off distance for a commercially available tunable quantum cascade laser. We present strategies for improving beam quality by compensating for fluctuations using a motorized mirror and a pair of motorized lenses. We also investigate QCL mode hops and how they affect laser beam properties at the sample. Detailed mode-hop stability maps were measured.

Those that handle explosives materials invariably become contaminated with particulates of materials, which become entrapped in the grooves of the fingers and are then transferred by contact to other surfaces. These particles provide an evidentiary trail which is useful for security applications, a fact which is enhanced by the fact that many explosives materials of interest have low vapor pressures, augmenting their longevity. The persistence or stability of explosives particles on a substrate is a function of several environmental parameters or particle properties, including vapor pressure, particle size and geometry, airflow, particle field size, substrate topography, humidity, reactivity, adlayers, admixtures, particle areal density, and temperature. In this work we deposited particles of 2,4-dinitrotoluene on standard microscope glass slides by particle sieving and studied their sublimation as a function of temperature and relative humidity. A custom airflow cell allowed us to monitor the particles with in situ photomicroscopy while keeping the airflow over the particles, substrate type, and areal field size constant for each experiment. We define the size-independent radial sublimation velocity for the equivalent sphere of a particle as the parameter to characterize the sublimation rate. The dependence of the sublimation rate for an ensemble of particles on temperature was quantified according the radial sublimation velocity, while the sublimation of 2,4-dinintrotoluene was found to independent of relative humidity between 25-90%.

We are developing a technique for the standoff detection of trace explosives on relevant substrate surfaces using photothermal infrared (IR) imaging spectroscopy (PT-IRIS). This approach leverages one or more compact IR quantum cascade lasers, which are tuned to strong absorption bands in the analytes and directed to illuminate an area on a surface of interest. An IR focal plane array is used to image the surface and detect increases in thermal emission upon laser illumination. The PT-IRIS signal is processed as a hyperspectral image cube comprised of spatial, spectral, and temporal dimensions as vectors within a detection algorithm. The ability to detect trace analytes at standoff on relevant substrates is critical for security applications but is complicated by the optical and thermal analyte/substrate interactions. This manuscript describes a series of PT-IRIS experimental results and analysis for traces of RDX, TNT, ammonium nitrate, and sucrose on steel, polyethylene, glass, and painted steel panels. We demonstrate detection at surface mass loadings comparable with fingerprint depositions ( 10μg/cm2 to 100μg/cm2) from an area corresponding to a single pixel within the thermal image.

This manuscript describes the results of recent tests regarding standoff detection of trace explosives on relevant substrates using a mobile platform. We are developing a technology for detection based on photo-thermal infrared (IR) imaging spectroscopy (PT-IRIS). This approach leverages one or more microfabricated IR quantum cascade lasers, tuned to strong absorption bands in the analytes and directed to illuminate an area on a surface of interest. An IR focal plane array is used to image the surface thermal emission upon laser illumination. The PT-IRIS signal is processed as a hyperspectral image cube comprised of spatial, spectral and temporal dimensions as vectors within a detection algorithm. Increased sensitivity to explosives and selectivity between different analyte types is achieved by narrow bandpass IR filters in the collection path. We have previously demonstrated the technique at several meters of stand-off distance indoors and in field tests, while operating the lasers below the infrared eye-safe intensity limit (100 mW/cm 2 ). Sensitivity to explosive traces as small as a single 10 μm diameter particle (~1 ng) has been demonstrated. Analytes tested here include RDX, TNT, ammonium nitrate and sucrose. The substrates tested in this current work include metal, plastics, glass and painted car panels.

We are developing a technology for stand-off detection based on photo-thermal infrared imaging spectroscopy (PT-IRIS). In this approach, one or more infrared (IR) quantum cascade lasers are tuned to strong absorption bands in the analytes and directed at the sample while an IR focal plane array is used to image the subsequent thermal emissions. In this paper we present recent advances in the theory and numerical modeling of photo-thermal imaging and spectroscopy of particulates on flat substrates. We compare the theoretical models with experimental data taken on our mobile cart-based PT-IRIS system. Synthetic data of the photo-thermal response was calculated for a wide range of analytes, substrates, particle sizes, and analyte mass loadings using their known thermo-physical and optical properties. These synthetic data sets can now be generated quickly and were used to accelerate the development of detection algorithms. The performance of detection algorithms will also be discussed.

Proceedings of the Conference on Spectroscopies in Novel Superconductors, Break-junction tunneling data are reported in Bi2Sr2CaCu2O8+δ over a wide range of hole concentration from underdoped to overdoped. The strong conductance peaks in the superconducting state reveal a single gap consistent with d-wave symmetry. In addition, sharp dips are observed at a voltage, Ω/□, measured with respect to the gap edge. These features are shown to be reproduced in other junction types from the literature including atomically resolved STM and □-axis mesas, establishing their intrinsic character. Trends are observed with doping and temperature which link the dip to the resonance spin excitation and indicate that the quasiparticles are strongly coupled to this mode.

We report tunneling spectroscopy of superconductor-insulator-superconductor break junctions on heavily overdoped Bi2Sr2CaCu2O8 + δ with Tc = 56 K. At T Tc. Above Tc, the tunneling conductance shows a flat background without any indication of a pseudogap near the Fermi level.

High resolution angle-resolved photoemission spectroscopy of highly overdoped Bi 2 Sr 2 CaCu 2 O 8+δ with Tc = 51 K indicates that the basic transport processes in this material are fundamentally different from both the lesser-doped cuprates and model metallic compounds. The overdoped sample has sharp ARPES peaks at the Fermi energy throughout the Brillouin zone even in the normal state, unlike the lesser-doped compounds. In particular, the spectra near the (π, 0) point show the presence of a sharp peak well above Tc. The ARPES line shapes, and thus the self-energy, at a given energy are almost independent of k. Further, the quasiparticle scattering rate at the Fermi energy seems to be closely tied to direct resistivity measurements. This leads us to the conclusion that overdoped Bi 2 Sr 2 CaCu 2 O 8+δ is best described as a quasiparticle liquid. However, the energy dependence of the scattering rates is quite similar to that found in the lesser-doped compounds and quite different from that seen in a typical metal.

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

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