Hexanitrostilbene (HNS) is a secondary explosive widely used in a variety of commercial and military applications, due
in part to its high heat resistivity. Degradation of HNS is known to occur through exposure to a variety of sources
including heat, UV radiation, and certain chemical compounds, all of which may lead to reduced performance.
Detecting the degradation of HNS within a device, however, has required destructive analyses of the entire device while
probing the HNS in only an indirect fashion. Specifically, the common methods of investigating this degradation include
wet chemical, surface area and performance testing of the devices incorporating HNS rather than a direct interrogation of
the material itself. For example, chemical tests frequently utilized, such as volatility, conductivity, and contaminant
trapping, provide information on contaminants present in the system rather than the chemical stability of the HNS. To
instead probe the material directly, we have pursued the use of optical methods, in particular infrared (IR) spectroscopy,
in order to assess changes within the HNS itself. In addition, by successfully implementing miniature silicon (Si)
waveguides fabricated at Sandia National Laboratories to facilitate this spectroscopic approach, we have demonstrated
that HNS degradation monitoring may take place in a non-destructive, in-situ fashion. Furthermore, as these waveguides
may be manufactured in a variety of configurations, this direct, non-destructive, approach holds promise for
incorporation into a variety of devices.
Hexanitrostilbene (HNS) is a widely used explosive, due in part to its high thermal stability. Degradation of HNS is
known to occur through UV, chemical exposure, and heat exposure, which can lead to reduced performance of the
material. Common methods of testing for HNS degradation include wet chemical and surface area testing of the material
itself, and performance testing of devices that use HNS. The commonly used chemical tests, such as volatility,
conductivity and contaminant trapping provide information on contaminants rather than the chemical stability of the
HNS itself. Additionally, these tests are destructive in nature. As an alternative to these methods, we have been
exploring the use of vibrational spectroscopy as a means of monitoring HNS degradation non-destructively. In
particular, infrared (IR) spectroscopy lends itself well to non-destructive analysis. Molecular variations in the material
can be identified and compared to pure samples. The utility of IR spectroscopy was evaluated using pressed pellets of
HNS exposed to DETA (diethylaminetriamine). Amines are known to degrade HNS, with the proposed product being a
σ-adduct. We have followed these changes as a function of time using various IR sampling techniques including
photoacoustic and attenuated total reflectance (ATR).
We employ infrared spectroscopy (IR) with attenuated total reflectance (ATR) as a sampling technique to monitor live and dried RAW cells (a murine macrophage cell line) during activation with g-interferon and lipopolysaccharide. By comparing the spectra of activated cells at various time points to the spectra of healthy control cells, we identify spectral bands associated with nucleic acids that are markers for the cell activation process. These spectral changes are slight and can be complicated with the normal metabolic changes that occur within cells. We will discuss the use of data pretreatment strategies to accurately correct for the contribution of the buffer to the live cell spectra. We find the standard background correction method inadequate for concentrated solutions of cells. Data presented shows the severe effect incorrect background subtraction has on the cell spectra. We report a more accurate correction for phosphate buffer spectral contribution using an interactive subtraction of the buffer spectrum. We will show classification of dried control and activated macrophage cell spectra using partial-least squares analysis with multiplicative scatter correction.
Proc. SPIE. 4040, Unattended Ground Sensor Technologies and Applications II
KEYWORDS: Signal to noise ratio, Principal component analysis, Statistical analysis, Sensors, Interference (communication), Signal processing, Acoustics, Signal detection, Unattended ground sensors, Data analysis
This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier-based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statistics of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.
Recent investigations by our group have demonstrated that near-infrared spectra collected from lysed blood solutions can be used to create clinically useful partial least squares (PLS) models for pH with standard errors of prediction below 0.05 pH units for a pH range of 1 (6.8 to 7.8). Further work was performed in order to discern the primary source of pH information in the spectra. Results from these experiments are presented using spectral data acquired over the spectral range of 1300 nm to 2500 nm from plasma, lysed blood and amino acids solutions. Data were analyzed by principal component analysis (PCA) and loading vectors were compared. Experiments were designed to eliminate possible correlation between pH and other components in the system in order to ensure variations in the spectral data were due to hydrogen ion changes only. Results indicate that variations in the spectral characteristics of histidine mimic those seen in lysed blood, but not those seen in plasma, suggesting that histidine residues from hemoglobin are providing the necessary variation for pH modeling in the lysed blood solutions.