Publications & Presentations

Progress in Standoff Surface Contaminant Detector Platform

Julia R. Dupuis, Jay Giblin, John Dixon, Joel Hensley, David Mansur, and William J. Marinelli
SPIE Defense and Security
Micro- and Nanotechnology Sensors, Systems, and Applications IX
Anaheim, CA, April 13, 2017

Progress towards the development of a longwave infrared quantum cascade laser (QLC) based standoff surface contaminant detection platform is presented. The detection platform utilizes reflectance spectroscopy with application to optically thick and thin materials including solid and liquid phase chemical warfare agents, toxic industrial chemicals and materials, and explosives.

The platform employs an ensemble of broadband QCLs with a spectrally selective detector to interrogate target surfaces at 10s of m standoff. A version of the Adaptive Cosine Estimator (ACE) featuring class based screening is used for detection and discrimination in high clutter environments. Detection limits approaching 0.1 μg/cm2 are projected through speckle reduction methods enabling detector noise limited performance.

The design, build, and validation of a breadboard version of the QCL-based surface contaminant detector are discussed. Functional test results specific to the QCL illuminator are presented with specific emphasis on speckle reduction.

Progress in Standoff Surface Contaminant Detector Platform

Julia R. Dupuis, Jay Giblin, John Dixon, Joel Hensley, David Mansur, and William J. Marinelli
SPIE Defense and Commercial Sensing,
Anaheim, CA, April 9-13, 2017

Progress towards the development of a longwave infrared quantum cascade laser (QLC) based standoff surface contaminant detection platform is presented. The detection platform utilizes reflectance spectroscopy with application to optically thick and thin materials including solid and liquid phase chemical warfare agents, toxic industrial chemicals and materials, and explosives.

The platform employs an ensemble of broadband QCLs with a spectrally selective detector to interrogate target surfaces at 10s of m standoff. A version of the Adaptive Cosine Estimator (ACE) featuring class based screening is used for detection and discrimination in high clutter environments. Detection limits approaching 0.1 ug/cm2 are projected through speckle reduction methods enabling detector noise limited performance.

The design, build, and validation of a breadboard version of the QCL-based surface contaminant detector are discussed. Functional test results specific to the QCL illuminator are presented with specific emphasis on speckle reduction.

Laser-Based Sensors for Addressing Climate Change

Mickey B. Frish
Conference on Lasers and Electro-Optics (CLEO)
Session AM3B: Greenhouse Gas Sensing
May 15, 2017, San Jose, CA

Identifying, measuring, and reducing mankind’s contribution to climate change is an
urgent international endeavor. This paper describes our work dedicated towards developing and
applying laser sensors to support efforts to reduce greenhouse gas emissions.

Advanced LWIR hyperspectral sensor for on-the-move proximal detection of liquid/solid contaminants on surfaces

Jay P. Giblin, John Dixon, Julia R. Dupuis, Bogdan R. Cosofret, William J. Marinelli
SPIE Defense and Commercial Sensing
Anaheim, CA, 9 - 13 April 2017
(SPIE Paper No. 10183-4)

Sensor technologies capable of detecting low vapor pressure liquid surface contaminants, as well as solids, in a noncontact fashion while on-the-move continues to be an important need for the U.S. Army.

In this paper, we discuss the development of a long-wave infrared (LWIR, 8-10.5 μm) spatial heterodyne spectrometer coupled with an LWIR illuminator and an automated detection algorithm for detection of surface contaminants from a moving vehicle. The system is designed to detect surface contaminants by repetitively collecting LWIR reflectance spectra of the ground. Detection and identification of surface contaminants is based on spectral correlation of the measured LWIR ground reflectance spectra with high fidelity library spectra and the system’s cumulative binary detection response from the sampled ground. We present the concepts of the detection algorithm through a discussion of the system signal model. In addition, we present reflectance spectra of surfaces contaminated with a liquid CWA simulant, triethyl phosphate (TEP), and a solid simulant, acetaminophen acquired while the sensor was stationary and on-the-move. Surfaces included CARC painted steel, asphalt, concrete, and sand. The data collected was analyzed to determine the probability of detecting 800 μm diameter contaminant particles at a 0.5 g/m2 areal density with the SHSCAD traversing a surface

Scanning, standoff TDLAS leak imaging and quantification

Richard T. Wainner, Nicholas F. Aubut, Matthew C. Laderer, Michael B. Frish
SPIE Commercial & Scientific Sensing and Imaging Conference
Next Generation Spectroscopic Technologies X
Anaheim, California
9 - 13 April 2017
SPIE Paper No. 10210-5

This paper reports a novel quantitative gas plume imaging tool, based on active near-infrared Backscatter Tunable Diode Laser Absorption Spectroscopy (b-TDLAS) technology, designed for upstream natural gas leak applications.

The new tool integrates low-cost laser sensors with video cameras to create a highly sensitive gas plume imager that also quantifies emission rate, all in a lightweight handheld ergonomic package. It is intended to serve as a lower-cost, higherperformance, enhanced functionality replacement for traditional passive non-quantitative mid-infrared Optical Gas Imagers (OGI) which are utilized by industry to comply with natural gas infrastructure Leak Detection and Repair (LDAR) requirements. It addresses the need for reliable, robust, low-cost sensors to detect and image methane leaks, and to quantify leak emission rates, focusing on inspections of upstream oil and gas operations, such as well pads, compressors, and gas plants. It provides: 1) Colorized quantified images of path-integrated methane concentration. The images depict methane plumes (otherwise invisible to the eye) actively interrogated by the laser beam overlaid on a visible camera image of the background. The detection sensitivity exceeds passive OGI, thus simplifying the manual task of leak detection and location; and 2) Data and algorithms for using the quantitative information gathered by the active detection technique to deduce plume flux (i.e. methane emission rate). This key capability will enable operators to prioritize leak repairs and thereby minimize the value of lost product, as well as to quantify and minimize greenhouse gas emissions, using a tool that meets EPA LDAR imaging equipment requirements.