Sean J. Hart

Refractive-index-driven separation of colloidal polymer particles using optical chromatography

Separation of equivalently sized polystyrene, n=1.59, poly(methylmethacrylate), n=1.49, and silica, n=1.43, beads has been accomplished using optical chromatography. The optical separations were performed using a glass flowcell that permits the optical trapping laser to be lightly focused into the fluid pathway against the fluid flow. Separation occurs due to the balance of fluid and optical forces; particles come to rest when the force due to the fluid flow equals the radiation pressure force. The ability to optically separate particles based upon their refractive index opens avenues for the characterization of colloidal samples based upon chemical characteristics, in addition to size.

On-line sample pre-concentration in microfluidic devices: a review.

On-line sample preconcentration is an essential tool in the development of microfluidic-based separation platforms. In order to become more competitive with traditional separation techniques, the community must continue to develop newer and more novel methods to improve detection limits, remove unwanted sample matrix components that disrupt separation performance, and enrich/purify analytes for other chip-based actions. Our goal in this review is to familiarize the reader with many of the options available for on-chip concentration enhancement with a focus on those manuscripts that, in our assessment, best describe the fundamental principles that govern those enhancements. Sections discussing both electrophoretic and nonelectrophoretic modes of preconcentration are included with a focus on device design and mechanisms of preconcentration. This review is not meant to be a comprehensive collection of every available example, but our hope is that by learning how on-line sample concentration techniques are being applied today, the reader will be inspired to apply these techniques to further enhance their own programs.

A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection

We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem.

Multi-criteria fire detection systems using a probabilistic neural network

Abstract The Navy program, Damage Control Automation for Reduced Manning (DC-ARM), is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and, more importantly, increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria-based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to nuisance alarm sources. A multi-signature early warning fire detection system is being developed to provide reliable warning of actual fire conditions in less time with fewer nuisance alarms than can be achieved with commercially available smoke detection systems. In this study, a large database consisting of the responses of 20 sensors to several different types of fires and nuisance sources was generated and analyzed using a variety of multivariate methods. Three data matrices were developed at discrete times corresponding to the different alarm levels of a conventional photoelectric smoke detector. The alarm times represent 0.82%, 1.63% and 11% obscurations per meter. The datasets were organized into three classes representing the sensor responses for baseline (nonfire), fires and nuisance sources. A robust data analysis strategy for use with a sensor array consisting of four to five sensors for early fire detection and nuisance source rejection was developed using a probabilistic neural network (PNN) that was developed at the Naval Research Laboratory for chemical sensor arrays. The analysis algorithms described in this paper evaluate discrete samples and develop classification models that examine individual chemical signatures at discrete points.

Light emitting diode excitation emission matrix fluorescence spectroscopy

An excitation emission matrix (EEM) fluorescence instrument has been developed using a linear array of light emitting diodes (LED). The wavelengths covered extend from the upper UV through the visible spectrum: 370-640 nm. Using an LED array to excite fluorescence emission at multiple excitation wavelengths is a low-cost alternative to an expensive high power lamp and imaging spectrograph. The LED-EEM system is a departure from other EEM spectroscopy systems in that LEDs often have broad excitation ranges which may overlap with neighboring channels. The LED array can be considered a hybrid between a spectroscopic and sensor system, as the broad LED excitation range produces a partially selective optical measurement. The instrument has been tested and characterized using fluorescent dyes: limits of detection (LOD) for 9,10-bis(phenylethynyl)-anthracene and rhodamine B were in the mid parts-per-trillion range; detection limits for the other compounds were in the low parts-per-billion range (< 5 ppb). The LED-EEMs were analyzed using parallel factor analysis (PARAFAC), which allowed the mathematical resolution of the individual contributions of the mono- and dianion fluorescein tautomers a priori. Correct identification and quantitation of six fluorescent dyes in two to six component mixtures (concentrations between 12.5 and 500 ppb) has been achieved with root mean squared errors of prediction (RMSEP) of less than 4.0 ppb for all components.

Early warning fire detection system using a Probabilistic Neural Network

The Navy program, Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and,more importantly increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed, such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires,and reduced susceptibility to nuisance alarm sources. A multi-criteria early warning fire detection system, has been developed to provide reliable warning of actual fire conditions, in less time, with fewer nuisance alarms,than can be achieved with commercially available smoke detection systems. In this study a four-sensor array and a Probabilistic Neural Network have been used to produce an early warning fire detection system. A prototype early warning fire detector was built and tested in a shipboard environment. The current alarm algorithm resulted in better overall performance than the commercial smoke detectors, by providing both improved nuisance source immunity with generally equivalent or faster response times.

Identification and classification of bcl genes and proteins of Bacillus cereus group organisms and their application in Bacillus anthracis detection and fingerprinting.

ABSTRACT The Bacillus cereus group includes three closely related species, B. anthracis, B. cereus, and B. thuringiensis, which form a highly homogeneous subdivision of the genus Bacillus. One of these species, B. anthracis, has been identified as one of the most probable bacterial biowarfare agents. Here, we evaluate the sequence and length polymorphisms of the Bacillus collagen-like protein bcl genes as a basis for B. anthracis detection and fingerprinting. Five genes, designated bclA to bclE, are present in B. anthracis strains. Examination of bclABCDE sequences identified polymorphisms in bclB alleles of the B. cereus group organisms. These sequence polymorphisms allowed specific detection of B. anthracis strains by PCR using both genomic DNA and purified Bacillus spores in reactions. By exploiting the length variation of the bcl alleles it was demonstrated that the combined bclABCDE PCR products generate markedly different fingerprints for the B. anthracis Ames and Sterne strains. Moreover, we predict that bclABCDE length polymorphism creates unique signatures for B. anthracis strains, which facilitates identification of strains with specificity and confidence. Thus, we present a new diagnostic concept for B. anthracis detection and fingerprinting, which can be used alone or in combination with previously established typing platforms.

Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys

The outputs from a physics-based modeler of magnetometry data have been successfully used with a probabilistic neural network (PNN) to discriminate unexploded ordnance (UXO) from ordnance-related scrap. Cross-validation predictions were performed on three data sets to determine which modeler parameters were most valuable for UXO classification. The best performing parameter combination consisted of the modeler outputs depth, size, and inclination. The cross-validation results also indicated that good prediction performance could be expected. Model outputs from one location at a site were used to train a PNN model, which could correctly discriminate UXO from scrap at a different location of the same site. In addition, data from one site, the former Buckley Field, Arapahoe County, CO, was used to predict targets detected at an entirely different training range. The Badlands Bombing Range, Bulls Eye 2 (BBR 2), Cuny Table, SD. Through careful selection of the probability threshold cutoff, the UXO detection rate obtained was 95% with a false alarm rate of only 37%. The ability to distinguish individual UXO types has been demonstrated with correct classifications between 71% and 95%.

Enhanced optical chromatography in a PDMS microfluidic system

The purely refractive index driven separation of uniformly sized polystyrene, n = 1.59 and poly(methylmethacrylate), n = 1.49 in an optical chromatography system has been enhanced through the incorporation of a custom poly(dimethysiloxane) (PDMS) microfluidic system. A customized channel geometry was used to create separate regions with different linear flow velocities tailored to the specific application. These separate flow regions were then used to expose the entities in the separation to different linear flow velocities thus enhancing their separation relative to the same separation in a constant velocity flow environment. A microbiological sample containing spores of the biological warfare agent, Bacillus anthracis, and a common environmental interferent, mulberry pollen, was investigated to test the use of tailored velocity regions. These very different samples were analyzed simultaneously only through the use of tailored velocity regions.

Toward label-free optical fractionation of blood--optical force measurements of blood cells.

There is a compelling need to develop systems capable of processing blood and other particle streams for detection of pathogens that are sensitive, selective, automated, and cost/size effective. Our research seeks to develop laser-based separations that do not rely on prior knowledge, antibodies, or fluorescent molecules for pathogen detection. Rather, we aim to harness inherent differences in optical pressure, which arise from variations in particle size, shape, refractive index, or morphology, as a means of separating and characterizing particles. Our method for measuring optical pressure involves focusing a laser into a fluid flowing opposite to the direction of laser propagation. As microscopic particles in the flow path encounter the beam, they are trapped axially along the beam and are pushed upstream from the laser focal point to rest at a point where the optical and fluid forces on the particle balance. On the basis of the flow rate at which this balance occurs, the optical pressure felt by the particle can be calculated. As a first step in the development of a label-free device for processing blood, a system has been developed to measure optical pressure differences between the components of human blood, including erythrocytes, monocytes, granulocytes, and lymphocytes. Force differentials have been measured between various components, indicating the potential for laser-based separation of blood components based upon differences in optical pressure. Potential future applications include the early detection of blood-borne pathogens for the prevention of sepsis and other diseases as well as the detection of biological threat agents.