The detection of mixtures of NO{sub x}`s with hydrogen using catalytic metal films on the Sandia Robust Sensor with pattern recognition

Microsensors often do not have the selectivity to chemical species available in large laboratory instruments. A new type of pattern recognition algorithm is used to classify mixtures of H{sub 2} with NO{sub 2} and O{sub 2}. The microsensors used are thin film catalytic metal field effect transistors and chemiresistors on the Sandia Robust Sensor platform. For this study pure Pd thin films and Pd/Ni alloys are shown to provide good classification of mixtures containing NO{sub 2} from those containing O{sub 2} or no oxidant

Optimizing Chemical Sensor Array Sizes

Optimal selection of array sensors for a chemical sensing application is a nontrivial task. It is commonly believed that "more is better" when choosing the number of sensors required to achieve good chemical selectivity. However, cost and system complexity issues point towards the choice of small arrays. A quantitative array optimization is carried out to explore the selectivity of arrays of partially-selective chemical sensors as a function of array size. It is shown that modest numbers (dozens) of target analytes are completely distinguished with a range of arrays sizes. However, the array selectivity and the robustness against sensor sensitivity variability are significantly degraded if the array size is increased above a certain number of sensors, so that relatively small arrays provide the best performance. The results also suggest that data analyses for very large arrays of partially-selective sensors will be optimized by separately anal yzing small sensor subsets

Recognizing Atoms in Atomically Engineered Nanostructures: An Interdisciplinary Approach

This report describes the results of a Sandia Laboratov Directed Research & Development project to develop a technique that can identifi atoms in atomically engineered nanostructures. The report provides a detailed description of the experimental measurement techniques and subsequent image analysis procedures used in the identification process, followed by examples of the technique's successful application to several atomic surface features. Use of this technique requires the experimental measurement of both constant-current topographic and multi-bias conductance data from an atomic surface with the scanning tunneling microscope. These measurements are rendered as a collection of topographic and single-bias conductance images of the surface. Image pixels are then grouped into classes by a computed grouping algorithm, according to the shared conductance characteristics exhibited at each pixel. The image pixels are then color-coded by class to produce a false-color image of the scanned surface that chemically distinguishes surface electronic features over the entire area of the measured atomic surface

Sensor-fusion-based biometric identity verification

Future generation automated human biometric identification and verification will require multiple features/sensors together with internal and external information sources to achieve high performance, accuracy, and reliability in uncontrolled environments. The primary objective of the proposed research is to develop a theoretical and practical basis for identifying and verifying people using standoff biometric features that can be obtained with minimal inconvenience during the verification process. The basic problem involves selecting sensors and discovering features that provide sufficient information to reliably verify a person`s identity under the uncertainties caused by measurement errors and tactics of uncooperative subjects. A system was developed for discovering hand, face, ear, and voice features and fusing them to verify the identity of people. The system obtains its robustness and reliability by fusing many coarse and easily measured features into a near minimal probability of error decision algorithm

SAW arrays using dendrimers and pattern recognition to detect volatile organics

chemical sensor arrays eliminate the need to develop a high-selectivity material for every analyte. The application of pattern recognition to the simultaneous responses of different microsensors enables the identification and quantification of multiple analytes with a small array. Maximum materials diversity is the surest means to create an effective array for many analytes, but using a single material family simplifies coating development. Here the authors report the successful combination of an array of six dendrimer films with mass-sensitive SAW (surface acoustic wave) sensors to correctly identify 18 organic analytes over wide concentration ranges, with 99.5% accuracy. The set of materials for the array is selected and the results evaluated using Sandia`s Visual-Empirical Region of Influence (VERI) pattern recognition (PR) technique. The authors evaluated eight dendrimer films and one self-assembled monolayer (SAM) as potential SAW array coatings. The 18 organic analytes they examined were: cyclohexane, n-hexane, i-octane, kerosene, benzene, toluene, chlorobenzene, carbon tetrachloride, trichloroethylene, methanol, n-propanol, pinacolyl alcohol, acetone, methyl isobutyl ketone, dimethylmethylphosphate, diisopropylmethylphosphonate, tributylphosphate, and water