Characterization of tin dioxide film for chemical vapors sensor

Recently, oxide semiconductor material used as transducer has been the central topic of many studies for gas sensor. In this paper we investigated the characteristic of a thick film of tin dioxide (SnO₂) film for chemical vapor sensor. It has been prepared by screen-printing technology and deposited on alumina substrate provided with two gold electrodes. The morphology, the molecular composition and the electrical properties of this material have been characterized respectively by Atomic Force Spectroscopy (AFM), Fourier Transformed Infrared Spectroscopy (FTIR) and Impedance Spectroscopy (IS). The electrical properties showed a resistive behaviour of this material less than 300 °C which is the operating temperature of the sensor. The developed sensor can identify the nature of the detected gas, oxidizing or reducing.     

Improved probabilistic neural network algorithm for chemical sensor array pattern recognition

An improved probabilistic neural network (IPNN) algorithm for use in chemical sensor array pattern recognition applications is described. The IPNN is based on a modified probabilistic neural network (PNN) with three innovations designed to reduce the computational and memory requirements, to speed training, and to decrease the false alarm rate. The utility of this new approach is illustrated with the use of four data sets extracted from simulated and laboratory-collected surface acoustic wave sensor array data. A competitive learning strategy, based on a learning vector quantization neural network, is shown to reduce the storage and computation requirements. The IPNN hidden layer requires only a fraction of the storage space of a conventional PNN. A simple distance-based calculation is reported to approximate the optimal kernel width of a PNN. This calculation is found to decrease the training time and requires no user input. A general procedure for selecting the optimal rejection threshold for a PNN-based algorithm using Monte Carlo simulations is also demonstrated. This outlier rejection strategy is implemented for an IPNN classifier and found to reject ambiguous patterns, thereby decreasing the potential for false alarms.     

Detection of noncharged organic substances by new measurement method using a lipid membrane sensor

This paper reports a new measurement method to detect ppb levels of noncharged organic substances using lipid/polymer membrane sensors. Noncharged organic substances have large influences on the adsorption of positively-charged lipids to negatively-charged membranes. Organic solvents (trichloroethylene) and endocrine disrupting chemicals (di-2-ethylhexylphthalate) were detected by utilizing the sensor output, which is the change of membrane potential caused by interactions among the lipid membrane, noncharged organic substances, and lipids in solution. This new potentiometric method has a possibility of detection of a trace amount of noncharged toxic substances.     

Evanescent fiber-optic chemical sensor for monitoring volatile organic compounds in water

The transport of trichloroethylene, 1,1,1-trichloroethane, and toluene in aqueous solutions through a polydimethylsiloxane film was modeled using a Fickian diffusion model to fit data obtained from an evanescent fiber-optic chemical sensor (EFOCS). The resultant diffusion coefficients for these analytes were respectively 3 × 10(-)(7), 5 × 10(-)(7), and 1 × 10(-)(7) cm(2)/s. Inclusion of an interfacial conductance term, defined as the ratio of the mass transport coefficient across the polymer surface and the analyte diffusion coefficient in the polymer, was required to accurately model the data. It was determined that the interfacial conductance terms were generally of the same order of magnitude for the analytes examined, suggesting a constant transport mechanism for the analytes. Linear chemometric algorithms were used to model the EFOCS response to aqueous mixtures of the three analytes with individual analyte concentrations between 20 and 300 ppm. Both partial least-squares and principal component regression algorithms performed comparably on the calibration sets, with cross-validated root-mean-squared errors of prediction for trichloroethylene, 1,1,1-trichloroethane, and toluene of approximately 26, 29, and 22 ppm, respectively. The resultant prediction model was then used to determine analyte concentrations in an independent data set with comparable precision.     

Adaptive K-NN for the detection of air pollutants with a sensor array

The field of air-quality monitoring is gaining increasing interest, with regard to both indoor environment and air-pollution control in open space. This work introduces a pattern recognition technique based on adaptive K-nn applied to a multisensor system, optimized for the recognition of some relevant tracers for air pollution in outdoor environment, namely benzene, toluene, and xylene (BTX), NO/sub 2/, and CO. The pattern-recognition technique employed aims at recognizing the target gases within an air sample of unknown composition and at estimating their concentrations. It is based on PCA and K-nn classification with an adaptive vote technique based on the gas concentrations of the training samples associated to the K-neighbors. The system is tested in a controlled environment composed of synthetic air with a fixed humidity rate (30%) at concentrations in the ppm range for BTX and NO/sub 2/, in the range of 10 ppm for CO. The pattern recognition technique is experimented on a knowledge base composed of a limited number of samples (130), with the adoption of a leave-one-out procedure in order to estimate the classification probability. In these conditions, the system demonstrates the capability to recognize the presence of the target gases in controlled conditions with a high hit-rate. Moreover, the concentrations of the individual components of the test samples are successfully estimated for BTX and NO/sub 2/ in more than 80% of the considered cases, while a lower hit-rate (69%) is reached for CO.     

Detection of phase singularities with a Shack-Hartmann wavefront sensor

While adaptive optical systems are able to remove moderate wavefront distortions in scintillated optical beams, phase singularities that appear in strongly scintillated beams can severely degrade the performance of such an adaptive optical system. Therefore the detection of these phase singularities is an important aspect of strong-scintillation adaptive optics. We investigate the detection of phase singularities with the aid of a Shack-Hartmann wavefront sensor and show that, in spite of some systematic deficiencies inherent to the Shack-Hartmann wavefront sensor, it can be used for the reliable detection of phase singularities, irrespective of their morphologies. We provide full analytical results, together with numerical simulations of the detection process.     

Fabrication and characterization of ZnO nanoneedle array using metal organic chemical vapor deposition

High density and vertically well-aligned ZnO nanoneedle arrays were fabricated on the ZnO thin film deposited on silicon substrates. The ZnO buffer layer and nanoneedles were synthesized by metal organic chemical vapor deposition using diethylzinc and oxygen gas. The ZnO buffer film was grown at 250 degrees C and the growth temperature of nanoneedles was in the range of 480-500 degrees C. As-grown ZnO nanoneedles showed single crystalline structure of ZnO (002). The crystalline properties of three samples (A: as-deposited ZnO buffer layer, B: annealed buffer film, C: ZnO nanoneedles) were compared using XRD and Raman spectroscopy. The synthesized ZnO nanoneedles (sample C) showed highest crystalline quality among three samples. The field emission properties of ZnO nanoneedles were investigated, which showed low turn on field of 4.8 Vmicrom(-1) and high field enhancement factor of 3.2 x 103.     

Monolithic miniaturized quartz microbalance array and its application to chemical sensor systems for liquids

We report on the design, fabrication, and application of novel monolithic miniaturized quartz microbalance (QMB) arrays. Up until now, almost all reported resonator arrays (often designated as "electronic noses" or "electronic tongues", respectively, dependent on their application) are assembled from single QMBs. We fabricate arrays with up to 36 QMBs on a single AT-cut quartz blank. Mass sensitive devices based on AT-cut quartz resonators are suitable as (bio)chemical sensors. A frequency shift caused by mass accumulation on the sensor surface increases theoretically with f/sup 2/, hence the detection limits for the application as chemical sensors should be decreased with increasing frequency. Since the quality factor Q of a quartz crystal decreases with f, the frequency stability is reduced, thus limiting mass sensitivity. The mass sensitivity of resonators with different resonant frequencies was examined by means of electrochemical copper deposition on their surface. Subsequently, the manufactured resonators were coated with different layers (polystyrene, amyl-calix[8]arene, /spl beta/-cyclodextrine). In order to examine the applicability of such coatings as sensitive layers, their sensitivities to toluene in water were investigated. Moreover, arrays with up to four different resonant frequencies on one chip were fabricated for comparing the resonator behavior of the same coating at different frequencies. In another test setup, different layers were sprayed onto an array of microbalances having all the same resonant frequency. This allowed for comparing the different coating behavior under equivalent test conditions. Arrays were tested for viscosity measurement to find an optimum resonant frequency.     

Remarks on the use of multilayer perceptrons for the analysis of chemical sensor array data

Multilayer perceptrons (MLPs) are a standard tool for establishing relationships between data in many real world problems, in the absence of a parametric model. In the last decade, they have often been used for analyzing data produced by arrays of chemical sensors [electronic noses (e-noses)]. Still, the central issue of controlling the complexity of an MLP for optimal generalization is frequently overlooked by chemical sensors practitioners causing incorrect or suboptimal results (over or underfitting). In this paper, we will: 1) present different ways of controlling the complexity of an MLP (model order selection, early stopping, and regularization); 2) shortly review the literature on complexity control, inside and outside the e-nose community; and 3) give examples of effective complexity control for two e-noses datasets of different size and learning difficulty. It will be shown that, if early stopping or regularization are adopted, overfitting is avoided whatever the number of hidden units (and, hence, network weights). Another issue tackled in this paper is the influence on the generalization error of the number of principal components over which data are projected (before being fed into the MLP). Simulations show that (test set) performance depends strongly on the number of principal components and that even components with less than 1% of the global variance enhance classification.     

Digital signal processing for biaxial position measurement with a pyroelectric sensor array

This paper proposes a new full digital approach to estimate biaxial position with a pyroelectric sensor array. The previously developed analog interface shows its limits in the calibration procedure, requiring several trimming adjustments. A DSP-based hardware has been developed to experimentally evaluate three digital methods: radial basis function (RBF) neural network, best fitted plane (BFP), and look-up table (LUT) in the least mean square (LMS) error sense. Experimental results show that no dramatic improvements are obtained by the RBF, despite the long training required and the external PC support for weight calculation. The BFP reaches performances comparable to the analog processing system using only nine calibration points, but the best tradeoff has been found with the LUT technique. Actually, with a 64-point calibration set, LUT gives a root mean square error (RMSE) of 0.5% with respect to full scale (FS), offering a valid in-circuit compensation of array structural defects.     

Discrimination of methanol and ethanol vapors by the use of a single optical sensor with a microporous sensitive layer

The sorption of methanol and ethanol vapors by a microporous glassy polycarbonate is studied. The increase of the refractive index of the polymer during analyte sorption is measured by surface plasmon resonance. Both analytes are sorbed into the micropores of the polymer showing different diffusion kinetics. The sensor response during analyte exposure is subdivided into different time channels. By evaluating this additional data dimension by neural networks, a simultaneous multicomponent analysis of binary mixtures of ethanol and methanol vapors is possible using the sensor response of only one single sensor. A feature extraction results in an interpretable model and an improved prediction with errors of 2.0% for methanol and 2.4% for ethanol.     

Fiber-optic sensor system for rapid positioning of a microelectrode array

A fiber-optic intensity sensor has been developed for vertically positioning microelectrode arrays above the retina of a live frog. Closely spaced fibers illuminate and collect reflections from the retinal surface, and the output is electronically processed to drive an automated positioning circuit. Experimental and theoretical evaluations of fiber types and separation for both specular and diffuse reflectors, in vitro and in vivo, are presented, and multimode fibers on 125-mum centers are chosen for retinal experimentation. The sensor has applications in assessing spatial selectivity of stimulation of a multielectrode array and may be adaptable for lateral positioning.     

Optical sensor interrogation with a blazed fiber Bragg grating and a charge-coupled device linear array

We present what is to our knowledge the first comprehensive investigation of the use of blazed fiber Bragg gratings (BFBGs) to interrogate wavelength division multiplexed (WDM) in-fiber optical sensor arrays. We show that the light outcoupled from the core of these BFBGs is radiated with sufficient optical power that it may be detected with a low-cost charge-coupled device (CCD) array. We present thorough system performance analysis that shows sufficient spectral-spatial resolution to decode sensors with a WDM separation of 75 rhom, signal-to-noise ratio greater than 45-dB bandwidth of 70 nm, and drift of only 0.1 rhom. We show the system to be polarization-state insensitive, making the BFBG-CCD spectral analysis technique a practical, extremely low-cost, alternative to traditional tunable filter approaches.     

Parameter estimation using a sensor array in a Ricean fading channel

The estimation of the Direction-Of-Arrival (DOA) and the variance of the angular spread, using an array of sensors in the case of a Ricean channel is considered, using the Maximum-Likelihood, Least-Squares and Weighted Least Squares criteria. The Cramér-Rao bound is also obtained for the problem of interest. Simplification of the cost functions to reduce the dimension of the problem has been carried out and the performance of the methods has been studied based on numerical experiments. A major part of the work was carried out when K V S Hari was visiting the Department of Signals, Sensors and Systems during Jul–Sep 1995, on leave from the Indian Institute of Science     

Detection of organomercurials with sensor bacteria.

Mercury and its organic compounds, especially methylmercury, are hazardous compounds that concentrate in biota via biomagnification and cause severe neurological disorders in animals. In this paper, a recombinant whole-cell bacterial sensor for the detection of the organic compounds of mercury was constructed. The sensor carries firefly luciferase gene as a reporter under the control of the mercury-inducible regulatory part of broad spectrum mer operon from pDU1358. In addition, a gene-encoding organomercurial lyase (an enzyme necessary for cleavage of the mercury-carbon bond) was coexpressed in the sensor strain. The sensitivity of the sensor was evaluated on some environmentally important organomercurial compounds. The lowest detectable concentrations were 0.2 nM (50 ng/L), 1 nM (0.34 microg/L), and 10 microM (2.3 mg/L) for methylmercury chloride, phenylmercury acetate, and dimethylmercury, respectively. The sensor responded also to inorganic mercury and, therefore, using the sensor described here together with sensor bacteria responding only to inorganic mercury, it should be possible to characterize the mercury contamination, for example, in environmental samples.     

Detection and classification characteristics of arrays of carbon black/organic polymer composite chemiresistive vapor detectors for the nerve agent simulants dimethylmethylphosphonate and diisopropylmethylphosponate

Arrays of conducting polymer composite vapor detectors have been evaluated for performance in the presence of the nerve agent simulants dimethylmethylphosphonate (DMMP) and diisopropylmethylphosponate (DIMP). Limits of detection for DMMP on unoptimized carbon black/ organic polymer composite vapor detectors in laboratory air were estimated to be 0.047-0.24 mg m(-3). These values are lower than the EC50 value (where EC50 is the airborne concentration sufficient to induce severe effects in 50% of those exposed for 30 min) for the nerve agents sarin (methylphosphonofluoridic acid, 1-methylethyl ester) and soman (methylphosphonofluoridic acid, 1,2,2-trimethylpropyl ester), which has been established as approximately 0.8 mg m(-3). Arrays of these vapor detectors were easily able to resolve signatures due to exposures to DMMP from those due to DIMP or due to a variety of other test analytes (including water, methanol, benzene, toluene, diesel fuel, lighter fluid, vinegar, and tetrahydrofuran) in a laboratory air background. In addition, DMMP at 27 mg m(-3) could be detected and differentiated from the signatures of the other test analytes in the presence of backgrounds of potential interferences, including water, methanol, benzene, toluene, diesel fuel, lighter fluid, vinegar, and tetrahydrofuran, even when these interferents were present in much higher concentrations than that of the DMMP or DIMP being detected.     

Detection performance of a diffusive wave phased array

Diffusive wave phased arrays have been demonstrated to be a sensitive method of detecting inhomogeneities embedded in heavily scattering media. However, the increase in sensitivity is coupled with an increase in noise, so that the optimum performance may not be obtained when the sources are modulated in antiphase. The performance of a range of configurations in the presence of Gaussian noise is investigated by using probabilistic detection theory. A model of diffusive wave propagation through scattering media is used to demonstrate that the phase performance can be improved by controlling the relative phase difference between the two sources. However, the best performance is obtained by using the amplitude response of a single source system. The major benefit of a phased array system is therefore the rejection of common systematic noise.