I. Kiselev

Employment of Electric Potential to Build a Gas-Selective Response of Metal Oxide Gas Sensor Array

This paper presents an approach to design multisensor microarrays for electronic nose instruments employing a metal oxide thin film whose spatial gas-sensitive properties are locally differentiated by electric potential. The measurement of local potential as a sensor signal over the metal oxide film serves to build a gas discriminating pattern which can be selectively identified by pattern recognition techniques. The gas sensitivity studies have revealed a higher gas response on the film areas, which are activated by positive potential, than that at earth potential. The gas-recognition capability of potential patterns is, at least, comparable with the one of conductance patterns recorded at the same microarrays differentiated by nonhomogeneous spatial heating. The experimental data are discussed in terms of oxide surface charging by adspecies coming through the ambient air. The suggested approach could be considered for designing reproducible multisensor systems on a single-chip platform.

A white-light interferometer as a gauge to measure the thickness of thin film: a practical extension of the phase method and correlogram summation

In this paper we discuss the method to evaluate the thickness of transparent films employing nonlinear phase spectrum of white-light interferogram. To be practically reliable in the 60–1000 nm range of film thicknesses, the method considers a simple compensation of the disturbing spectrum nonlinearities induced by outer inputs (optical instrumentation, substrate) that allows one to deal with fringe data distortion. The method has been validated by fundamental theory considerations and tested on several types of the films under different interferometer objective magnifications. The application of high objective magnifications has been found to require an additional correction. We also introduce rather simple method of spatial wave packet fitting which has demonstrated the promising statistics in primary tests to gauge the film thickness with advanced lateral resolution.

The Gas‐Sensing Characteristics Of Percolating 2‐D SnO2 Nanowire Mats As A Platform For Electronic Nose Devices

We describe gas‐sensing characteristics of percolating SnO2 nanowire (NW) mats employed in Electronic nose (E‐nose) instrument. The current strategy is based on combining bottom‐up technology of NWs growth and top‐down fabrication of multisensor microarray according to KAMINA (KArlsruhe Micro NAse) E‐nose architecture. Such issues of the NW‐based multisensor systems are discussed as gas‐sensing stability, gas sensitivity and gas classification using Linear Discriminant Analysis (LDA) pattern recognition technique.

Noise robustness of interferometric surface topography evaluation methods. Correlogram correlation

In this paper we promote a method for the evaluation of a surface topography which we call the correlogram correlation method. Employing a theoretical analysis as well as numerical simulations the method is proven to be the most accurate among available evaluation algorithms in the common case of uncorrelated noise. Examples illustrate the superiority of the correlogram correlation method over the common envelope and phase methods.

Close‐To‐Practice Assessment Of Meat Freshness With Metal Oxide Sensor Microarray Electronic Nose

In this report we estimate the ability of KAMINA e‐nose, based on a metal oxide sensor (MOS) microarray and Linear Discriminant Analysis (LDA) pattern recognition, to evaluate meat freshness. The received results show that, 1) one or two exposures of standard meat samples to the e‐nose are enough for the instrument to recognize the fresh meat prepared by the same supplier with 100% probability; 2) the meat samples of two kinds, stored at 4° C and 25° C, are mutually recognized at early stages of decay with the help of the LDA model built independently under the e‐nose training to each kind of meat; 3) the 3–4 training cycles of exposure to meat from different suppliers are necessary for the e‐nose to build a reliable LDA model accounting for the supplier factor. This study approves that the MOS e‐nose is ready to be currently utilised in food industry for evaluation of product freshness. The e‐nose performance is characterized by low training cost, a confident recognition power of various product decay co...

The characterization of nanostructured copper-doped tin oxide films for gas sensor microarrays

In this paper, we describe thin nanostructured SnO/sub 2/:Cu films fabricated by RF magnetron sputtering and characterized with a variety of surface analytical techniques. The average grain size of the 200-1000 nm thick films with columnar structure was determined to be 20-100 nm. A slight oxygen deficiency was observed at the film surface by XPS with [O]/[Sn] ratio equal to about 1.6. Based on these films, gas sensor microarrays of the KAMINA (Karlsruhe Micronose) type were built up. The gas sensitivity of the film sensor segments operated at 350/spl deg/C was found suitable to respond to tested reducing gases (ammonia, acetone, alcohols) with response times of less than 10 sec.

On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute of Technology (Germany). We consider the instability of e-nose operation at different time scales ranging from minutes to many years. To test the e-nose we employ open-air and headspace sampling of analyte odors. The multivariate recognition algorithm to process the multisensor array signals is based on the linear discriminant analysis method. Accounting for the received results, we argue that the stability of device operation is mostly affected by accidental changes in the ambient air composition. To overcome instabilities, we introduce the add-training procedure which is found to successfully manage both the temporal changes of ambient and the drift of multisensor array properties, even long-term. The method can be easily implemented in practical applications of e-noses and improve prospects for device marketing.

Could We Apply a NeuroProcessor For Analyzing a Gas Response Of Multisensor Arrays

We describe an effort of implementation of hardware neuroprocessor to carry out pattern recognition of signals generated by a multisensor microarray of Electronic Nose type. The multisensor microarray is designed with the SnO2 thin film segmented by co‐planar electrodes according to KAMINA (KArlsruhe Micro NAse) E‐nose architecture. The response of this microarray to reducing gases mixtured with a synthetic air is processed by principal component analysis technique realized in PC (Matlab software) and the neural microprocessor NeuroMatrix NM6403. It is shown that the neuroprocessor is able to successfully carry out a gas‐recognition algorithm at a real‐time scale.

Electrical characterisation of a low-density layer of SnO₂-nanowires deposited on a set of parallel Pt electrodes

Low-density layers of SnO 2 nanowires were produced using the vapor solid method and dry-pressed onto surface-oxidized Si-substrates equipped with a set of 39 parallel Pt-electrodes. Current-Voltage (I-V) characteristics of the segments between the electrodes were measured in ambient air at a substrate temperature of 300°C. Statistical analysis of the 38 I-V characteristics allows drawing conclusions, that only Schottky contacts between large nanowires and electrodes are significant for conductometry, and that they have very similar barrier characteristics. The statistical approach and its advantages are demonstrated. The clarity obtained concerning the roles of different resistivity mechanisms involved enables predictions of the nanowire net device behavior in applications, which is demonstrated on an instance of long-term stability examination of gas sensor arrays.