Gas Sensor Microarray Research Articles

Localized aerosol-assisted CVD of nanomaterials for the fabrication of monolithic gas sensor microarrays

The self-heating capability of MEMS-based devices is used to grow locally into the sensing active area of monolithic gas sensor microarrays differently-functionalized materials via aerosol-assisted CVD. Results derived from SEM, TEM, XRD and Raman demonstrate the integration of non-functionalized (WO3−x) and functionalized nanostructures with gold (WO3-x/Au) or platinum (WO3-x/Pt) NPs into the array. Tests of these microarrays toward various concentrations of reducing gases show stable and reproducible responses, with the highest responses (R) for WO3−x to carbon monoxide (e.g. R=4.3–80ppm), for WO3−x/Au to ethanol (e.g. R=7–80ppm) and for WO3−x/Pt to hydrogen (e.g. R=3.6–80ppm). Principal component analysis of the sensor response replicates to each gas and concentration suggest that the differences in the sensing properties of each element of the array provide the complementary information to discriminate H2 and EtOH from CO.

Fabrication and characterization of gas sensor micro-arrays

A novel structures of nanomaterials gas sensors array constructed using ZnO, and ZnO doped with Al via sol–gel technique. Two structure arrays are developed; the first one is a double sensor array based on doping with percentages of 1% and 5%. The second is a quadrature sensor array based on several doping ratios concentrations (0%, 1%, 5% and 10%). The morphological structures of prepared ZnO were revealed using scanning electron microscope (SEM). X-ray diffraction (XRD) patterns reveal a highly crystallized wurtzite structure and used for identifying phase structure and chemical state of both ZnO and ZnO doped with Al under different preparation conditions and different doping ratios. Chemical composition of Al-doped ZnO nanopowders was performed using energy dispersive X-ray (EDS) analysis. The electrical characteristics of the sensor are determined by measuring the two terminal sensor’s output resistance for O2, H2 and CO2 gases as a function of temperature.

Impact of sensor packaging on analytical performance and power consumption of metal oxide based gas sensor microarrays

The analytical performance and the power consumption of gas sensor microarrays belong to the most prominent properties governing the applicability. Packaging of sensor systems is often underestimated in its essential influence. Measurements and finite-element model calculations of the heat dissipation and gas fluidics have been performed to optimize the periphery of an SnO2 equipped gradient gas sensor microarray operated at 300 °C.

Multicomponent quantification with a novel method applied to gradient gas sensor microarray signal patterns

Abstract Industrial monitoring can be performed efficiently by an electronic nose (EN) not only in case of direct gas analysis but also if liquids have to be analysed for volatile components. The control of a binary biocide mixture contained in oil–water emulsions is presented as an example for a multicomponent quantification. A carrier gas was bubbled through the lubricant emulsion at elevated temperature to enhance the evaporation of the two biocide components to be determined by a gradient gas sensor microarray. A linear discriminant analysis trained with well-defined model mixtures showed a reasonable multicomponent quantification with a special two-dimensional interpolation.

Synthesis and characterization of nanoscale Al–Si–O gradient membranes

Novel ultrathin gas-permeable Al–Si-oxide membranes have been developed by means of ion induced chemical vapor deposition in order to improve the gas analytical performance of an electronic nose. Dependent on the used precursor tailored Al∕Si concentration ratios and even concentration gradients are attainable. The diversity in chemical composition and thickness across the gas sensor microarray has been proven by the combination of ellipsometry for the freshly prepared membrane and line scans derived from Auger electron spectroscopy and angle resolved x-ray photoelectron spectroscopy, respectively, for the baked membrane.

Nanogranular SnO2 Layers for Gas Sensing Applications by In Situ Deposition of Nanoparticles Produced by the Karlsruhe Microwave Plasma Process

To improve the performance of the central element of the Karlsruhe Micronose—a gas-sensor microarray—the gas sensitive layer is modified toward a highly porous nanogranular layer. To synthesize those layers, the Karlsruhe Microwave Plasma Process which is in its native form a precursor-based process to produce nanoparticles with diameters below 10 nm, was modified for in situ tin-dioxide layer deposition. The produced layers have due to their structure a very large active surface area. The process parameters were optimized to generate thin layers with high surface homogeneity. This was mostly established by significantly reducing the precursor feed and therefore reducing the primary particle size to below 2 nm. The layers were analyzed for their mechanical stability, structural, and chemical properties. It is shown that the precursor residue can be completely removed by applying a default annealing step. The structure of the layers reminds of little clubs starting on top of the substrate growing wider toward the surface. Prototype sensors were fabricated and tested for their gas sensory properties in comparison to a standard gas-sensor microarray with a sputtered tin-dioxide layer. The gas-sensor microarrays with nanogranular layer show an increased signal response of up to one order of magnitude to isopropanol. The time of response is equal in both sensor systems while the time of recovery is nearly doubled for the sensors with nanogranular layer due to increased surface area and gas absorption.

Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies

The selectivity of metal oxide gas sensors can be improved by operating the sensors in a temperature-modulated mode. Although the selection of optimal modulating frequencies deserves accurate attention, this aspect has been generally overlooked. In this paper, a systematic method to determine which are the optimal temperature-modulation frequencies to solve a given gas analysis problem has been introduced, discussed in detail and fully validated for the first time. The optimization method is based on the use of multi-level pseudo-random sequences. These sequences share some properties with white noise and allow for the impulse response of the sensor–gas system to be estimated. Using this strategy, it is shown that the best temperature-modulating frequencies to discriminate and quantify gases using an array of four metal oxide gas sensors are identified. The process is illustrated solving a practical application: the quantitative analysis of acetaldehyde, ethylene, ammonia and their binary mixtures (monitoring climacteric fruit during cold storage). By using a multi-sinusoidal temperature-modulating signal, the frequencies of which are a reduced set of the optimal ones, the gases and gas mixtures were discriminated with a 100% success rate. In gas identification, features from the sensors’ dynamic response extracted via the fast Fourier transform (FFT) were used together with a fuzzy ARTMAP neural network. After the identification process, the concentration of the different species was accurately predicted by PLS-based calibration models. These results compare favorably with the ones obtained when the sensor array was operated in a steady-state mode. The optimization method is shown to be consistent and effective, since the process of determining optimal modulation frequencies and the validation process were conducted using different metal oxide gas sensor micro-arrays (of the same type) and different measurement sets.

Sensitive Impurity Detection and Recognition in Inert Gaseous Media with SnO2 Based Gas Sensor Microarray

ABSTRACTContrary to the conventional use of semi-conductive metal oxides as gas detectors for components in air the analytical performance of segmented SnO2-layers were investigated to detect and discriminate impurities in inert gases such as nitrogen or helium. A broad variety of technical processes require high purity inert gases which have to be kept under permanent surveillance to detect leaks to the atmosphere, a gas release of contaminated fittings or other contaminations. The unique gradient gas sensor microarray of the Karlsruhe micronose (KAMINA) was equipped with segmented layers of either nanoparticle or sputtered SnO2 layers and exposed to a high purity nitrogen flow of 1lit/min into which temporarily model impurities have been admitted with a variety of concentrations. Pulsed exposures were performed to nitrogen containing oxygen (0.2 to 10 vol %), water vapour (5 to 15 % relative humidity) and 2-propanol as well as tetrachloromethane in the ppm range. For all model contaminations a fast and sensitive response has been found. The measured concentration dependence revealed a detection limit of about 0.01 vol% for oxygen and 0.03% of relative humidity for an operating temperature of the KAMINA chip at 325..273 °C. The detection limits for the organic compounds were even found to be in the lower ppb range. The conductivity patterns obtained from the simple segmentation microstructure of the KAMINA SnO2 layers operated with a temperature gradient were evaluated by a Principal Component Analysis (PCA) which proved an appropriate gas discrimination power to distinguish pure nitrogen and the different kinds of contaminations for both types of SnO2 layers. However, the sputtered layer showed better detection limits while the nanoparticle layers offered a better sensitivity, i.e. a higher slope of the concentration dependence.

Optimised temperature modulation of metal oxide micro-hotplate gas sensors through multilevel pseudo random sequences

We show how it is possible to optimise a multi-frequency signal to be used in the modulation of the operating temperature of an integrated gas sensor microarray. In the first step, a multilevel pseudo random sequence (ML-PRS), which allows for modulating the operating temperature of the sensors in a wide frequency range, is used to obtain an estimate of the impulse response of each microsensor-gas system. ML-PRS are interesting because they help to reduce the effects of noise and non-linearity as experienced with gas sensors. In the second step, by analysing the spectral components of the impulse response estimates, the modulating frequencies that better help in discriminating and quantifying the gases and gas mixtures considered are found. Finally, by selecting a subset of the best modulating frequencies, an optimised multi-frequency temperature-modulating signal can be synthesised. The method is illustrated by analysing different concentrations of NH 3, NO 2 and their mixtures using a microarray of WO 3-based gas sensors, but it can be further applied to any given gas analysis problem.

SNMS investigations of platinum-doped nanogranular tin dioxide layers

Thin platinum-doped nanogranular SnO 2 layers are examined because of its high gas sensitivity and fast gas response to be applied in gas sensor microarrays. The nanogranular metal oxide layers were prepared from a colloidal dispersion using spin coating on silicon substrates. Field emission scanning electron microscopy (FE-SEM) investigations showed quite homogeneous layers of 20 nm particles, containing a few holes of some micron width, probably due to bubbles introduced into the layer during wet deposition. Depth resolved analysis with secondary neutral mass spectrometry (SNMS) was employed to characterize the elemental content and depth distribution of the 20 nm particle layers. A platinum content of approx. 1 at.%, homogeneously spread throughout the particles was found, as well as carbon and chlorine residues of a few atomic percent enriched at the surface of the particles.

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 oC/mm and 6.7 oC/mm, applied across the sensor elements (segments) of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis) coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

Surface analytical characterization of SiO2 gradient membrane coatings on gas sensor microarrays

Ion beam assisted deposition is applied to cover a gas sensor microarray of an electronic nose with an ultrathin gas-permeable SiO2 membrane varying in thickness across the array. Auger electron spectroscopy sputter depth profiles and non-Rutherford backscattering spectroscopy were used to study the uniformity of the deposition and the subsequent annealing step. The combination of spectroscopic ellipsometry for the freshly prepared membranes and line scans derived from Auger and angle resolved x-ray photoelectron spectroscopy, respectively, for the baked membrane is presented as a powerful quantification method for the determination of the desired SiO2 membrane thickness profiles.

Air quality monitoring and fire detection with the Karlsruhe electronic micronose KAMINA

An indoor air monitoring device is one of the most prominent consumer applications of an electronic nose (EN). Integral gas analysis similar to biogenic odor perception can be a versatile tool to obtain continuous information about pollutants, odors, and air compositions indicating gaseous precursors of dangers such as fires. However, an EN to be used as a common household device has to combine high sensitivity and excellent gas discrimination power with inexpensiveness, small size, and low power consumption. A special gas sensor microarray of thumbnail size has been developed at the Forschungszentrum Karlsruhe based on metal-oxide technology to meet these requirements. The microarray is produced by simply partitioning a monolithic metal-oxide layer with parallel electrode strips allowing low cost fabrication. A temperature gradient and a membrane thickness gradient (on metal-oxide layer) are responsible for differentiation between the individual sensor segments and thus for the conductivity patterns that are accordingly produced. The two membranes form the basis of gas discrimination power, reliability self checks, and online noise reduction. Model gas exposures usually show detection limits lower than 1 ppm. Successful practical tests are reported on the detection of overheated wire insulation for fire prevention as well as on air quality analysis for air conditioning purposes (e.g., air quality control during a meeting).

Gradient gas sensor microarrays for on-line process control — a new dynamic classification model for fast and reliable air quality assessment

A new dynamic gas classification model was developed to achieve a reliable on-line discrimination at very fast response times. The aim was to be able to follow rapid changes in gas compositions using an electronic nose in consumer applications. The electronic nose is based on a microarray especially designed for production at very low costs. This is essential for application in mass products. Common classification methods used for signal evaluation of electronic noses such as Linear Discriminant Analysis (LDA), Neural Networks (NN) or Soft Independent Modelling of Class Analogy (SIMCA) fail to detect non-stationary gas mixtures. The new model, however, combines classification of steady states with transient evaluation via time series analysis. Rapid signal transients are detected by appropriate digital filters, steady state signals are classified by the above mentioned standard methods. The simplicity of the algorithm model allows implementation in low-cost electronic units, containing micro controllers with very limited memory capacity. To give an example, the automatic control of the ventilation flap of automobiles was investigated. Intermediate streams of bad air could be detected within 1–2 s. The error of pollutants detection was reduced from 25%, applying static classification only, to 10% for the new dynamic model.

Automated cooking and frying control using a gas sensor microarray

A novel type of metal oxide gas sensor array was used in order to develop a so-called “intelligent cooking pot”. The sensor array consists of 40 monolithic sensor elements whose different sensitivity was achieved by a gradient technique. The sensor array was installed in the cover of a pot. With this set-up the feasibility of monitoring cooking processes by gas analysis was shown at the example of frying beef. The distinction of five different stages of the frying process was achieved by principal component analysis (PCA) of the signal patterns.

Application of a gas sensor microarray to human breath analysis

A novel type of metal oxide gas sensor array was used in order to develop a cheap instrument for a quick self-check of breath odor quality. The sensor array consists of 40 monolithic sensor elements whose different sensitivity was achieved by gradient techniques. SnO 2 and WO 3 coatings as detection layers were tested at different temperatures in order to optimize the system to a sensitive and fast detection of malodor components like NH 3, H 2S, CH 3SH, indole and putrescine, with little cross-sensitivity to ethanol and humidity.

Quality control of gas sensor microarrays using Auger electron spectroscopy

The role of Auger electron spectroscopy in evaluating and developing specific fabrication steps for gas sensor microarrays is exemplified. Using Auger line scans, the shadow mask sputter technique is evaluated regarding the geometrical integrity of the electrode pattern. The technique of ion beam assisted chemical vapor deposition is applied to coat the microarray with an ultrathin gas-permeable SiO 2 membrane varying in thickness across the sensor. A quantification method for the determination of the thickness profiles of the SiO 2 selector membrane and the carbon contamination layer is presented. Moreover, the influence of thermal annealing and long-term gas exposure on the device quality is studied.

Wedge-shaped ceramic membranes for gas sensor applications produced by a variety of CVD techniques

Abstract Various chemical vapor deposition (CVD) techniques, based on thermal CVD and ion beam assisted deposition, were used to deposit wedge-shaped SiO 2 or Al 2 O 3 membranes with controlled thickness gradients on gas sensor microarrays. Such a microarray consists of many gas sensors formed by segmentation of a sputtered metal oxide layer with parallel platinum strip electrodes. Owing to the varying thickness of the coating across the microarray a gradual change of the gas sensing properties of the sensor segments was achieved. The membranes with a thickness of up to about 500 nm were analyzed using X-ray photoelectron spectroscopy, secondary neutral mass spectrometry and ellipsometry in order to determine the chemical composition and the layer thickness of the membrane, as well as the thickness gradient. Wedge-shaped membranes with thickness gradients of 8.5 to 193 nm mm −1 were obtained.

Non-uniform SiO2 membranes produced by ion beam-assisted chemical vapor deposition to tune WO3 gas sensor microarrays

Ion beam-assisted chemical vapor deposition (IBAD) has been applied to prepare non-uniform SiO2 layers to adjust the gas selectivity of metal oxide gas sensors integrated in a microarray of 40 WO3 individually addressable gas sensor elements. SiO2 membranes with a thickness gradient in one direction are applied on microarrays to differentiate initially identical sensor elements with respect to their gas response. The analysis with X-ray photoelectron spectroscopy (XPS) and secondary neutral mass spectrometry (SNMS) showed the coating of the microarray by a SiO2 layer with a thickness gradient across the microarray to be successful. The gas-detecting properties were examined by exposing the SiO2-coated microarray to model gas atmospheres. A different gas response for each sensor element was obtained due to the non-uniform SiO2 coating resulting in decisive conductivity patterns of the microarray to be used for gas recognition.