• Journal of Sensor Science and Technology
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• v.29 no.6
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• pp.407-411
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• 2020

In this study, we propose an array-type gas sensor with high selectivity and response using multiple oxide semiconductors. The sensor array was composed of SnO₂ and In₂O₃, and the detection characteristics were improved by using Pt, Au, and Pd catalysts. All samples were deposited directly on the Pt interdigitated electrode (IDE) through the e-beam evaporator glancing angle deposition (GAD) method. They grew in the form of well-aligned nanorods at off-axis angles. The prepared SnO₂ and In₂O₃ nanorod samples were exposed to CH₃COCH₃, C₇H₈, and NO₂ gases in a 300℃ dry condition. Au-decorated SnO₂, Au-decorated In₂O₃, and Pd-decorated In₂O₃ exhibited high selectivity for CH₃COCH₃, C₇H₈, and NO₂, respectively. They demonstrated a high detection limit of the sub ppb level computationally. In addition, measurements from each sensor were executed in the 40% relative humidity condition. Although there was a slight reduction in detection response, high selectivity and distinguishable detection characteristics were confirmed.

• Journal of Sensor Science and Technology
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• v.21 no.6
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• pp.402-407
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• 2012

We report a Fabry-Perot interferometer (FPI)-based multi-channel micro-spectrometer used for multi-gas measurement in the spectral range of $3-5{\mu}m$ and its gas sensing performance. The fabricated infrared (IR) spectrometer consists of two parts: an FPI on the top side for selective IR filtering and a $V_2O_5$-based IR detector array on the bottom side for the detection of the filtered IR. Experimental results show that the FPI-based multi-channel gas sensor has reliability and selectivity for simultaneously detecting environmentally harmful gases such as $CH_4$, $CO_2$, $N_2O$ and CO in the spectral range of $3-5{\mu}m$. The fabricated FPI-based multi-channel gas sensor also demonstrated that a reliable and selective detection of gas concentrations ranging from 0 to 500 ppm is feasible. In addition, the electrical characteristics demonstrate a superior response performance in regards to the selectivity in the multi-target gases.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.138-140
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• 2005

A fuzzy ARTMAP neural network and a fuzzy ART neural network are proposed to identify $H_2S$, $NH_3$ and their mixtures and to estimate their concentrations, respectively. Features are extracted from a micro gas sensor array operated in a thermal modulation plan. After dimensions of the features are reduced by a preprocessing scheme, the features are fed into the proposed fuzzy neural networks. By computer simulations, the proposed methods are shown to be fast in learning and accurate in concentration estimating. The results are compared with other methods and discussed.

• Journal of the Korean Institute of Gas
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• v.24 no.2
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• pp.44-49
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• 2020

In this paper, a measuring circuit is designed through analyzing manufacture specification of the sensor based on MOS. And the best input-output polynomial are induced that really gas sensors are used in gas safety management industrial fields. Response characteristics of a MOS gas sensor is analysed by through sensor's output voltages are measured after standard gases with six kinds of concentrations are manufactured and are injected to the sensor. A lookup table is created by relations of sensor's output voltages by injecting gases with other concentrations. Because data of the formed lookup table are equal interval, a polynomial can be induced of method of approximation function. So the 5th polynomial of input-output for a sensor is defined, coefficients are calculated by using least squares method, and the 5th polynomial is completed for representing characteristics of the sensor. If the proposed polynomial is applied to gas leak detectors, an inverse transformation of polynomial and programing of array codes are recreated. In this research, polynomial is implemented with array types that intervals of values of a lookup table are one-fifth sampled and interpolated. The performance of proposed 5th calibration equation is verified that errors are reduced than a linear expression when tests are performed by measurement of concentrations against injection of standard gases.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.309-312
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• 2001

A primitive gas classification system which can classify limited species of gas was designed and simulated. The 'electronic nose' consists of an array of 4 metal oxide gas sensors with different selectivity patterns, signal collecting unit and a signal pattern recognition and decision Part in PLD(programmable logic device) chip. Sensor array consists of four commercial, tin oxide based, semiconductor type gas sensors. BP(back propagation) neutral networks with MLP(Multilayer Perceptron) structure was designed and implemented on CPLD of fifty thousand gate level chip by VHDL language for processing the input signals from 4 gas sensors and qualification of gases in air. The network contained four input units, one hidden layer with 4 neurons and output with 4 regular neurons. The 'electronic nose' system was successfully classified 4 kinds of industrial gases in computer simulation.

• Korean Journal of Materials Research
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• v.23 no.12
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• pp.667-671
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• 2013

We present an easy method of preparing two-dimensional (2D) periodic hollow tin oxide ($SnO_2$) hemisphere array gas sensors using polystyrene (PS) spheres as a template. The structures were fabricated by the sputter deposition of thin tin (Sn) metal over an array of PS spheres on a planar substrate followed by calcination at an elevated temperature to oxidize Sn to $SnO_2$ while removing the PS template cores. The $SnO_2$ hemisphere array structures were examined by scanning electron microscopy and X-ray diffraction. The structures were calcined at various temperatures and their sensing properties were examined with varying operation temperatures and concentrations of nitric oxide (NO) gas. Their gas-sensing properties were investigated by measuring the electrical resistances in air and the target gases. The measurements were conducted at different NO concentrations and substrate temperatures. A minimum detection limit of 30 ppb, showing a sensitivity of S = 1.6, was observed for NO gas at an operation temperature of $150^{\circ}C$ for a sample having an Sn metal layer thickness corresponding to 30 sec sputtering time and calcined at $600^{\circ}C$ for 2 hr in air. We proved that high porosity in a hollow $SnO_2$ hemisphere structure allows easy diffusion of the target gas molecules. The results confirm that a 2D hollow $SnO_2$ hemisphere array structure of micronmeter sizes can be a good structural morphology for high sensitivity gas sensors.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.123-126
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• 2005

The IEEE 1451.4 standards defines an architectural model for interfacing smart transducers for sensors & actuators. This standard allows analogue transducers to communicate their identification and calibration data in a digital format. A digital format is called TEDS(transducer electronic data sheet). However, the standard template of IEEE 1451.4 TEDS do not supports gas sensors to use in electronic nose system, such as array sensors. In this paper, a solution to standardize sensors for electronic nose systems is presented.

• Journal of Sensor Science and Technology
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• v.11 no.5
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• pp.286-293
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• 2002

We fabricated gas recognition system using conducting polymer sensor array for recognizing and analyzing VOCs(Volatile Organic Compounds) gases. The polypyrrole and polyaniline thin film sensors which were made by chemical polymerization were employed to detect VOCs. The multi-dimensional sensor signals obtained from the sensor array were analyzed using PCA(principal component analysis) technique and RBF(radial basis function) Network. Throughout the experimental trails, we confirmed that RBF Network is effective than PCA technique in identifying VOCs.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1549-1552
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• 2003

Semiconductor gas sensor using tin oxide as sensing material has been used to detect gases based on the fact that impedance of the sensing material varies when the gas sensor is exposed to the gases. This variation comprises of two parts. The first one is variation in resistance of the sensing material and the other is expressed in terms of the sensor capacitance variation. Normally, only variation of the sensor resistance is considered. In this paper, using AC measurement with a capacitor-coupled inverting amplifier circuit, both changes in the sensor resistance and variations in the sensor capacitance were investigated. These characteristics were represented as magnitude gain and phase shift of AC signal at a specific frequency after passing it through the sensor and the designed circuit. A two-stage artificial neural network, which utilized the information above, was employed to identify and quantify three combustible gases: methane, propane and butane. The network outputs were approximately proportional to concentrations of test gases with reasonable level of error.

• Ceramist
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• v.22 no.1
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• pp.70-81
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• 2019

Breath analysis is rapidly evolving as a non-invasive disease recognition and diagnosis method. Metal oxide gas sensors are one of the most ideal platforms for realizing portable, hand-held breath analysis devices in the near future. This paper reviewed the recent developments in metal oxide gas sensors detecting exhaled biomarker gases such as nitric oxides, acetone, ammonia, hydrogen sulfide, and hydrocarbons. Emphasis was placed on strategies to tailor sensing materials/films capable of highly selective and sensitive detection of biomarker gases with negligible cross-response to ethanol, the major interfering breath gas. Specific examples were given to highlight the validity of the strategies, which include optimization of sensing temperature, doping additives, utilizing acid-base interaction, loading catalysts, and controlling gas reforming reaction. In addition, we briefly discussed the design and optimization method of gas sensor arrays for implementing the simultaneous assessment of multiple diseases. Breath analysis using high-performance metal oxide gas sensors/arrays will open new roads for point-of-care diagnosis of diseases such as asthma, diabetes, kidney dysfunction, halitosis, and lung cancer.