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

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.9
• /
• pp.1641-1647
• /
• 2006

A dual micro gas sensor way for detecting reducing gas and bad order was fabricated using nano sized $SnO_2$ thin film fabrication method. To make nano-sized thin gas sensitive $SnO_2$ thin rilm, thin tin metal layer $2500{\AA}$ thick was oxidized between 600 and $800^{\circ}C$ by thermal oxidation. The gas sensing layers such as $SnO_2,\;SnO_2(+Pt)\;and\;SnO_2(+CuO)$ were patterned by metal shadow mask for simple fabrication process on the silicon substrate. The micro gas sensors with $SnO_2(Pt)$ and $SnO_2(+CuO)$ showed good selectivity to CO gas among reducing gases and good sensitivity to $H_2S$ that is main component of bad odor, separately.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.3
• /
• pp.590-595
• /
• 2008

A new adaptive TMS variable area flow meter that is used to the environment measuring equipment is proposed. This system is consist of a ball moving within the tube line which corresponds to gas flow and photo sensor array which monitoring the movement of ball. This system can monitoring the position of bali in case of the very few gas flow in various levels. And it can automatically adjust the gas flow at the highest and the lowest level to prevent the tube line blockage.

• Journal of Sensor Science and Technology
• /
• v.33 no.2
• /
• pp.112-116
• /
• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• Journal of Sensor Science and Technology
• /
• v.8 no.6
• /
• pp.461-468
• /
• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• Journal of the Korean institute of surface engineering
• /
• v.54 no.5
• /
• pp.260-266
• /
• 2021

Synthesizing low-dimensional structures of oxide semiconductors is a promising approach to fabricate highly efficient gas sensors by means of possible enhancement in surface-to-volume ratios of their sensing materials. In this work, vertically aligned zinc oxide (ZnO) nanorods are successfully synthesized on a transparent glass substrate via seed-mediated hydrothermal synthesis method with the use of a ZnO nanoparticle seed layer, which is formed by thermally oxidizing a sputtered Zn metal film. Structural and optical characterization by x-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy reveals the successful preparation of the ZnO nanorods array of the single hexagonal wurtzite crystalline phase. From gas sensing measurements for the nitrogen dioxide (NO₂) gas, the vertically aligned ZnO nanorod array is observed to have a highly responsive sensitivity to NO₂ gas at relatively low concentrations and operating temperatures, especially showing a high maximum sensitivity to NO₂ at 250 ℃ and a low NO₂ detection limit of 5 ppm in dry air. These results along with a facile fabrication process demonstrate that the ZnO nanorods synthesized on a transparent glass substrate are very promising for low-cost and high-performance NO₂ gas sensors.

• Journal of the Korean institute of surface engineering
• /
• v.56 no.4
• /
• pp.219-226
• /
• 2023

Utilizing low-dimensional structures of oxide semiconductors is a promising approach to fabricate relevant gas sensors by means of potential enhancement in surface-to-volume ratios of their sensing materials. In this work, vertically aligned cupric oxide (CuO) nanorods are successfully synthesized on a transparent glass substrate via seed-mediated hydrothermal synthesis method with the use of a CuO nanoparticle seed layer, which is formed by thermally oxidizing a sputtered Cu metal film. Structural and optical characterization by x-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy reveals the successful preparation of the CuO nanorods array of the single monoclinic tenorite crystalline phase. From gas sensing measurements for the nitrogen monoxide (NO) gas, the vertically aligned CuO nanorod array is observed to have a highly responsive sensitivity to NO gas at relatively low concentrations and operating temperatures, especially showing a high maximum sensitivity to NO at 200 ℃ and a low NO detection limit of 2 ppm in dry air. These results along with a facile fabrication process demonstrate that the CuO nanorods synthesized on a transparent glass substrate are very promising for low-cost and high-performance NO gas sensors.

• Electrical & Electronic Materials
• /
• v.8 no.4
• /
• pp.509-517
• /
• 1995

• Journal of Sensor Science and Technology
• /
• v.33 no.3
• /
• pp.185-185
• /
• 2024

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.431-435
• /
• 2013

Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.