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123 results on '"Park, Yangkyu"'

8. MOF(CuBDC)-Microcantilever IR Spectroscopy for Methane Sensing with High Sensitivity and Selectivity.

Author

Seo, Seungwan, Kwon, Seok Bin, and Park, Yangkyu

Subjects
FOURIER transform infrared spectroscopy, INFRARED spectroscopy, SCANNING electron microscopy, METAL-organic frameworks, CARBON dioxide
Abstract

Methane, a greenhouse gas with 21 times the global warming potential of carbon dioxide, is increasingly subject to stringent emission regulations, driving the demand for high-performance methane sensors. This study proposes a novel IR spectroscopy technique based on a CuBDC-integrated microcantilever (CuBDC-microcantilever IR spectroscopy) for CH4 sensing, offering exceptional sensitivity and selectivity. The metal-organic framework (MOF) CuBDC was synthesized on the microcantilever using a drop-and-dry method facilitated by an intense pulsed light technique. Characterization via scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy confirmed the successful formation of CuBDC on the microcantilever. The CuBDC-microcantilever IR spectroscopy demonstrated a significantly enhanced sensitivity, with a differential amplitude at the CH4 characteristic peak approximately 13 times higher than that of a conventional Si microcantilever. Moreover, the limit of detection was determined to be as low as 14.05 ppm. The clear separation of the CH4 characteristic peak from the water and acetone vapor peaks also emphasized the sensor's high selectivity. These findings highlight the superior sensitivity and selectivity of the proposed sensor, positioning it as a promising platform for CH4 detection in industrial and environmental applications. [ABSTRACT FROM AUTHOR]

Published
2025
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15. Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning.

Author

Jeong, Ho-Jung, Park, Chang-Ju, Kim, Kihyun, and Park, Yangkyu

Subjects
MACHINE learning, FOURIER transform infrared spectroscopy, LASER spectroscopy, PENTAERYTHRITOL tetranitrate, ALUMINUM oxide
Abstract

Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Heater-Integrated Flexible Piezoresistive Pressure Sensor Array for Smart-Car Seats

Author

Park, Yangkyu, Gwon, Na-Hyeon, Seong, Woo-Kyeong, and Kim, Wonhyo

Abstract

With the growing popularity of autonomous vehicles, the need for real-time monitoring of occupant posture has led to the requirement for a pressure sensor array. Moreover, heated car seats not only offer drivers comfort but also prevent cramped postures, allowing pressure sensors to accurately measure drivers’ pressure distribution and enhance safety in autonomous driving. Herein, we propose a heater-integrated pressure sensor array designed specifically for seats in a commercially available GV80 car. The unique signal lines in our sensor enable the implementation of both sensing and heating functionalities within a single substrate, streamlining the fabrication process for the heater-integrated sensor. The sensor was characterized in terms of sensitivity, linearity, response time, repeatability, hysteresis, body temperature effect, and pressure interference among sensing elements. Our sensor exhibited a wide linearity region of 17.06 kPa with a maximum linearity error of 4.4% and a fast response time of less than 4 ms. It operated reliably with a maximum relative standard deviation (RSD) of 5.2% and a hysteresis error of 12.2%. The maximum resistance difference affected by the body temperature was as small as 8.5%, and the resistance changes of one sensing element were almost constant, regardless of the pressure applied to the other sensing element. Additionally, we studied the variations in sensor characteristics with respect to heater temperatures. Furthermore, we customized the embedded system and graphical user interface (GUI) to demonstrate sensor responses according to five different postures. Thus, our heater-integrated sensor shows promise as a potential device for emerging smart-car seats.

Published
2024
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