화학오염운 탐지를 위한 접촉식 화학탐지기를 탑재한 무인기와 원거리 화학탐지기의 복합 운용개념 고찰

이명재,정유진,정영수,이재환,남현우,박명규 한국군사과학기술학회 2020 한국군사과학기술학회지 Vol.23 No.3

Early-detection and monitoring of toxic chemical gas cloud with chemical detector is essential for reducing the number of casualties. Conventional method for chemical detection and reconnaissance has the limitation in approaching to chemically contaminated site and prompt understanding for the situation. Stand-off detector can detect and identify the chemical gas at a long distance but it cannot know exact distance and position. Chemical detection UAV is an emerging platform for its high mobility and operation safety. In this study, we have conducted chemical gas cloud detection with the stand-off chemical detector and the chemical detection UAV. DMMP vapor was generated in the area where the cloud can be detected through the field of view(FOV) of stand-off chemical detector. Monitoring the vapor cloud with standoff detector, the chemical detection UAV moved back and forth at the area DMMP vapor being generated to detect the chemical contamination. The hybrid detection system with standoff cloud detection and point detection by chemical sensors with UAV seems to be very efficient as a new concept of chemical detection.

Development of highly-stable binder-free chemical sensor electrodes for p-nitroaniline detection

Ahmad, Rafiq,Tripathy, Nirmalya,Ahn, Min-Sang,Hahn, Yoon-Bong Elsevier 2017 JOURNAL OF COLLOID AND INTERFACE SCIENCE - Vol.494 No.-

<P><B>Abstract</B></P> <P>Herein, pre-seeded fluorine doped tin oxide (FTO) glass substrates were used as an electrode for zinc oxide nanorods (ZnO NRs) growth by a low-temperature solution route in order to fabricate binder-free high-sensitive chemical sensor. The vertically-grown ZnO NRs exhibited a more favorable active morphology and improved sensing properties for <I>p</I>-nitroaniline (pNA) detection. On investigation with different concentrations of pNA, the ZnO NRs/FTO electrode showed an excellent sensitivity (10.18μAμM<SUP>−1</SUP> cm<SUP>−2</SUP>) and low detection limit (0.5μM) with good selectivity, outstanding long-term stability, and high reproducibility. Collectively, the present work emphasizes the potency of ZnO NRs/FTO electrodes for fabrication of an efficient and reliable chemical sensing device with improved performances.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Solution-based, direct growth of ZnO nanorods on FTO electrode. </LI> <LI> Used successfully for the detection of p-nitroaniline chemical. </LI> <LI> As-fabricated sensor showed excellent sensitivity and outstanding long-term stability. </LI> <LI> The chemical sensors were highly reproducible and repeatable. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

M13 Bacteriophage/Silver Nanowire Surface-Enhanced Raman Scattering Sensor for Sensitive and Selective Pesticide Detection

Koh, Eun Hye,Mun, ChaeWon,Kim, ChunTae,Park, Sung-Gyu,Choi, Eun Jung,Kim, Sun Ho,Dang, Jaejeung,Choo, Jaebum,Oh, Jin-Woo,Kim, Dong-Ho,Jung, Ho Sang American Chemical Society 2018 ACS APPLIED MATERIALS & INTERFACES Vol.10 No.12

<P>A surface-enhanced Raman scattering (SERS) sensor comprising silver nanowires (AgNWs) and genetically engineered M13 bacteriophages expressing a tryptophan-histidine-tryptophan (WHW) peptide sequence (BPWHW) was fabricated by simple mixing of BPWHW and AgNW solutions, followed by vacuum filtration onto a glass-fiber filter paper (GFFP) membrane. The AgNWs stacked on the GFFP formed a high density of SERS-active hot spots at the points of nanowire intersections, and the surface-coated BPWHW functioned as a bioreceptor for selective pesticide detection. The BPWHW-functionalized AgNW (BPWHW/AgNW) sensor was characterized by scanning electron microscopy, confocal scanning fluorescence microscopy, atomic force microscopy, and Fourier transform infrared spectroscopy. The Raman signal enhancement and the selective pesticide SERS detection properties of the BPWHW/AgNW sensor were investigated in the presence of control substrates such as wild-type M13 bacteriophage-decorated AgNWs (BPWT/AgNW) and undecorated AgNWs (AgNW). The BPWHW/AgNW sensor exhibited a significantly higher capture capability for pesticides, especially paraquat (PQ), than the control SERS substrates, and it also showed a relatively higher selectivity for PQ than for other bipyridylium pesticides such as diquat and difenzoquat. Furthermore, as a field application test, PQ was detected on the surface of PQ-pretreated apple peels, and the results demonstrated the feasibility of using a paper-based SERS substrate for on-site residual pesticide detection. The developed M13 bacteriophage-functionalized AgNW SERS sensor might be applicable for the detection of various pesticides and chemicals through modification of the M13 bacteriophage surface peptide sequence.</P> [FIG OMISSION]</BR>

STI-CMP 공정 적용을 위한 연마 정지점 고찰

김상용,서용진 한국전기전자재료학회 2001 전기전자재료학회논문지 Vol.14 No.3

In this study, the improved throughput and stability in device fabrication could be obtained by applying CMP process to STi structue in 0.18 um semiconductor device. To employ the CMP process in STI structure, the Reverse Moat Process used to be added after STI Fill, as a result, the process became more complex and the defect were seriously increased than they had been,. Removal rate of each thin film in STI CMP was not uniform, so, the device must have been affected. That is, in case of excessive CMP, the damage on the active area was occurred, and in the case of insufficient CMP nitride remaining was happened on that area. Both of them deteriorated device characteristics. As a solution to these problems, the development of slurry having high removal rate and high oxide to nitride selectivity has been studied. The process using this slurry afford low defect levels, improved yield, and a simplified process flow. In this study, we evaluated the 'High Selectivity Slurry' to do a global planarization without reverse moat step, and also we evaluated EPD(Eend Point Detection) system with which 'in-situ end point detection' is possible.

Photoreactivity of Anthraquinones for the Analysis of Ginsenosides Using Photoreduction Fluorescence Detection-HPLC

Park, Man-Ki,Kim, Bak-Kwang,Park, Jeong-Hill,Shin, Young-Geun,Cho, Kyung-Hee,Do, Young-Mi The Pharmaceutical Society of Korea 1996 Archives of Pharmacal Research Vol.19 No.6

The photoreactivity of twelve anthraquinone derivatives was examined to evaluate its usefulness as a photo-reagent for the analysis of ginsenosides using photoreduction fluorescence (PRF) detection method. Among the tested compounds, 2-tert-butylandthraquinone (TBAQ), 2-chloroanthraquinone (CAQ) and anthraquinone (AQ) showed good characteristics as photoreagents. The detection limits of ginsenoside $Rg_{1}$PRF-HPLC method using TBAQ, CAQ or AQ as a photo-reagent were found to be ca. 35 ng, 50 ng and 50 ng, respectively.

아두이노를 이용한 스마트 안전모

이동건,김원범,김중수,임상근,공기석 한국인터넷방송통신학회 2019 한국인터넷방송통신학회 논문지 Vol.19 No.1

Major causes of industrial accidents include falls and gas leak. The existing safety helmet and smart device combination products are focused on convenience, so the functions to prevent such accidents are insufficient. We developed a smart helmet focusing on fall accident detection and gas leak detection. We also developed management system to manage workers efficiently. Its core function is to detect dangerous conditions of employees, to communicate with managers and to confirm the situations of workers. The effectiveness of the combustible gas measurement capability was verified through experiments. However, since a significant amount of power consumption is founded due to continuous operation of the board and the sensor, countermeasures such as replacing with a large capacity battery are required. 산업 재해의 주요 원인에는 추락사고, 가스 누출 등이 있다. 기존의 안전모와 스마트 디바이스 결합 제품들은 편의성에 초점을 맞춰져 있어 위와 같은 사고를 예방하기 위한 기능이 미흡하다. 본 논문에서는 추락사고 인지와 가스 누출 감지기능에 중점을 둔 스마트 안전모 개발을 다루었다. 또한 효율적으로 근로자를 관리할 수 있는 관리 시스템을 개발하였다. 이 시스템의 핵심 기능은 근로자의 위험 상태를 감지하여 관리자에게 전달하고 근로자의 상태를 확인하는 것이다. 실험을통해 가연성 가스 측정 능력의 효용성을 검증하였다. 하지만 보드와 센서의 지속적인 동작으로 인해 상당한 전력 소모가발견됨에 따라 대용량 배터리로 교체하는 등의 대응 방안이 요구된다는 점도 발견하였다.

수학적 변환과 심층 생성 모델을 활용한 DMMP와 2-CEES의 모의 라만 분광 생성

박성원,정보성,김홍중 한국군사과학기술학회 2023 한국군사과학기술학회지 Vol.26 No.5

To build an automated system detecting toxic chemicals from Raman spectra, we have to obtain sufficient data of toxic chemicals. However, it usually costs high to gather Raman spectra of toxic chemicals in diverse situations. Tackling this problem, we develop methods to generate synthetic Raman spectra of DMMP and 2-CEES without actual experiments. First, we propose certain mathematical transforms to augment few original Raman spectra. Then, we train deep generative models to generate more realistic and diverse data. Analyzing synthetic Raman spectra of toxic chemicals generated by our methods through visualization, we qualitatively verify that the data are sufficiently similar to original data and diverse. For conclusion, we obtain a synthetic dataset of DMMP and 2-CEES with the proposed algorithm

수학적 변환과 심층 생성 모델을 활용한 DMMP와 2-CEES의 모의 라만 분광 생성

박성원,정보성,김홍중 한국군사과학기술학회 2023 한국군사과학기술학회지 Vol.26 No.6

. To build an automated system detecting toxic chemicals from Raman spectra, we have to obtain sufficient data of toxic chemicals. However, it usually costs high to gather Raman spectra of toxic chemicals in diverse situations. Tackling this problem, we develop methods to generate synthetic Raman spectra of DMMP and 2-CEES without actual experiments. First, we propose certain mathematical transforms to augment few original Raman spectra. Then, we train deep generative models to generate more realistic and diverse data. Analyzing synthetic Raman spectra of toxic chemicals generated by our methods through visualization, we qualitatively verify that the data are sufficiently similar to original data and diverse. For conclusion, we obtain a synthetic dataset of DMMP and 2-CEES with the proposed algorithm

수동형 FTIR 분광계에서 초동 탐지 기법을 이용한고속 원거리 화학 가스 탐지 알고리즘

유형근,박동조,남현우,박병황 한국군사과학기술학회 2018 한국군사과학기술학회지 Vol.21 No.6

In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.

군집화를 통해 추출한 AI 학습용 분광 스펙트럼 데이터 기반 유해화학물질 판독 모델

유성민(Seong-Min Ryoo),김연진(Yeon-Jin Kim),조숙경(Sook-Kyung Cho),백성하(Sung-Ha Baek),김경배(Gyeong-Bae Kim) 한국통신학회 2024 韓國通信學會論文誌 Vol.49 No.8

유해화학물질 사고는 소량이라도 주변에 큰 영향을 미칠 수 있기에 사고 발생 시 해당 화학물질을 신속하게 식별하고 적절히 대응하는 것이 매우 중요하다. 기존의 화학물질판독 연구는 화학물질 센서 및 영상정보를 활용하였으나, 화학물질 센서를 기반으로 한 방법은 센서가 없는 경우 적용이 어려우며, 영상정보를 기반으로 한 방법은 물질의 색상이 동일하거나 무색인 경우에 탐지가 어려운 문제점이 있다. 따라서 본 논문에서는 동일한 색상이나 무색의 유해화학물질을 구분할 수 있도록 분광 스펙트럼 데이터를 이용한 새로운 AI 기반 유해화학물질 판독 시스템의 인공지능학습데이터 구축 기법 및 유해화학물질 판독 모델을 제안한다. 제안된 인공지능학습데이터 구축 기법은 원시 분광 스펙트럼 데이터에 군집화를 통한 데이터 추출 방법을 적용하여 데이터 추출의 정확도를 향상시켰다. 또한, 추출한 데이터를 기반으로 물질의 분광 스펙트럼 데이터의 특성을 반영해서 랜덤 포레스트 알고리즘을 활용해 구현한 유해화학물질 판독 모델을 제안하였고, 자체 실험을 통해 모델의 성능을 검증했다. 본 논문에서 제안된 인공지능기반 유해화학물질 판독 시스템은 시각적 특징이 없는 유해화학물질 사고 발생 시에도 신속한 판독이 가능함으로써 선제적 대응을 통해 화학 사고의 피해를 줄일 수 있을 것으로 기대된다. Hazardous chemical incidents can have a significant impact on the surroundings even with small quantities involved. Therefore, it is crucial to swiftly identify the chemical substance involved in an incident and respond appropriately when such incidents occur. Conventional studies on chemical substance identification have utilized chemical sensors and visual information. However, methods based on chemical sensors are challenging to apply when sensors are unavailable, and approaches relying on visual information face difficulties in detecting substances with identical or colorless characteristics. Therefore, this paper proposes a novel artificial intelligence-based hazardous chemical detection system using spectral spectrum data to address the challenge of distinguishing hazardous chemicals with the same color or those that are colorless. The paper introduces a technique for constructing artificial intelligence training data and a hazardous chemical detection model based on spectral spectrum data. The proposed artificial intelligence training data construction method improved the accuracy of data extraction by applying clustering to raw spectral spectrum data for extraction. Additionally, based on the extracted data and reflecting the characteristics of material spectral spectrum data, we proposed a hazardous chemical detection model implemented using the random forest algorithm. We validated the performance of the model through in-house experiments. The AI-based hazardous chemical detection system proposed in this paper is expected to minimize the damage from chemical incidents through rapid identification, even in cases where hazardous chemicals lack visual characteristics, enabling proactive response measures.