Herein, a new and generic strategy has been proposed to introduce uniformly distributed graphitic carbon into the nanostructured metal oxide. A facile and generic synthetic protocol has been proposed to introduce uniformly distributed conducting graphitic carbon into the Co3O4 nanoparticles ( Co3O4 NPs@graphitic carbon). The prepared Co3O4 NPs@graphitic carbon has been drop casted onto the portable screen-printed electrode (SPE) to realize its potential application in the individual and simultaneous quantification of toxic Pb(II) and Cd(II) ions present in aqueous solution. The proposed Co3O4 NPs@graphitic carbon-based electrochemical sensor exhibits a wide linear range from 0 to 120 ppb with limit of detection of 3.2 and 3.5 ppb towards the simultaneous detection of Pb(II) and Cd(II), which falls well below threshold limit prescribed by WHO.
The mercury ion ( Hg2+) is regarded as one of the toxic cations that is extremely harmful and dangerous to human health and the environment. With this growing awareness, it is imperative that facile and rapid sensing systems developed for the detection of Hg2+. Due to excellent sensitivity and selectivity, graphene quantum dots (GQDs), a zero-dimensional carbon nanomaterial, are attracting the attention of researchers as promising candidates as fluorescent probes for Hg2+ detection. This study aimed at conducting an in-depth review of recent advances into GQD-based materials as fluorescent probes in Hg2+ sensing. This systematic review was carried out by covering three main databases, namely, Scopus and Science Direct as the dominant databases, followed by Google Scholar as the supporting database. GQD-based materials encompassing bare GQDs, N-GQDs, B, N-GQDs, N, S-GQDs, N, K-GQDs, RhB-GQDs, Cys-GQDs, PEHA-GQD-DPA, Gly-GQDs, Mn(II)-NGQDs, NH2– Ru@ SiO2- NGQDs and FA-GQDs were discussed thoroughly with regard to their synthesis strategies, along with their potential application in the detection of Hg2+. The doping of heteroatoms is envisaged to enhance the quantum yield and selectivity of bare GQDs. This review might unlock a wide range of opportunities for the application of various GQD-based materials as an adaptable, feasible and scalable approach to the detection of Hg2+.
국내 유통되는 반려동물 사료의 살모넬라 분석시 증균 배양법, 효소면역기법에 의한 분석, 종 특이 primer를 활 용한 PCR 방법을 활용하여 비교 평가하였다. 시료 175점 Salmonella spp. 검출 결과 증균배양법 및 종 특이 primer를 활용한 PCR 방법에 의한 검출 방법에서 2점의 시료(육포, 옥수수 글루텐)가 양성으로 확인되었고, 효소면역기법에 의한 검출방법에서는 1점의 시료(옥수수 글루텐)가 양성 으로 확인되었다. 증균배양법 및 효소면역기법에 의한 검 출방법에 비해 종 특이 primer를 활용한 PCR 방법을 적 용 할 경우 시료에서 분리된 균주의 종(species) 판별이 가 능하였다.
PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.
In the present study, a novel electrochemical sensor for acetaminophen (AMP) which included quantum graphitic carbon nitride dots, g-C3N4QDs, was designed and conducted with molecular imprinted polymer (MIP). First, bulk g-C3N4 was generated with direct thermal polycondensation of melamine. After the treatment of the acidic solution containing H2SO4: HNO3 (1:1, v:v), the heating treatment at 200 °C on the dispersion provided g-C3N4QDs. In this respect, for nanomaterial characterization, some spectroscopic approaches were performed including Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) as well as electroanalytical methods such as electrochemical impedance (EIS) and cyclic voltammetry (CV). In accordance with the aims of the study, AMP imprinted electrode was formed after high electrocatalytic performance and linear range of 1.0 × 10– 11–2.0 × 10– 8 M and the LODs of 2.0 × 10– 12 was achieved. Eventually, an AMP-printed sensor was also used for AMP identification in pharmaceutical samples.
A solid-phase competition enzyme-linked immunosorbent assay (ELISA), recombinant VP2 (rVP2) protein, and monoclonal antibody (mAb) were developed for the specific and sensitive detection of porcine parvovirus (PPV) antibodies in pig sera. A total of 1,544 sera samples were collected from breeding pig farms located in the Gyeongsangbuk-do Province in the Republic of Korea. The optimal operating conditions of SC-ELISA were as follows. The concentration of rVP2 proteins coated on the wells was 4 μg/mL, the swine sera were diluted 1:2, and the HRP-conjugated PPV VP2 mAb (9A8 clone) was used at 500 ng/mL. These results suggest that the SC-rVP-ELISA assay may be a valuable alternative to the current diagnostic tools used to detect PPV-specific monoclonal antibodies and broadly monitor PPV infections in domestic pigs at different breeding stages.
우리나라는 여러 건의 여객선 사고를 겪으면서, 여객선 안전관리를 위해 다양한 제도를 운영하고 있다. 2021년 기준 우리나라 연안을 운항하는 여객선 162척 중, 차량갑판이 개방된 형태의 차도선이 105척(65 %)을 차지하고 있다. 차도선은 2~4개의 섬을 경유하는 운항 패턴을 가지고 있다. 출항지(모항)에서 안전점검은 선원과 운항관리실의 운항감독관, 해사안전감독관에 의해 실시된다. 경유지에서 의 안전점검은 자체점검이 실시되는 경우가 있다. 여느 제도와 마찬가지로 제도적, 현실적 한계 등이 있다. 이를 위해 영상처리기법을 활 용하여 차량을 검출하고 이를 선박 복원성 계산과 연동하는 방안을 제안하고자 본 연구를 수행하였다. 차량 검출을 위해 차영상을 이용 하는 방법과 기계학습을 이용하는 방법을 사용하였다. 검출된 데이터를 선박 복원성 계산에 활용하였다. 기계학습을 통해 차량을 검출하 는 경우, 차영상에 의한 차량 검출 방법보다 차량 식별에 안정적임을 알 수 있었다. 다만, 카메라가 일몰과 같은 상황에서 역광을 받는 경 우와 야간과 같은 상황에서 부두와 선박 내부의 강한 조명에 의해 차량이 식별되지 않는 한계가 있었다. 안정적인 영상처리를 위해 충분 한 영상 데이터 확보와 프로그램 고도화가 필요해 보인다.
Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).
The electrochemical type gas sensor has the advantage of being easy to use due its small size, and it is also relatively inexpensive. However, its output can easily vary depending on temperature and humidity conditions. Therefore, it is important to ascertain the exact output characteristics of a sensor according to the measuring environment in order to improve measurement accuracy for any set of given conditions. The purpose of this study is to obtain basic information about the output characteristics of a sensor that is used both indoor and outdoor according to the variation in temperature and humidity conditions in order to improve the accuracy of the sensor. To achieve this result, a calibration curve was made using ammonia standard gas and the calibration factor was calculated using the calibration curve and the measuring accuracy was confirmed with regard to the ammonia sensor. Based on the test results, the variation of the sensor output value was large in relation to temperature and humidity variation. It was found that the output value from the sensor at higher temperature and humidity conditions was also higher. However, the measuring accuracy of the sensor could be improved by more than 10% by applying the calibration factor and an average accuracy of more than 97% could be achieved. It is anticipated that the result of this study can be used as basic data to obtain more accurate results using electrochemical sensors for a given set of temperature and humidity conditions, and therefore, it can also be considered that the reliability and applicability of electrochemical sensors can be improved.
Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.
국내외 해상 위험·유해물질(HNS, Hazardous and Noxious Substances) 물동량 증가와 함께 HNS 유출 사고가 빈번히 발생하고 있다. HNS는 전 세계적으로 약 6,000여 종으로 대부분 유독한 성질을 가지므로 이러한 유출 사고 발생은 해양 생태계 파괴를 비롯하여 폭발 및 화재 등으로 인한 인명 및 재산피해를 유발한다. 따라서 해상 HNS 유출 사고를 대비하여 파장에 따른 HNS 분광 라이브러리 구축 및 탐지 알고리즘을 개발해야 한다. 본 연구에서는 프랑스 현지에서 지상 HNS 유출 실험을 진행하였다. 초분광센서 관측을 통해 파장에 따른 톨루엔 라이브러리 스펙트럼을 구축하였으며, 분광혼합 알고리즘을 활용하여 초분광 HNS를 탐지하였다. 전처리 과정으로 주성분 분석을 적용하여 노이즈 제거 및 차원 압축을 수행하였으며, N-FINDR 기법을 통해 영상을 대표하는 톨루엔과 해수의 엔드멤버 스펙트럼을 추출하였다. 스펙트럼 기반의 톨루엔 및 해수의 점유비율을 계산함으로써 모든 픽셀의 HNS 탐지 정확도를 확률로 제시하였다. 최대 탐지 정확도를 가지는 점유비율 선정을 위해 418.15 nm 파장의 복사도 영상과 비교하였으며, 그 결과 약 42%의 비율에 서 99% 이상의 정확도를 나타내었다. 해상 HNS 유출은 높은 위험성으로 인해 사람이 쉽게 접근할 수 없는 한계를 지닌다. 본 HNS 실험과정 및 탐지 결과는 초분광 원격탐사에 기반한 HNS 오염 해역 추정에 도움이 될 것이다.