• Title/Summary/Keyword: Bio-Sensor

Search Result 593, Processing Time 0.031 seconds

Carbon Monoxide Sensor Based on a B2HDDT-doped PEDOT:PSS Layer

  • Memarzadeh, R.;Noh, Hui-Bog;Javadpour, S.;Panahi, F.;Feizpour, A.;Shim, Yoon-Bo
    • Bulletin of the Korean Chemical Society
    • /
    • v.34 no.8
    • /
    • pp.2291-2296
    • /
    • 2013
  • An efficient carbon monoxide (CO) sensor was developed based on poly(3,4-ethylenedioxy)thiophenepoly(styrenesulfonate) (PEDOT:PSS) modified with a new pyrimidine-fused heterocyclic compound, bis(2-hydroxyphenyl)dihydropyrido[2,3-d:6,5-d]dipyrimidine-tetraone (B2HDDT). B2HDDT remains stable in the polymer matrix through interactions with functional groups of the polymer. It created prominent sites that captured CO gas, and the experimental parameters, including the amount of doped B2HDDT in the PEDOT:PSS film, were optimized. The sensor probe was also examined to verify its reliability for detecting CO in the presence of atmospheric gases in a discriminating manner. NMR, AFM, and FT-IR spectra were obtained to evaluate the structure and morphology of the B2HDDT-doped PEDOT:PSS (PEDOT:PSS/B2HDDT) film. The content of 35 vol % B2HDDT (7.0 mM) in PEDOT:PSS provided the largest response factor (${\Delta}R/R_o$) for the CO gas. The sensor response was reproducible, with a relative standard deviation < 5% (n = 5). The detection limit was determined to be $0.44{\pm}0.05$ vol %.

Tri-enzyme modified electrochemical biosensor for paracetamol detection (파라세타몰 검출을 위한 전기화학적 다중효소 바이오센서)

  • Park, Deog-Su;Shim, Yoon-Bo;Chang, Seung-Cheol
    • Journal of Sensor Science and Technology
    • /
    • v.17 no.1
    • /
    • pp.29-34
    • /
    • 2008
  • A new disposable amperometric tri-enzyme biosensor for the detection of paracetamol has been developed. The paracetamol sensors developed uses horseradish peroxidase modified screen-printed carbon electrodes (HRP-SPCEs) coupled with immobilized enzymes, tyrosinase and aryl acylamidase, prepared using a poly (vinyl alcohol) bearing styrylpyridinium groups (PVA-SbQ) matrix. Optimization of the experimental parameters has been performed and the paracetamol biosensor showed detection limit for paracetamol is as low as $100{\mu}M$ and the sensitivity of the sensor is $1.46nA{\mu}M^{-1}cm^{-2}$.

Fabrication of Optical Sheet for LED Lighting with Integrated Environment Monitoring Sensors (환경모니터링 센서가 집적된 LED 조명용 광학시트 제작)

  • Choi, Yong Joon;Lee, Young Tae
    • Journal of the Semiconductor & Display Technology
    • /
    • v.12 no.3
    • /
    • pp.35-39
    • /
    • 2013
  • In this paper, we developed an optical sheet for LED lighting with integrated $CO_2$ gas and temperature sensor which can monitor at the indoor environment. The optical sheet for LED lighting is fabricated through PMMA(Polymethyl methacrylate) injection process using mold. This research enables to fabricate the reflective sheet, light-guide plate and the prism sheet in a optical sheet. The fabricated sheet demonstrates higher intensity of optical efficiency compared with single-sided sheets. The $CO_2$ sensor was fabricated using NDIR(NON-Dispersive Infrared) method and it has $0.0235mV/V{\cdot}PPM$ sensitivity. The temperature sensor was fabricated using RTD(Resistance temperature detector) method and it has $0.563{\Omega}/^{\circ}C $sensitivity.

A Study on Emergency Monitoring Robot System by Back-Propagation Algorithm

  • Yoo, Sowol;Kim, Miae;Lee, Kwangok;Bae, Sanghyun
    • Journal of Integrative Natural Science
    • /
    • v.7 no.1
    • /
    • pp.62-66
    • /
    • 2014
  • This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

Yield Prediction of Chinese Cabbage (Brassicaceae) Using Broadband Multispectral Imagery Mounted Unmanned Aerial System in the Air and Narrowband Hyperspectral Imagery on the Ground

  • Kang, Ye Seong;Ryu, Chan Seok;Kim, Seong Heon;Jun, Sae Rom;Jang, Si Hyeong;Park, Jun Woo;Sarkar, Tapash Kumar;Song, Hye young
    • Journal of Biosystems Engineering
    • /
    • v.43 no.2
    • /
    • pp.138-147
    • /
    • 2018
  • Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

Development of an Automatic Sprayer Arm Control System for Unmanned Pest Control of Pear Trees (배나무 무인 방제를 위한 약대 자동 제어시스템 개발)

  • Hwa, Ji-Ho;Lee, Bong-Ki;Lee, Min-Young;Choi, Dong-Sung;Hong, Jun-Taek;Lee, Dae-Weon
    • Journal of Bio-Environment Control
    • /
    • v.23 no.1
    • /
    • pp.26-30
    • /
    • 2014
  • Purpose of this study was a development of a sprayer arm auto control system that could be operated according to distance from pear trees for automation of pest control. Auto control system included two parts, hardware and software. First, controller was made with an MCU and relay switches. Two types of ultra-sonic sensors were installed to measure distance from pear trees: one on/off type that detect up to 3 m, and the other continuous type providing 0~5 V output corresponding to distance of 0~3 m. Second, an auto control algorithm was developed to control. Each spraying arm was controlled according to the sensor-based distance from the pear trees. And it could dodge obstacles to protect itself. Max and min signal values were eliminated, when five sensor signals was collected, and then signals were averaged to reduce sensor's noises. According to results of field experiment, auto control test result was better than non auto control test result. Spraying rates were 69.25% (left line) and 98.09% (right line) under non auto control mode, because pear trees were not planted uniformly. But, auto control test's results were 92.66% (left line) and 94.64% (right line). Spraying rate was increased by maintaining distance from tree.