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An immunoassay using biotinylated single-walled carbon nanotubes as Raman biomarkers
Cao, Chengfan,Kim, Jung Heon,Kwon, Ye-Jin,Kim, Young-Jin,Hwang, Eung-Soo,Baik, Seunghyun Royal Society of Chemistry 2009 The Analyst Vol.134 No.7
<P>A new immunoassay with biotinylated single-walled carbon nanotubes as persistent, non-photobleaching Raman biomarkers demonstrated excellent sensitivity and specificity.</P> <P>Graphic Abstract</P><P>A new immunoassay with biotinylated single-walled carbon nanotubes as persistent, non-photobleaching Raman biomarkers demonstrated excellent sensitivity and specificity. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=b900501n'> </P>
단일벽 탄소 나노튜브의 흡광도 신호를 이용한 DNA 결합 측정
조승범(Chengfan Cao),윤도경(Dokyung Yoon),최재붕(Jae-Boong Choi),김영진(Young-Jin Kim),백승현(Seunghyun Baik) 대한기계학회 2007 대한기계학회 춘추학술대회 Vol.2007 No.10
Single walled carbon nanotubes(SWCNTs) can be applied for biomolecular sensors and/or markers due to their small size and excellent optical properties such as near infrared fluorescence and absorption. Here, we put out an optical biosensor using absorption signal of SWCNTs to detect DNA hybridization. SWCNTs exhibit sharp absorption peak distributions at different wavelengths when they exist in a well-dispersed solution state. It is found that DNA hybridization results in red peak shifts of nanotubes in the near infrared region. Compared to metallic species, semiconducting SWCNTs show manifest variations. The findings in this work make SWCNTs be used to selectively recognize various DNA interactions.
Yoon, Dokyung,Cao, Chengfan,Choi, Jae-Boong,Kim, Young-Jin,Baik, Seunghyun American Scientific Publishers 2008 Journal of Nanoscience and Nanotechnology Vol.8 No.10
<P>We have investigated the effect of ultraviolet irradiation exposure time on single walled carbon nanotubes (SWNTs) wrapped with single-strand DNA (ssDNA) using absorption spectroscopy and Raman spectroscopy. The absorbance value of ssDNA-wrapped SWNTs in aqueous media was found to be decreased, where as SWNTs wrapped with ssDNA with polyd(T) were aggregated under UV irradiation.</P>
Random Weighting Estimation for Systematic Error of Observation Model in Dynamic Vehicle Navigation
Wenhui Wei,Shesheng Gao,Yongmin Zhong,Chengfan Gu,Aleksandar Subic 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.2
The Kalman filter requires kinematic and observation models not contain any systematic error. Otherwise,the resultant navigation solution will be biased or even divergent. In order to overcome this limitation, thispaper presents a new random weighting method to estimate the systematic error of observation model in dynamicvehicle navigation. This method randomly weights the covariance matrices of observation residual vector, predictedresidual vector and estimated state vector to control their magnitudes, thus governing the random weighting estimationfor the covariance matrix of observation vector. Random weighting theories are established for estimationsof the observation model’s systematic error and the covariance matrices of observation residual vector, predictedresidual vector, observation vector and estimated state vector. Experiments and comparison analysis with the existingmethods demonstrate that the proposed random weighting method can effectively resist the disturbance ofthe observation model’s systematic error on the state parameter estimation, leading to the improved accuracy fordynamic vehicle navigation.
Shufeng Fan,Long Yi Jin,Zhuoshi Wang,Chengfan Li,Yongri Liang,Tie Chen 한국고분자학회 2015 Macromolecular Research Vol.23 No.10
Triblock coil-rod-coil compounds, consisting of four biphenyls and a p-terphenyl unit linked together with ether bonds as a rod segment, incorporating lateral methyl or ethoxymethyl groups at 2 and 5 positions of the benzene ring of p-terphenyl and poly(ethylene oxide) (PEO) with a degree of polymerization of 7, 12, 17, incorporating lateral methyl groups between the rod and coil segment as the coil segments, were synthesized. The compound structures were characterized with 1H NMR and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectroscopy. The self-assembling behavior of the compounds is investigated by means of differential scanning calorimetry (DSC), small-angle X-ray scattering (SAXS), and optical polarized micrograph (POM) in the bulk state. These compounds spontaneously self-assemble into lamellar, hexagonal perforated lamellar, rectangular columnar, and oblique columnar structures in the crystalline phase, and hexagonal perforated lamellar, bicontinuous cubic, and oblique columnar structures in the liquid crystalline phase, respectively. The results revealed that the lateral methyl groups attached to the interface of rod and coil segments strongly induce the compounds to self-assemble into various supramolecular nanostructures in the crystalline phase and the liquid crystalline mesophase.
Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter
Bingbing Gao,Shesheng Gao,Yongmin Zhong,Gaoge Hu,Chengfan Gu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.5
The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamicsystems due to its simple calculation process and superior performance in highly nonlinear systems. However, itssolution will be degraded or even divergent when the system model involves uncertainty. This paper presents aninteracting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This methodcombines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbanceof system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process modeluncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principleof innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF androbust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilisticweighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparisonanalysis validate the efficacy of the proposed method.
Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter
Bingbing Gao,Gaoge Hu,Shesheng Gao,Yongmin Zhong,Chengfan Gu 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration.