Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Design of Au@WO3 core−shell structured nanospheres for ppb-level NO2 sensing

Abstract: As motivated by the driving force of the urgent demand for high-performance nitrogen dioxide (NO2) sensor, in this work, a novel structure of Au@WO3 core–shell nanospheres (CSNSs) was designed and successfully prepared for NO2 detection. The techniques of X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, Brunauer-Emmett-Teller measurement, and elemental mapping analysis were used for the characterizations of the obtained samples. The results demonstrated that Au nanoparticles with 25–50 nm in diameter were successfully encapsulated by WO3 shells with the thickness of 30–50 nm. The NO2 sensing performance of the Au@WO3 CSNSs as well as the pure WO3 nanospheres were systematically investigated. Compared with the pure WO3 nanospheres, Au@WO3 CNNSs exhibited overall enhanced NO2 sensing performances in terms of response, detection limit, and response/recovery times. At the optimal operating temperature of 100 °C, Au@WO3 CSNSs showed excellent NO2 selectivity and long-term stability. Notably, the excellent NO2 sensing performance of Au@WO3 CSNSs was slightly affected by humidity. The possible sensing mechanism of the enhanced NO2 sensing properties of the Au@WO3 CSNSs was discussed.

Preparation and Characterization of Fe3O4/CdTe Magnetic/Fluorescent Nanocomposites and Their Applications in Immuno-Labeling and Fluorescent Imaging of Cancer Cells

Abstract: The synthesis of a new kind of magnetic, fluorescent multifunctional nanoparticles (approximately 30 nm in diameter) was demonstrated, where multiple fluorescent CdTe quantum dots (QDs) are covalently linked to and assembled around individual silica-coated superparamagnetic Fe(3)O(4) nanoparticles and active carboxylic groups are presented on the surface for easy bioconjugation with biomolecules. The Fe(3)O(4) nanoparticles were first functionalized with thiol groups, followed by chemical conjugation with multiple thioglycolic acid modified CdTe QDs to form water-soluble Fe(3)O(4)/CdTe magnetic/fluorescent nanocomposites. X-ray diffraction, infrared spectroscopy, transmission electron microscopy, absorption and fluorescence spectroscopy, and magnetometry were applied to fully characterize the multifunctional nanocomposites. The nanocomposites were found to exhibit magnetic and fluorescent properties favorable for their applications in magnetic separation and guiding as well as fluorescent imaging. The carboxyl groups on the nanocomposite surface were proved to be chemically active and readily available for further bioconjugation with biomolecules such as bovine serum albumin and antibodies, enabling the applications of the nanocomposites for specific recognition of biological targets. The Fe(3)O(4)/CdTe magnetic/fluorescent nanocomposites conjugated with anti-CEACAM8 antibody were successfully employed for immuno-labeling and fluorescent imaging of HeLa cells.

Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines

Abstract: Battery state-of-charge (SOC) estimation is essential for a mobile robot, such as inspection of power transmission lines. It is often estimated using a Kalman filter (KF) under the assumption that the statistical properties of the system and measurement errors are known. Otherwise, the SOC estimation error may be large or even divergent. In this paper, without the requirement of the known statistical properties, a SOC estimation method is proposed using an H∞ observer, which can still guarantee the SOC estimation accuracy in the worst statistical error case. Under the conditions of different currents and temperatures, the effectiveness of the proposed method is verified in the laboratory and field environments. With the comparison of the proposed method and the KF-based one, the experimental results show that the proposed method can still provide accurate SOC estimation when there exist inexact or unknown statistical properties of the errors. The proposed method has been applied successfully to the robot for inspecting the running 500-kV extra high voltage power transmission lines.