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).

Surface free-carrier screening effect on the output of a ZnO nanowire nanogenerator and its potential as a self-powered active gas sensor

Abstract: The output of a piezoelectric nanogenerator (NG) fabricated using ZnO nanowire arrays is largely influenced by the density of the surface charge carriers at the nanowire surfaces. Adsorption of gas molecules could modify the surface carrier density through a screening effect, thus, the output of the NG is sensitive to the gas concentration. Based on such a mechanism, we first studied the responses of an unpackaged NG to oxygen, H2S and water vapor, and demonstrated its sensitivity to H2S to a level as low as 100 ppm. Therefore, the piezoelectric signal generated by a ZnO NWs NG can act not only as a power source, but also as a response signal to the gas, demonstrating a possible approach as a self-powered active gas sensor.

NIR-Responsive Silica-Coated NaYbF4:Er/Tm/Ho Upconversion Fluorescent Nanoparticles with Tunable Emission Colors and Their Applications in Immunolabeling and Fluorescent Imaging of Cancer Cells

Abstract: NaYbF4:RE upconversion (UC) fluorescent nanoparticles (NPs) were synthesized with variable rare-earth dopants (RE = Er3+, Tm3+, or Ho3+, or a combination of these ions) from rare-earth stearate precursors in a water−ethanol−oleic acid system by using a two-phase solvothermal method. The NPs were shown to emit visible light, such as orange, yellow, green, cyan, blue or pink light in response to near-infrared (NIR) irradiation, and their emission colors could be simply tuned by changing either the codopant concentration or the dopant species. The UC NPs were well-dispersed and spherical with an average size of 15−35 nm. They emitted strong UC fluorescence under the 980 nm NIR excitation. The effects of solvothermal reaction time and temperature on nanoparticle size and phase structure as well as UC fluorescence intensity were systematically studied. Water dispersibility was achieved by forming a silica coat on the surface of the UC NPs. After animo functionalization, the silica-coated UC NPs were chemically...

Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Abstract: This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.

Extreme ultraviolet resist materials for sub-7 nm patterning

Abstract: Continuous ongoing development of dense integrated circuits requires significant advancements in nanoscale patterning technology. As a key process in semiconductor high volume manufacturing (HVM), high resolution lithography is crucial in keeping with Moore's law. Currently, lithography technology for the sub-7 nm node and beyond has been actively investigated approaching atomic level patterning. EUV technology is now considered to be a potential alternative to HVM for replacing in some cases ArF immersion technology combined with multi-patterning. Development of innovative resist materials will be required to improve advanced fabrication strategies. In this article, advancements in novel resist materials are reviewed to identify design criteria for establishment of a next generation resist platform. Development strategies and the challenges in next generation resist materials are summarized and discussed.