4,218 publications from this institution
Mass-manufactured stretchable negative Poisson's ratio yarn TENG as a fundamental material for environmental energy harvesting and self-powered sensors.
Abstract Oil–water emulsions are a considerable hazard to the environment, ecology, and human health, if not appropriately treated. This study proposes a self‐powered and efficient triboelectric dehydrator (TED) based on a wind‐driven freestanding rotary triboelectric nanogenerator (FR‐TENG) to separate water‐in‐oil emulsions. This TED can form a high‐voltage electric field in the emulsion when the FR‐TENG is driven by mechanical energy. The dehydration performance of the TED is analyzed in detail through multiphysics‐coupled models and experiments. It is found that the TED can dehydrate water‐in‐oil emulsions with a wide range of initial moisture contents. In particular, even when the initial moisture content is 60%, which is near the phase inversion concentration of the emulsion, the dehydration rate of the TED can still reach 99.41%. In addition, the performance of TED is demonstrated in a simulated situation of wind, suggesting that the present TED has great potential application for separating oil–water emulsions by harvesting environmental energy.
A simple model of charge transfer by loss-less quantum-mechanical tunneling between two solids is proposed. The model is applicable to electron transport and contact electrification between e.g. a metal and a dielectric solid. Based on a one-dimensional effective-mass Hamiltonian, the tunneling transmission coefficient of electrons through a barrier from one solid to another solid is calculated analytically. The transport rate (current) of electrons is found using the Tsu-Esaki equation and accounting for different Fermi functions of the two solids. We show that the tunneling dynamics is very sensitive to the vacuum potential versus the two solids conduction-band edges and the thickness of the vacuum gap. The relevant time constants for tunneling and contact electrification, relevant for triboelectricity, can vary over several orders of magnitude when the vacuum gap changes by one order of magnitude, say, 1 Å to 10 Å. Coulomb repulsion between electrons on the left and right material surfaces is accounted for in the tunneling dynamics.
A male Morpho peleides butterfly wing is decorated by two types of scales, cover and ground scales. We have studied the optical properties of each type of scales in conjunction with the structural information provided by cross-sectional transmission electron microscopy and computer simulation. The shining blue color is mainly from the Bragg reflection of the one-dimensional photonic structure, e.g., the shelf structure packed regularly in each ridges on cover scales. A thin-film-like interference effect from the base plate of the cover scale enhances such blue color and further gives extra reflection peaks in the infrared and ultraviolet regions. The analogy in the spectra acquired from the original wing and that from the cover scales suggests that the cover scales take a dominant role in its structural color. This study provides insight of using the biotemplates for fabricating smart photonic structures.
Optical neural networks are hardware neural networks implemented based on physical optics, and they have demonstrated advantages of high speed, low energy consumption, and resistance to electromagnetic interference in the field of image processing. However, most previous optical neural networks were designed for coherent light inputs, which required the introduction of an electro-optical conversion module before the optical computing device. This significantly hindered the inherent speed and energy efficiency advantages of optical computing. In this paper, we propose a diffraction algorithm for incoherent light based on mutual intensity propagation, and on this basis, we established a model of an incoherent optical neural network. This model is completely passive and directly performs inference calculations on natural light, with the detector directly outputting the results, achieving target classification in an all-optical environment. The proposed model was tested on the MNIST, Fashion-MNIST, and ISDD datasets, achieving classification accuracies of 82.32%, 72.48%, and 93.05%, respectively, with experimental verification showing an accuracy error of less than 5%. This neural network can achieve passive and delay-free inference in a natural light environment, completing target classification and showing good application prospects in the field of remote sensing.
Objective: The paper analyzes the medical undergraduates' influencing factors for solving the employment problem in primary healthcare institutions. Methods: 304 Clinical medicine medical undergraduates were surveyed by a self-designed questionnaire. The influencing factors of medical undergraduates' employment intentions were analyzed by a Chi-square test and binary logistic regression method. Results: The main reasons for willing to work in primary healthcare institutions are "to accumulate primary experience" "to respond to the national call and serve the society" "to pave the way for future work" and "to make contributions for their hometown". The main reasons for not willing to is "small room for improvement", "not optimistic development prospects" "slow economic development and poor working conditions" "insufficient policy protection and difficult re- employment" and " not high salary". Conclusion: Improve the support policy so that the talent is "willing to work in primary healthcare institutions". Provide a platform to make sure that people are "willing to be at primary healthcare institutions".
The Inertial Measurement Unit/Global Positioning System/BeiDou Navigation Satellite System (IMU/GPS/BDS) tightly coupled integrated navigation system and navigational positioning system uses observation information based on the code pseudorange. The system can effectively improve the accuracy of the pseudorange-observed quantity based on the carrier-phase smoothed pseudorange, thereby improving the accuracy of navigation and positioning. However, the measurement noise after pseudorange smoothing does not conform with the characteristics of white noise, which causes the Kalman filter to easily diverge. At the same time, the stability of the filter is more seriously affected due to the presence of cycle slips. In connection with the aforementioned problems, the characteristics of the smoothed pseudorange noise is analyzed and a noise model is established in this paper. On this basis, a robust adaptive filtering algorithm is designed to carry out online, real-time estimation and compensation on the measurement noise, which, in combination with the robust estimation theory, carries out filtering to reduce the effects on the filter brought by the level of measurement noise and the uncertainties of the model. Theoretical analysis and simulation results show that, in a complex environment, the robust adaptive tightly coupled integrated navigation system based on a carrier-phase smoothed pseudorange has higher positioning accuracy.
