Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Anisotropic thermally conductive flexible films based on nanofibrillated cellulose and aligned graphene nanosheets

Abstract: The anisotropic thermally conductive flexible films with nanofibrillated cellulose (NFC) and in situ reduced graphene oxide (RGO) nanosheets are prepared via a vacuum-assisted self-assembly technique. The hybrid films exhibit superior in-plane thermal conductivities, and a value of 6.168 W m−1 K−1 is achieved with 30 wt% GO loading. Noteworthily, the through-plane thermal conductivities are extremely low (≤0.072 W m−1 K−1). The high anisotropy of the thermal conductivity is systematically investigated and correlated to the alignment of RGO nanosheets. Moreover, hybrid films exhibit excellent flexibility as well as high tensile strength. The aligned RGO nanosheets significantly delayed the thermal degradation of the hybrid films. The strongly anisotropic thermally conductive properties of these films can be useful for their application in thermal management.

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Abstract: The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to $30\%$ compared with the existing upper confidence bound based learning algorithm.

Upconversion Nanoprobes: Recent Advances in Sensing Applications.

Abstract: Rare earth ions doped upconversion nanoparticles (UCNPs) are capable of stepwisely converting two or more lower energy near-infrared (NIR) excitation photons into a higher energy visible and ultraviolet emission photon, resulting in a large anti-Stokes shift of several hundred nanometers. Because the f-electron configurations of lanthanide ions have abundant energy levels, and many of them possess long lifetimes, UCNPs exhibit unique optical properties including optical tunability over emission wavelengths and lifetimes, better photo-stability and improved monochromatic color-purity and spatial resolution. However, the low luminescent efficiency, largely owing to their substantially weak and narrow band absorptions, has greatly hampered the translation of UCNPs-based technologies from the academic stage to real world products. Recently, increasing efforts have been paid on boosting the upconversion luminescence brightness and efficiency, including inert shells coating, high concentration of activators dop...