Shandong Women's University

About: Shandong Women's University is a based out in . It is known for research contribution in the topics: Higher education & The Internet. The organization has 350 authors who have published 323 publications receiving 998 citations.

Phosphorus and oxygen dual-doped graphene as superior anode material for room-temperature potassium-ion batteries

Abstract: The intercalation of potassium ions into graphitic carbon materials has been demonstrated to be feasible while the electrochemical performance of the potassium-ion battery (PIB) is still unsatisfactory. More effort should be made to improve the specific capacity and achieve superior rate capability. Functional phosphorus and oxygen dual-doped graphene (PODG) is introduced as the anode for PIB, made by a thermal annealing method using triphenylphosphine and graphite oxide as precursors. It exhibits high specific capacity and ultra-long cycling stability, delivers a capacity of 474 mA h g−1 at 50 mA g−1 after 50 cycles and retains a capacity of 160 mA h g−1 at 2000 mA g−1 after 600 cycles. The superior electrochemical performance of PODG is mainly due to the large interlayer spacing caused by phosphorus and oxygen dual-doping, which facilitates potassium-ion insertion and extraction. Furthermore, the ultrathin and wrinkled features structure leads to a continuous and efficient supply of vacancies and defects for potassium storage.

How China is fostering sustainable growth: the interplay of green investment and production-based emission.

Abstract: To mitigate environmental problems and to achieve sustainability, China is striving to transition to low-carbon urban economies. Among several significant steps, the country has made remarkable success in controlling the emissions from transportation, buildings, and energy by shutting down or relocating several polluting industries. This study contributes to the issue of sustainable growth debate using time series data from China for the period 1998-2017 and empirically examines the effects of green investment and renewable energy consumption on production-based carbon emissions for China. The strength of this study is that it tested some new variables such as production-based carbon emissions and green investment. Using autoregressive distributed lag model (ARDL) cointegration technique, we found that production-based emission and its determinants move together in the long run. The study found that green investment and renewable energy consumption are both helpful in controlling production-based carbon emissions, while trade openness increases production-based carbon emissions. Hence, green investment and renewable energy consumption contribute to the achievement of sustainable growth. Moreover, based on a robustness check, human capital, financial development, and environment-specific technological innovation are found to be helpful in curbing production-based carbon emissions. Our study recommends financial technology (fin-tech), green investment, and public-private partnership investment in renewable energy to mitigate the effect of production-based carbon emissions.

Sparse network embedding for community detection and sign prediction in signed social networks

Abstract: Network embedding is an important pre-process for analysing large scale information networks. Several network embedding algorithms have been proposed for unsigned social networks. However, these methods cannot be simply migrate to signed social networks which have both positive and negative relationships. In this paper, we present our signed social network embedding model which is based on the word embedding model. To deal with two kinds of links, we define two relationships: neighbour relationship and common neighbour relationship, as well as design a bias random walk procedure. In order to further improve interpretation of the representation vectors, the follow-proximally-regularized-leader online learning algorithm is introduced to the traditional word embedding framework to acquire sparse representations. Extensive experiments were carried out to compare our algorithm with three state-of-the-art methods for community detection and sign prediction tasks. The experimental results demonstrate that our algorithm performs better than the comparison algorithms on most signed social networks.