|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | High mass loading Ni_(4)Co_(1)-OH@CuO core-shell nanowire arrays obtained by electrochemical reconstruction for alkaline energy storage显示文摘The design of three-dimensional(30)core-shell hetercistructures is an efficient method to achieve high mass specific capacity of electroactive materials under high mass loading.In this work,porous Ni_(4)Co1-0H nanosheets with a mass loading of 7.7 mg·cm^(-2) are obtained by using Ni_(4)Cor(NO_(3))_(2)(0H)_(4) supported on the CuO nanowires as precursors via an unavoidable electrochemically induced phase reconstruction.During the electrochemical reconstruction process,the N03-anions in Ni_(4)Cor(N0_(3))_(2)(0H)_(4) are easily replaced by OH-anions in the electrolyte.The phase reconstruction is accompanied by the decrease of ionic diffusion.:resistance and the increase of pore volume,and the shift of binding energy.The obtained Ni4Co1-0H nanosheets show a high:mass specific capacity of 363.6 mAh·g^(-1) at 5 mA·cm^(-2).The as-fabricated alkaline hybrid supercapacitor and Ni-Zn battery deliver high energy density of 293.1 and 604.9 Wh·kg^(-1),respectively,indicating.excellent alkaline energy storage performance. | Yawei Yu Xiulan Hu Shuai Wang Handan Qiao Zeyu Liu Kefan Song Xiaodong Shen | 2022 | Nano Research2022，15，1： | 1 |
| 2 | An Early Warning Model of Telecommunication Network Fraud Based on User Portrait显示文摘With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value. | Wen Deng Guangjun Liang Chenfei Yu Kefan Yao Chengrui Wang Xuan Zhang | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 3 | Tribological characteristics of dimpled surfaces filled with dopamine-modified MoS_(2)显示文摘To improve the tribological characteristics of dimples on the surface of 45 steel,the dimples were filled with MoS_(2)and MoS_(2)modified by dopamine(MoS_(2)@DA),and ball-disk friction and wear tests were conducted.Specifically,the dimple filling gap,abrasion depth,and surface cross-sectional area of 45 steel were measured.The wear morphology of the friction ball and exfoliation of MoS_(2)in the dimples and the bending characteristics of the specimens were studied.The surface friction coefficient of MoS_(2)@DA-filled specimen was 17.9%lower than MoS_(2)-filled specimen,and the dimple filling gap was 70.1%lower,the surface abrasion depth was 5.8%lower,and the abrasion cross-sectional area was 17.7%smaller.Moreover,the bending strength of the MoS_(2)@DA specimen was 3.27 times greater than that of the MoS_(2)specimen,and the exfoliation of MoS_(2)was slowed by filling with the MoS_(2)@DA.Finally,the tribological characteristics were also superior for the specimens prepared with MoS_(2)@DA. | Tuo Qin Aibing Yu Shuo Zhao Kefan Li Shaochun Qi Jiawang Ye | 2023 | Biosurface and Biotribology2023，9，4： | 0 |
| 4 | Computational fluid dynamics modeling of rapid pyrolysis of solid waste magnesium nitrate hydrate under different injection methods显示文摘This study developed a numerical model to efficiently treat solid waste magnesium nitrate hydrate through multi-step chemical reactions.The model simulates two-phase flow,heat,and mass transfer processes in a pyrolysis furnace to improve the decomposition rate of magnesium nitrate.The performance of multi-nozzle and single-nozzle injection methods was evaluated,and the effects of primary and secondary nozzle flow ratios,velocity ratios,and secondary nozzle inclination angles on the decomposition rate were investigated.Results indicate that multi-nozzle injection has a higher conversion efficiency and decomposition rate than single-nozzle injection,with a 10.3%higher conversion rate under the design parameters.The decomposition rate is primarily dependent on the average residence time of particles,which can be increased by decreasing flow rate and velocity ratios and increasing the inclination angle of secondary nozzles.The optimal parameters are injection flow ratio of 40%,injection velocity ratio of 0.6,and secondary nozzle inclination of 30°,corresponding to a maximum decomposition rate of 99.33%. | Wenchang Wu Kefan Yu Liang Zhao Hui Dong | 2024 | Chinese Journal of Chemical Engineering2024，66，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员