Heteroatoms in situ-doped hierarchical porous hollow-activated carbons for high-performance supercapacitor

Yan Rui,Wang Kai,Tian Xiaodong,Li Xiao,Yang Tao,Xu Xiaotong,He Yiting,Lei Shiwen,Song Yan 한국탄소학회 2020 Carbon Letters Vol.30 No.3

Heteroatoms in situ-doped hierarchical porous hollow-activated carbons (HPHACs) have been prepared innovatively by pyrolyzation of setaria viridis combined with alkaline activation for the frst time. The micro-morphology, pore structure, chemical compositions, and electrochemical properties are researched in detail. The obtained HPHACs are served as outstand�ing electrode materials in electrochemical energy storage ascribe to the particular hierarchical porous and hollow structure, and the precursor setaria viridis is advantage of eco-friendly as well as cost-efective. Electrochemical measurement results of the HPHACs electrodes exhibit not only high specifc capacitance of 350 F g−1 at 0.2 A g−1, and impressive surface specifc capacitance (Cs) of 49.9 μF cm−2, but also substantial rate capability of 68% retention (238 F g−1 at 10 A g−1) and good cycle stability with 99% retention over 5000 cycles at 5 A g−1 in 6 M KOH. Besides, the symmetrical supercapacitor device based on the HPHACs electrodes exhibits excellent energy density of 49.5 Wh kg−1 at power density of 175 W kg−1 , but still maintains favorable energy density of 32.0 Wh kg−1 at current density of 1 A g−1 in 1-ethy-3-methylimidazolium tetrafuoroborate (EMIMBF4) ionic liquid electrolyte, and the excellent cycle stability behaviour shows the nearly 97% ratio capacitance retention of the initial capacitance after 10,000 cycles at current density of 2 A g−1. Overall, the results indicate that HPHACs derived from setaria viridis have appealing electrochemical performances thus are promising electrode materi�als for supercapacitor devices and large-scale applications.

Evaluation of the Efficacy of a Lactobacilli-Based Teat Detergents for the Microbiota of Cows Teats Using an Untargeted Metabolomics Approach

Yan Rui,Ji Zhongqing,Fan Jiaqi,Li Jiang,Ren Yan 한국미생물·생명공학회 2024 Journal of microbiology and biotechnology Vol.34 No.1

Teat cleaning pre- and post-milking is important for the overall health and hygiene of dairy cows. The purpose of this research was to evaluate the effectiveness of a teat detergents based on lactic acid bacteria according to changes in somatic cell count and cow-milk metabolites. Sixty-nine raw milk samples were collected from 11 Holstein-Friesian cows in China during 12 days of teat cleaning. An ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry-based untargeted metabolomic approach was applied to detect metabolomic differences after treatment with lactic acid bacteria and chemical teat detergents in cows with subclinical mastitis. The results suggest that the lactobacilli-based teat detergents could reduce somatic cell count and improve microhabitat of cow teat apex by adjusting the composition of metabolites. Furthermore, the somatic cell count could be decreased significantly within 10 days following the cleaning protocol. Lactic acid bacteria have the potential to be applied as a substitution to teat chemical detergents before and after milking for maintenance of healthy teats and breasts. Further, larger scale validation work is required to support the findings of the current study.

Patent citations and knowledge spillovers: an analysis of Chinese patents registered in the USA

Fei Yu,Yanrui Wu 기술경영경제학회 2014 ASIAN JOURNAL OF TECHNOLOGY INNOVATION Vol.22 No.1

This paper examines US patent citation data and analyses how different firms in China affect knowledge spillovers. Patents granted by the US patent office to applicants located in China are collected along with their citation counts. Two kinds of patent citations, namely citations of previous patents and those of non-patent literature, are used to measure knowledge flows. In the empirical analysis, the negative binomial and zero-inflated count models are considered. The regression results suggest the existence of heterogeneity among firms of different ownership. In terms of knowledge spillovers, the US multinational corporations perform better than those from other western countries; Taiwanese companies outperform their counterparts from Hong Kong and Chinese private corporations contribute more than Chinese state-owned enterprises. These results have important policy implications for the development of a knowledge-intensive economy in China.

The study of Factors Influencing Continuous Usage Intention using Augmented Reality Games : Comparative Analysis of Korean and Chinese Users

Namjae, Cho,YanRui, Wang,Jeong Hun, Lim,Giseob, Yu 한국경영정보학회 2021 한국경영정보학회 학술대회논문집 Vol.2021 No.11

This study is to focuses on a user’s attitude toward augmented reality games. Based on the Technology Acceptance Model (TAM) and Flow theory, continuous usage intention was set as a dependent variable, and immersion was set as a mediating variable. As independent variables, spatial presence, perceived interactivity, perceived pleasure, and sickness were set. Besides, this study strived to compare and analyze Korean and Chinese Data. The results of this study were as follows. First of all, Korean users had a significant effect on spatial presence, perceived pleasure, and immersion. Spatial presence and perceived pleasure had also mediated effects on continuous usage intention through immersion. However, perceived interactivity and sickness did not affect immersion and even no mediating effect to continuous usage intention. In the case of Chinese users, spatial presence, perceived interactivity, and perceived pleasure were grouped into one variable. This variable influenced immersion and also had mediated effect on continuous usage intention. However, sickness, like Korean users, did not affect the set path. This study had implications to analyze user perspective using immersion, which was found a significant variable in previous research. In addition, this study found similarities and characteristics through a comparative analysis between Korean and Chinese users indirectly.

Purification and Characterization of Novel Manganese Peroxidase from Rhizoctonia sp. SYBC-M3

Yujie Cai,Huiguang Wu,Xiangru Liao,Yanrui Ding,Jun Sun,Dabing Zhang 한국생물공학회 2010 Biotechnology and Bioprocess Engineering Vol.15 No.6

A novel manganese peroxidase of Rhizoctonia sp. SYBC-M3 (R-MnP) was purified by (NH4)2SO4fractionation, DEAE-cellulose-32 column chromatography,and Sephadex G100 column chromatography. The molecular mass of R-MnP was determined to be approximately 40.4 kDa by SDS-PAGE. The optimum temperature and pH for R-MnP were 55°C and 4.5, respectively. R-MnP was highly stability when the temperature was below 50°C. R-MnP could retain about 60% of its activity when the pH was between 4 and 6.5. However, R-MnP activity was inhibited by Fe3+, Cu2+, and Co3+ as well as increased by Zn2+ and Ca2+. R-MnP demonstrated oxidation of DMP,ABTS, veratryl alcohol, and guaiacol. The Km values of RMnP for H2O2 and Mn2+ were 25.3 and 53.9 μmol/L,respectively.

RCLSTMNet: A Residual-convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine

Chengjin Qin,Gang Shi,Jianfeng Tao,Honggan Yu,Yanrui Jin,Dengyu Xiao,Chengliang Liu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.2

During tunneling process, it is of critical importance to dynamically adjust operation parameters of shield machine due to changes of geological conditions. Cutterhead torque is one of the key load parameters, and its accurate prediction could adjust operational parameters including cutterhead rotational speed and tunneling speed in advance and avoid potential cutterhead jamming. Based on operation and state data collected by the monitoring system, we propose a residual-convolutional-LSTM neural network (RCLSTMNet) for forecasting cutter head torque in shield machine. On the basis of correlation analysis, parameters closely related to cutter head torque are selected as inputs by employing cosine similarity, which significantly reduces input dimension. Convolutional-LSTM neural network is fused and constructed for extracting deep useful features, while residual network module is utilized to avoid gradient disappearing and improve regression performance. Comparisons with recent data-driven cutterhead torque prediction methods are made on the actual engineering datasets, which demonstrate the presented RCLSTMNet outperforms the other data driven models in most cases. Moreover, the predicted curves of cutterhead torque using the proposed RCLSTMNet coincide with the actual curves much better than predicted curves using the other models. Meanwhile, the highest and average accuracy of RCLSTMNnet reach 98.1% and 95.6%, respectively.