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| 1 | Microbiologically influenced corrosion behavior of S32654 super austenitic stainless steel in the presence of marine Pseudomonas aeruginosa biofilm显示文摘S32654 super austenitic stainless steel(SASS) is widely used in highly corrosive environments. However,its microbiologically influenced corrosion(MIC) behavior has not been reported yet. In this study, the corrosion behavior of S32654 SASS caused by a corrosive marine bacterium Pseudomonas aeruginosa was investigated using electrochemical measurements and surface analysis techniques. It was found that P. aeruginosa biofilm accelerated the corrosion rate of S325654 SASS, which was demonstrated by a negative shift of the open circuit potential(EOCP), a decrease of polarization resistance and an increase of corrosion current density in the culture medium. The largest pit depth of the coupons exposed in the P.aeruginosa broth for 14 days was 2.83 m, much deeper than that of the control(1.33 m) in the abiotic culture medium. It was likely that the P. aeruginosa biofilm catalyzed the formation of CrO_3, which was detrimental to the passive film, resulting in MIC pitting corrosion. | Huabing Li Chuntian Yang Enze Zhou Chunguang Yang Hao Feng Zhouhua Jiang Dake Xu Tingyue Gu Ke Yang | 2017 | Journal of Materials Science & Technology2017，33，12： | 10 |
| 2 | Automated X-ray recognition of solder bump defects based on ensemble-ELM显示文摘Solder bumps realize the mechanical and electrical interconnection between chips and substrates in surface mount components,such as flip chip, wafer level packaging and three-dimensional integration. With the trend to smaller and lighter electronics,solder bumps decrease in dimension and pitch in order to achieve higher I/O density. Automated and nondestructive defect inspection of solder bumps becomes more difficult. Machine learning is a way to recognize the solder bump defects online and overcome the effect caused by the human eye-fatigue. In this paper, we proposed an automated and nondestructive X-ray recognition method for defect inspection of solder bumps. The X-ray system captured the images of the samples and the solder bump images were segmented from the sample images. Seven features including four geometric features, one texture feature and two frequency-domain features were extracted. The ensemble-ELM was established to recognize the defects intelligently. The results demonstrated the high recognition rate compared to the single-ELM. Therefore, this method has high potentiality for automated X-ray recognition of solder bump defects online and reliable. | SU Lei WANG LingYu LI Ke WU JingJing LIAO GuangLan SHI TieLin LIN TingYu | 2019 | Science China(Technological Sciences)2019，62，9： | 5 |
| 3 | Effect of Cu Addition to 2205 Duplex Stainless Steel on the Resistance against Pitting Corrosion by the Pseudomonas aeruginosa Biofilm显示文摘The effect of copper addition to 2205 duplex stainless steel(DSS) on its resistance against pitting corrosion by the Pseudomonas aeruginosa biofilm was investigated using electrochemical and surface analysis techniques. Cu addition decreased the general corrosion resistance, resulting in a higher general corrosion rate in the sterile medium. Because DSS usually has a very small general corrosion rate, its pitting corrosion resistance is far more important. In this work, it was shown that 2205-3%Cu DSS exhibited a much higher pitting corrosion resistance against the P. aeruginosa biofilm compared with the 2205 DSS control, characterized by no significant change in the pitting potential and critical pitting temperature(CPT) values. The strong pitting resistance ability of 2205-3%Cu DSS could be attributed to the copper-rich phases on the surface and the release of copper ions, providing a strong antibacterial ability that inhibited the attachment and growth of the corrosive P. aeruginosa biofilm. | Ping Li Yang Zhao Yuzhi Liu Ying Zhao Dake Xu Chunguang Yang Tao Zhang Tingyue Gu Ke Yang | 2017 | Journal of Materials Science & Technology2017，33，7： | 5 |
| 4 | Human embryonic stem cells-derived endothelial cell therapy facilitates kidney regeneration by stimulating renal resident stem cell proliferation in acute kidney injury显示文摘Endothelial cell therapy has been implicated to enhance tissue regeneration and vascularization in ischemic kidney. However, no published study has yet examined direct effects of endothelial cell treatment in kidney recovery. This study investigated the therapeutic efficacy of endothelial cells in a mouse model with acute kidney injury (AKI). Thus, human embryonic stem cells-derived endothelial cells (hESC-ECs) labeled with a reporter system encoding a double fusion reporter gene for firefly luciferase (Fluc) and green fluorescent protein (GFP) were characterized by Fluc imaging and immunofluoresence staining. Cultured hESC-ECs (1×106) were injected into ischemic kidney shortly after AKI. Survival of the transplanted hESC-ECs was monitored in vivo from day 1 to 14 after endothelial cell transplantation and potential impact of hESC-EC treatment on renal regeneration was assessed by histological analyses. We report that a substantial level of bioluminescence activity was detected 24 h after hESC-EC injection followed by a gradual decline from 1 to 14 d. Human ESC-ECs markedly accelerated kidney cell proliferation in response to ischaemia-induced damage, indicated by an elevated number of BrdU+ cells. Co-expression of Sca-1, a kidney stem cell proliferation marker, and BrdU further suggested that the observed stimulation in renal cell regeneration was, at least in part, due to increased proliferation of renal resident stem cells especially within the medullary cords and arteriole. Differentiation of hESC-ECs to smooth muscle cells was also observed at an early stage of kidney recovery. In summary, our results suggest that endothelial cell therapy facilitates kidney recovery by promoting vascularization, trans-differentiation and endogenous renal stem cell proliferation in AKI. | JIA XiaoHua L He LI Chen FENG GuoWei YAO XinPeng MAO LiNa KE TingYu CHE YongZhe XU Yong LI ZongJin KONG DeLing | 2013 | Chinese Science Bulletin2013，58，23： | 4 |
| 5 | Biofilm inhibition and corrosion resistance of 2205-Cu duplex stainless steel against acid producing bacterium Acetobacter aceti显示文摘Acid producing bacterium Acetobacter aceti causes pitting corrosion of stainless steel(SS).This work investigated the enhanced resistance of 2205-Cu duplex stainless steel(DSS)against biocorrosion by A.aceti in comparison with 2205 DSS using electrochemical and surface analysis techniques.With the addition of Cu to 2205 DSS,biofilms on the 2205-Cu DSS surface were inhibited effectively.The largest pit depth on 2205-Cu DSS surface in the presence of A aceti was 2.6μm,smaller than 5.5μm for 2205 DSS surface.The i(corr)was 0.42±0.03μA cm^-2 for 2205-Cu DSS in the biotic medium,which was much lower than that for 2205 DSS(3.69±0.65μA cm^-2).All the results indicated that the A aceti biofilm was considerably inhibited by the release of Cu^2+ions from the 2205-Cu DSS matrix,resulting in the mitigation of biocorrosion by A aceti. | Dan Liu Ru Jia Dake Xu Hongying Yang Ying Zhao Msaleem Khan Songtao Huang Jiankang Wen Ke Yang Tingyue Gug | 2019 | Journal of Materials Science & Technology2019，35，11： | 0 |
| 6 | The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin[version 1;peer review:1 approved with reservations,1 not approved]显示文摘The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently.As a key part of the shop-floor,humans'high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior.Therefore,the modeling system for cross-scale human behavior in digital twin shop-floors was developed,powered by the data fusion of macro-behavior and micro-behavior virtual models.Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position.Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation.In this study,we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors.Based on this theoretical system,we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling.Lastly,we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors.Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors. | Tingyu Liu Mengming Xia Qing Hong Yifeng Sun Pei Zhang Liang Fu Ke Chen | 2021 | Digital Twin2021，1，1： | 0 |
| 7 | miR-204 ameliorates osteoarthritis pain by inhibiting SP1-LRP1 signaling and blocking neuro-cartilage interaction显示文摘Osteoarthritis(OA)is a painful degenerative joint disease and is the leading cause of chronic disability among elderly individuals.To improve the quality of life for patients with OA,the primary goal for OA treatment is to relieve the pain.During OA progression,nerve ingrowth was observed in synovial tissue and articular cartilage.These abnormal neonatal nerves act as nociceptors to detect OA pain signals.The molecular mechanisms for transmitting OA pain in the joint tissues to the central nerve system(CNS)is currently unknown.MicroRNA miR-204 has been demonstrated to maintain the homeostasis of joint tissues and have chondro-protective effect on OA pathogenesis.However,the role of miR-204 in OA pain has not been determined.In this study,we investigated interactions between chondrocytes and neural cells and evaluated the effect and mechanism of miR-204 delivered by exosome in the treatment of OA pain in an experimental OA mouse model.Our findings demonstrated that miR-204 could protect OA pain by inhibition of SP1-LDL Receptor Related Protein 1(LRP1)signaling and blocking neuro-cartilage interaction in the joint.Our studies defined novel molecular targets for the treatment of OA pain. | Ke Lu Qingyun Wang Liuzhi Hao Guizheng Wei Tingyu Wang William WLu Guozhi Xiao Liping Tong Xiaoli Zhao Di Chen | 2023 | Bioactive Materials2023，，8： | 0 |
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