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1,623 results on '"Yao, Hui"'

8. Mine water inflow prediction based on DRN-BiLSTM model

Author

LIANG Manyu, YIN Shangxian, YAO Hui, XIA Xiangxue, XU Bin, LI Shuqian, and ZHANG Gaizhuo

Subjects
mine water inflow, drn-bilstm model, deep residual network (drn), bidirectional short and long memory network(bilstm), wavelet transform, Mining engineering. Metallurgy, TN1-997
Abstract

For the problem of low accuracy and applicability of the model prediction in the study of mine water inflow, a method of mine water inflow prediction based on bidirectional short and long memory network(BiLSTM) and deep residual network (DRN) is proposed. First, the data of mine water inflow is processed by wavelet decomposition and normalization to obtain trend item data and detail item data. Secondly, the trend item data was predicted by DRN network method, and the detail item data was predicted by BiLSTM network method. Finally, the two parts of the prediction results will be combined to get the mine water inflow prediction results. The results show that the DRN-BiLSTM model has higher prediction accuracy than a single model, indicating that the model has better generalization.

Published
2024
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12. Selective topological valley transport of elastic waves in a Bragg-type phononic crystal plate.

Author

Tan, Mao-Ting, Sun, Xiao-Wei, Liu, Yao-Hui, Gao, Xing-Lin, Hu, Lin-Wei, and Song, Ting

Subjects
PHONONIC crystals, QUANTUM Hall effect, ELASTIC waves, MIRROR symmetry
Abstract

Based on the quantum valley Hall effect analogy, this work proposes a phononic crystal plate with ligament-type beams to obtain the topological valley transmission of elastic waves. A pure Bragg degenerate state appears in the high-frequency region with a resonator introduced. By rotating the central scatterer and the beams, the mirror symmetry is broken to form a topological bandgap. Subsequently, this work finds that two selective edge states also appear beside the commonly non-trivial crossing edge states in the topological bandgap by calculating the projected band and eigenvalue spectrum of the supercell with different valley Hall phases phononic crystals. Their appearance is due to band separation of the topological edge states caused by an increase in the rotation angle. Both selective edge states can transmit topologically in specific paths. They will help further to broaden the width of the frequency band of topological transmission. Besides, an elastic wave splitter is designed and demonstrated numerically, which can form two channels and three channels in different frequency bands. With the topological selective edge state disappearing, a topological corner state exists in the edge bandgap. This work provides a theoretical reference for practical applications of broadband elastic wave topological transmission and elastic energy trapping. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

Author

Xueqiu You, Dongao Zhang, Xia-Guang Zhang, Xiangyu Li, Jing-Hua Tian, Yao-Hui Wang, and Jian-Feng Li

Subjects
In situ Raman, Interfacial water, Hydrogen evolution reaction, Cations, Technology
Abstract

Highlights In situ Raman spectroscopy study of the cation regulation for interfacial water on Au@Pd core–shell nanoparticle surface during hydrogen evolution reaction process. Direct spectral evidence reveals new strategies to optimize the structure of interfacial water by the adjusting of radius and valence of the cation in the electrolyte. The optimized structure of interfacial water will improve the charge transfer efficiency between the water and electrode increasing the performance of the hydrogen evolution reaction.

Published
2023
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20. Deep learning classification for macrophage subtypes through cell migratory pattern analysis

Author

Manasa Kesapragada, Yao-Hui Sun, Ksenia Zlobina, Cynthia Recendez, Daniel Fregoso, Hsin-Ya Yang, Elham Aslankoohi, Rivkah Isseroff, Marco Rolandi, Min Zhao, and Marcella Gomez

Subjects
correlation between cell shape and trajectories, macrophage polarization, classification of macrophage subtypes using migration patterns, deep learning for classification of macrophage subtypes, analysis of macrophage trajectory patterns, Biology (General), QH301-705.5
Abstract

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

Published
2024
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22. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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30. Loss of LPAR6 and CAB39L dysregulates the basal-to-luminal urothelial differentiation program, contributing to bladder carcinogenesis

Author

Lee, Sangkyou, Bondaruk, Jolanta, Wang, Yishan, Chen, Huiqin, Lee, June Goo, Majewski, Tadeusz, Mullen, Rachel D., Cogdell, David, Chen, Jiansong, Wang, Ziqiao, Yao, Hui, Kus, Pawel, Jeong, Joon, Lee, Ilkyun, Choi, Woonyoung, Navai, Neema, Guo, Charles, Dinney, Colin, Baggerly, Keith, Mendelsohn, Cathy, McConkey, David, Behringer, Richard R., Kimmel, Marek, Wei, Peng, and Czerniak, Bogdan

Published
2024
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38. Report on a case of liver-originating malignant melanoma of unknown primary

Author

Yuan Zheng, Guo Hai-yan, Lu Wei-ting, Wang Yao-hui, He Jing, Zhang Fan, Che Jun-yong, and Qiao Fei

Subjects
diagnosis, liver, malignant melanoma, unknown primary, Biology (General), QH301-705.5
Abstract

Malignant melanoma (MM) frequently occurs in the skin or mucosa, whereas malignant melanoma of unknown primary (MUP) is diagnosed in patients with lymph nodes or visceral organs as the site of origin, where it is challenging to detect the primary lesion by comprehensive examination. MUP is possibly related to the spontaneous regression of the primary lesion. In addition, primary hepatic melanoma (PHM) usually refers to the primary MM occurring in the liver, with no typical primary lesions and no manifestations of tumor metastasis. A 61-year-old male patient with liver as the site of origin was diagnosed with MM by Melan-A, HMB-45, and S-100 immunohistochemistry staining of liver biopsy tissue. Based on a comprehensive examination, no basis was found for melanoma in sites such as the skin, mucosa, five sense organs, brain, digestive tract, respiratory tract, or genitalia, and the patient was subsequently diagnosed with MUP. MMs require a comprehensive inspection, beginning with the liver, to search for the primary lesion; if the primary lesion is not found, the possibility of PHM or MUP should be considered.

Published
2023
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39. Revealing the role of interfacial water and key intermediates at ruthenium surfaces in the alkaline hydrogen evolution reaction

Author

Xing Chen, Xiao-Ting Wang, Jia-Bo Le, Shu-Min Li, Xue Wang, Yu-Jin Zhang, Petar Radjenovic, Yu Zhao, Yao-Hui Wang, Xiu-Mei Lin, Jin-Chao Dong, and Jian-Feng Li

Subjects
Science
Abstract

Abstract Ruthenium exhibits comparable or even better alkaline hydrogen evolution reaction activity than platinum, however, the mechanistic aspects are yet to be settled, which are elucidated by combining in situ Raman spectroscopy and theoretical calculations herein. We simultaneously capture dynamic spectral evidence of Ru surfaces, interfacial water, *H and *OH intermediates. Ru surfaces exist in different valence states in the reaction potential range, dissociating interfacial water differently and generating two distinct *H, resulting in different activities. The local cation tuning effect of hydrated Na+ ion water and the large work function of high-valence Ru(n+) surfaces promote interfacial water dissociation. Moreover, compared to low-valence Ru(0) surfaces, high-valence Ru(n+) surfaces have more moderate adsorption energies for interfacial water, *H, and *OH. They, therefore, facilitate the activity. Our findings demonstrate the regulation of valence state on interfacial water, intermediates, and finally the catalytic activity, which provide guidelines for the rational design of high-efficiency catalysts.

Published
2023
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40. Recovery of Lithium from Industrial Li-Containing Wastewater Using Fluidized-Bed Homogeneous Granulation Technology

Author

Van Giang Le, The Anh Luu, Huu Tuan Tran, Ngoc T. Bui, M. Mofijur, Minh Ky Nguyen, Xuan Thanh Bui, M. B. Bahari, Hoang Nhat Phong Vo, Chi Thanh Vu, Guo-Ping Chang Chien, and Yao-Hui Huang

Subjects
fluidized bed homogeneous granulation, industriallithium-containing wastewater, green and sustainable recycling, lithium recovery, lithium phosphate pellets, Mineralogy, QE351-399.2
Abstract

In this study, a novel fluidized-bed homogeneous granulation (FBHo-G) process was developed to recover lithium (Li) from industrial Li-impacted wastewater. Five important operational variables (i.e., temperatures, pH, [P]0/[Li]0 molar ratios, surface loadings, and up-flow velocities (Umf)) were selected to optimize the Li recovery (TR%) and granulation ratio (GR%) efficiencies of the process. The optimal operational conditions were determined as the following: a temperature of 75 °C, pH of 11.5, [P]0/[Li]0 of 0.5, surface loading of 2.5 kg/m2·h, and Umf of 35.7 m/h). The TR% and GR% at optimal condition could be as much as 90%. The material characterization of the recovery pellet products showed that they were highly crystallized Li3PO4 (purity ~88.2%). The pellets had a round shape and smooth surface with an average size of 0.65 mm, so could easily be stored and transported. The high purity enables them to be further directly reused as raw materials for a wide range of industrial applications (e.g., in the synthesis of cathode materials). Our calculation shows that the FBHo-G process could recover up to 0.1845 kg of lithium per cubic meter of Li-containing wastewater, at a recovery rate of ~90%. A brief technoeconomic analysis shows that FBHG process had economic viability, with an estimate production cost of USD 26/kg Li removed, while the potential gained profit for selling lithium phosphate pellets could be up to USD 48 per the same volume of wastewater and the net profit up to USD 22/m3 Li treated. In all, fluidized-bed homogeneous granulation, a seedless one-step recovery process, opens a promising pathway toward a green and sustainable recycling industry for the recovery and application of the resource-limited lithium element from nonconventional water sources.

Published
2024
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49. A bioelectronic device for electric field treatment of wounds reduces inflammation in an in vivo mouse model.

Author

Cristian O Hernandez, Hao-Chieh Hsieh, Kan Zhu, Houpu Li, Hsin-Ya Yang, Cynthia Recendez, Narges Asefifeyzabadi, Tiffany Nguyen, Maryam Tebyani, Prabhat Baniya, Andrea Medina Lopez, Moyasar A Alhamo, Anthony Gallegos, Cathleen Hsieh, Alexie Barbee, Jonathan Orozco, Athena M Soulika, Yao-Hui Sun, Elham Aslankoohi, Mircea Teodorescu, Marcella Gomez, Narges Norouzi, Roslyn Rivkah Isseroff, Min Zhao, and Marco Rolandi

Subjects
Medicine, Science
Abstract

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Published
2024
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50. The sinoatrial node extracellular matrix promotes pacemaker phenotype and protects automaticity in engineered heart tissues from cyclic strain

Author

Yao-Hui Sun, Hillary K.J. Kao, Phung N. Thai, Regan Smithers, Che-Wei Chang, Dalyir Pretto, Sergey Yechikov, Sarah Oppenheimer, Amanda Bedolla, Brooke A. Chalker, Rana Ghobashy, Jan A. Nolta, James W. Chan, Nipavan Chiamvimonvat, and Deborah K. Lieu

Subjects
CP: Cell biology, CP: Developmental biology, Biology (General), QH301-705.5
Abstract

Summary: The composite material-like extracellular matrix (ECM) in the sinoatrial node (SAN) supports the native pacemaking cardiomyocytes (PCMs). To test the roles of SAN ECM in the PCM phenotype and function, we engineered reconstructed-SAN heart tissues (rSANHTs) by recellularizing porcine SAN ECMs with hiPSC-derived PCMs. The hiPSC-PCMs in rSANHTs self-organized into clusters resembling the native SAN and displayed higher expression of pacemaker-specific genes and a faster automaticity compared with PCMs in reconstructed-left ventricular heart tissues (rLVHTs). To test the protective nature of SAN ECMs under strain, rSANHTs and rLVHTs were transplanted onto the murine thoracic diaphragm to undergo constant cyclic strain. All strained-rSANHTs preserved automaticity, whereas 66% of strained-rLVHTs lost their automaticity. In contrast to the strained-rLVHTs, PCMs in strained-rSANHTs maintained high expression of key pacemaker genes (HCN4, TBX3, and TBX18). These findings highlight the promotive and protective roles of the composite SAN ECM and provide valuable insights for pacemaking tissue engineering.

Published
2023
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