Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Zewei Ren"'
Autor:
Allison B. Anbari, Knoo Lee, Jennifer M. Hulett, Lada Micheas, Zewei Ren, Anthony Belenchia, Suhwon Lee
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background There are more than 4 million breast cancer survivors in the United States. With continuing improvements in early detection and treatment, the number of breast cancer survivors will only continue to increase. Breast cancer survivo
Externí odkaz:
https://doaj.org/article/d23175c2a8a944dd97d46395d304786c
Publikováno v:
Micromachines, Vol 15, Iss 5, p 645 (2024)
The widespread vibration is one of the most promising energy sources for IoT and small sensors, and broad-frequency vibration energy harvesting is important. Triboelectric nanogenerators (TENGs) can convert vibration energy into electrical energy thr
Externí odkaz:
https://doaj.org/article/e46941cb19c548e9a33d41f20d635918
Autor:
Ru Chen, Zewei Ren, Zhaohui Meng, Honglei Zhan, Xinyang Miao, Kun Zhao, Huibin Lű, Kuijuan Jin, Shijie Hao, Wenzheng Yue, Guozhen Yang
Publikováno v:
Energy Reports, Vol 6, Iss , Pp 795-801 (2020)
Deep learning methodologies have revolutionized prediction in many fields and is potential to do the same in the petroleum industry because of the complex oil–gas reservoir. A limitation remains for dense shale exploration in that the shales with i
Externí odkaz:
https://doaj.org/article/e2f89cb2769f428885a4690217be45de
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
Triboelectric nanogenerators harvest energy by contacting two solids or a liquid and a solid. Here the authors use a conductive liquid membrane as a permeable electrode to demonstrate triboelectrification via liquid–liquid contact by passing liquid
Externí odkaz:
https://doaj.org/article/1c9394a94ba640b991924a25b7e9341a
Autor:
Xunjia Li, Jianjun Luo, Kai Han, Xue Shi, Zewei Ren, Yi Xi, Yibin Ying, Jianfeng Ping, Zhong Lin Wang
Publikováno v:
Nature Food. 3:133-142
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f17ab816f8467d3acf83a1642c894040
https://doi.org/10.1038/s43016-021-00449-9
https://doi.org/10.1038/s43016-021-00449-9
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4e102b1d309e3f9bf70b3335d548bcf
https://doi.org/10.1109/tim.2021.3105237
https://doi.org/10.1109/tim.2021.3105237
Autor:
Haoran Wang, Yaqing Xue, Xin Song, Shulai Lei, Hong Yu, Cheng-Feng Du, Zewei Ren, Ruisheng Guo, Feng Zhou
Publikováno v:
Journal of Materials Chemistry A. 10:20953-20963
Partial Cr substitution for V in the V4C3Tx MXene promotes the capacitance, discharging potential, and cycle stability of in-plane ZMSCs. The device exhibits a high areal energy density of 51.12 μW h cm−2 and flexibility index of 100.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::940d334a255582e94f4b9162f55a409d
https://doi.org/10.1039/d2ta04747k
https://doi.org/10.1039/d2ta04747k
Publikováno v:
Nano Energy. 110:108375
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb5a9a042d5dc3190e0e162acc0f8932
https://doi.org/10.1016/j.nanoen.2023.108375
https://doi.org/10.1016/j.nanoen.2023.108375
Autor:
Yong Wang, Chengxin Zeng, Yichen Liu, Dingyi Yang, Yu Zhang, Zewei Ren, Qikun Li, Jian Hao, Wen Hu, Yizhang Wu, Rusen Yang
Publikováno v:
Materials; Volume 15; Issue 15; Pages: 5390
Although graphitic carbon nitride nanosheets (CNs) with atomic thickness are considered as promising materials for hydrogen production, the wide band gap (3.06 eV) and rapid recombination of the photogenerated electron–hole pairs impede their appli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d66c07aead7ec058c5a6e1c4b91a629f
https://pubmed.ncbi.nlm.nih.gov/35955325
https://pubmed.ncbi.nlm.nih.gov/35955325
Autor:
Kuijuan Jin, Huibin Lű, Kun Zhao, Honglei Zhan, Guozhen Yang, Shijie Hao, Wenzheng Yue, Zhaohui Meng, Ru Chen, Zewei Ren, Xinyang Miao
Publikováno v:
Energy Reports, Vol 6, Iss, Pp 795-801 (2020)
Deep learning methodologies have revolutionized prediction in many fields and is potential to do the same in the petroleum industry because of the complex oil–gas reservoir. A limitation remains for dense shale exploration in that the shales with i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21534ff6c7703f017d69808574bae76d
https://doi.org/10.1016/j.egyr.2020.04.004
https://doi.org/10.1016/j.egyr.2020.04.004