Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Yanbin Chan"'
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
This study proposed a hydroponic system with the capacity to acquire high-resolution in situ mass data for non-destructive evaluation of water circulation in lettuce. The system customizes the watering profile, enables high-frequency in situ weight m
Externí odkaz:
https://doaj.org/article/9b4a35544cd94ee5b929cb6a2cba4daf
Publikováno v:
Heliyon, Vol 10, Iss 18, Pp e37640- (2024)
Purpose: Due to the serious threat of tuberculosis to global health and limitations of existing diagnostic methods, this study combined the CRISPR/Cas12a system with Multiply-primed-RCA (MRCA) technology for Mycobacterium tuberculosis Point-of-care T
Externí odkaz:
https://doaj.org/article/ee66ca4da99949d4a37af9e307328629
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Lettuce (Lactuca sativa) germination is sensitive to environmental conditions. Recently, hydrogel has received increased attention as an alternative media to soil for seed germination. Compared to soil seeding, hydrogel-aided germination provides mor
Externí odkaz:
https://doaj.org/article/b3a89a6f36ad4115bca1ea2c20fdf80f
Autor:
John D. Busbee, Paul V. Braun, Huigang Zhang, Yu-Tsun Shao, Tan Shi, Pengcheng Sun, Shuangbao Wang, Zihan Shen, Jerome Davis, Yanbin Chan, Jinyun Liu, Chadd Kiggins, Hailong Ning, Jian-Min Zuo, Sheng Xu, Yuyan Hua, Peng Wang, Janna Eaves, Xuhao Hong
Publikováno v:
Science advances, vol 3, iss 5
Science Advances
Science Advances
Electrodeposition of lithium-ion battery cathodes enables ultraflexible, ultrathick, and high-power rechargeable batteries.
Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten
Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0d3dc696d09a62d640bb3a74a0e1018
https://doi.org/10.1126/sciadv.1602427
https://doi.org/10.1126/sciadv.1602427
Publikováno v:
Communications Biology, Vol 6, Iss 1, Pp 1-9 (2023)
Abstract Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computatio
Externí odkaz:
https://doaj.org/article/40efa7e64bae4a5291e3b6c41fb7cab3