Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Qiangshun, Guan"'
Autor:
Qiangshun, Guan, Hongying, Li, Goharzadeh, Afshin, Didarul Islam, MD, Chang Wei, Kang, Yit Fatt, Yap
Publikováno v:
In Applied Thermal Engineering 5 January 2024 236 Part B
Publikováno v:
Advanced Science, Vol 9, Iss 18, Pp n/a-n/a (2022)
Abstract The construction of photocatalytic systems that have strong redox capability, effective charge separation, and large reactive surfaces is of great scientific and practical interest. Herein, an edge‐connected 2D/2D Z‐scheme system that co
Externí odkaz:
https://doaj.org/article/f8f00b856a9744958b8ccd5dd141d82b
Publikováno v:
ACS Photonics. 10:715-726
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b59fe1da2aa44b26645ae03541887b5f
https://doi.org/10.1021/acsphotonics.2c01857
https://doi.org/10.1021/acsphotonics.2c01857
Autor:
Qiangshun Guan, Shoaib Anwer, Lianxi Zheng, Khalid Al-Ali, Giovanni Palmisano, Corrado Garlisi, Xuan Li
Publikováno v:
Materials Today. 47:75-107
Photocatalysis utilizes solar energy to produce clean fuels such as hydrogen or generate highly reactive species to subsequently break the organic pollutants into clean end products. Direct Z-scheme heterostructured photocatalysts can overcome the fu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41721ba94066b5bdbd1ec7fca050eeae
https://doi.org/10.1016/j.mattod.2021.02.017
https://doi.org/10.1016/j.mattod.2021.02.017
Publikováno v:
MRS Advances. 5:1537-1545
Machine learning-based approach is desired for accelerating materials design, development and discovery in combination with high-throughput experiments and simulation. In this work, we propose to apply a Bayesian optimization method to design ultrath
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50e21d5f506f9e0a72d60a0707b5cd3d
https://doi.org/10.1557/adv.2019.468
https://doi.org/10.1557/adv.2019.468
Publikováno v:
Composites Science and Technology. 217:109091
The greatest challenge in creating digital material twins from μCT images is the lack of a robust and versatile tool for segmenting the μCT images and post-processing the segmented volumes into a FE mesh. Here, we have used deep convolutional neura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3808b69769935a7fc2702ff01bb1b2d0
https://doi.org/10.1016/j.compscitech.2021.109091
https://doi.org/10.1016/j.compscitech.2021.109091
Publikováno v:
Advanced Engineering Materials. 23:2100118
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e80255cc70e0188987ca12adb7b1f91d
https://doi.org/10.1002/adem.202100118
https://doi.org/10.1002/adem.202100118
Publikováno v:
Composites Part A: Applied Science and Manufacturing. 139:106131
In this study, a novel approach of processing μCT images to create digital material twins is presented. A deep convolutional neural network (DCNN) was implemented and used to segment μCT images of two different types of reinforcement (2D glass and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d04953f19a304729b61e39cb811569a7
https://doi.org/10.1016/j.compositesa.2020.106131
https://doi.org/10.1016/j.compositesa.2020.106131