Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Sae Dieb"'
Data-driven optimization of the in silico design of ionic liquids as interfacial cell culture fluids
Publikováno v:
Science and Technology of Advanced Materials, Vol 25, Iss 1 (2024)
As an alternative to conventional plastic dishes, the interface between water-immiscible hydrophobic fluids, such as perfluorocarbons and silicones, permits cell adhesion and growth. Thus, it is expected to replace the petroleum-derived products in a
Externí odkaz:
https://doaj.org/article/e1f5fdd63e9c4acba3975c11e0cb4692
Autor:
Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, Masahiko Demura
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardening of Ni-based alloy to predict more flexible non-isothermal aging (NIA) and to examine the possible routes for the enhancement in st
Externí odkaz:
https://doaj.org/article/d00fbb4ec9374ad88ae87ec4445bf984
Publikováno v:
Science and Technology of Advanced Materials: Methods, Vol 3, Iss 1 (2023)
ABSTRACTThe determination of chemical compositions of materials plays a paramount role in materials design and discovery. Optimization of such compositions can be a very expensive trial-and-error task, specially when the desired properties are very s
Externí odkaz:
https://doaj.org/article/db80d73e9859407f9e9696a23e401b9f
Publikováno v:
Research Ideas and Outcomes, Vol 8, Iss , Pp 1-3 (2022)
SAMURAI (NIMS 2022), a directory service of the National Institute for Materials Science (NIMS) researchers in Japan was launched in 2009 following the development of NIMS institutional repository (Tanifuji et al. 2019). The concept is to synchronize
Externí odkaz:
https://doaj.org/article/9eef41cde51c4360b52d559dcc7ef5ec
Autor:
Luca Foppiano, Sae Dieb, Akira Suzuki, Pedro Baptista de Castro, Suguru Iwasaki, Azusa Uzuki, Miren Garbine Esparza Echevarria, Yan Meng, Kensei Terashima, Laurent Romary, Yoshihiko Takano, Masashi Ishii
Publikováno v:
Science and Technology of Advanced Materials: Methods, Vol 1, Iss 1, Pp 34-44 (2021)
A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-
Externí odkaz:
https://doaj.org/article/840843e48be2474d80fdd8af14ad3d90
Publikováno v:
Science and Technology of Advanced Materials: Methods, Vol 1, Iss 1, Pp 2-11 (2021)
In this study, we present an approach to create a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP). We apply this approach on SAMURAI, a directory
Externí odkaz:
https://doaj.org/article/eb7346e51d0e40cc865978dd99258cd2
Publikováno v:
The Journal of Physical Chemistry Letters. 14:3594-3601
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eda34b60229b549f8b5c88d7092a7fa3
https://doi.org/10.1021/acs.jpclett.3c00242
https://doi.org/10.1021/acs.jpclett.3c00242
Autor:
Sae Dieb
The approach of creating a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP) has become of more importance with the expansion of research informatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26e7b5d0f93de883b37c21bd64257b22
Autor:
Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, Masahiko Demura
In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardenable Ni-based alloy to predict more flexible non-isothermal heat treatment and to examine the possible heat treatment routes for the enhancemen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6460a55a860045cf28670213f2a57543
https://doi.org/10.21203/rs.3.rs-2593940/v1
https://doi.org/10.21203/rs.3.rs-2593940/v1
Autor:
Pedro Baptista de Castro, Yoshihiko Takano, Kensei Terashima, Luca Foppiano, Miren Esparza Echevarria, Sae Dieb, Masashi Ishii, Akira Suzuki, Yan Meng, Azusa Uzuki, Suguru Iwasaki, Laurent Romary
Publikováno v:
Science and Technology of Advanced Materials: Methods
Science and Technology of Advanced Materials: Methods, Taylor & Francis, 2021, 1 (1), ⟨10.1080/27660400.2021.1918396⟩
Science and Technology of Advanced Materials: Methods, 2021, 1 (1), ⟨10.1080/27660400.2021.1918396⟩
Science and Technology of Advanced Materials: Methods, Taylor & Francis, 2021, 1 (1), ⟨10.1080/27660400.2021.1918396⟩
Science and Technology of Advanced Materials: Methods, 2021, 1 (1), ⟨10.1080/27660400.2021.1918396⟩
A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b9c77ed76b698302d6a4c3c16b0633c
https://doi.org/10.1080/27660400.2021.1918396
https://doi.org/10.1080/27660400.2021.1918396