Zobrazeno 1 - 10
of 37
pro vyhledávání: '"George S. Fanourgakis"'
Publikováno v:
Sustainable Chemistry for the Environment, Vol 5, Iss , Pp 100056- (2024)
Unique properties of Metal-Organic Frameworks (MOFs), such as their extremely high surface areas and porosity, render them as one of, if not the most promising adsorbents for gas storage applications. However, their extremely flexible and tunable nat
Externí odkaz:
https://doaj.org/article/1e581347bc9142cba12cda938b53dde3
Autor:
Javad Iskandarov, George S. Fanourgakis, Shehzad Ahmed, Waleed Alameri, George E. Froudakis, Georgios N. Karanikolos
Publikováno v:
RSC Advances. 12:35703-35711
Data-driven approaches for foams can be a time- and data-efficient alternative method to experimental and conventional modeling techniques.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfe88abb7aad44ec831e2e299594b2ac
https://doi.org/10.1039/d2ra05841c
https://doi.org/10.1039/d2ra05841c
Publikováno v:
The Journal of Physical Chemistry C. 124:19639-19648
Toward the fast and accurate identification of top performing candidates from the huge pool of nanoporous materials that can potentially be synthesized, we present and evaluate a methodology that c...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab498e3db4beb80101cbb3721ff4f0d9
https://doi.org/10.1021/acs.jpcc.0c05491
https://doi.org/10.1021/acs.jpcc.0c05491
Publikováno v:
The Journal of Physical Chemistry C. 124:7117-7126
In the present study, we propose a new set of descriptors, appropriate for machine learning (ML) methods, aiming to predict accurately the gas adsorption capacities of nanoporous materials. The pre...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f8f8900dee80ad4f5f34795b241bd91
https://doi.org/10.1021/acs.jpcc.9b10766
https://doi.org/10.1021/acs.jpcc.9b10766
Publikováno v:
Journal of the American Chemical Society. 142:3814-3822
Application of machine learning (ML) methods for the determination of the gas adsorption capacities of nanomaterials, such as metal-organic frameworks (MOF), has been extensively investigated over the past few years as a computationally efficient alt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e49b90c5e1d4e76bb5ef9d45fe4b2bc0
https://doi.org/10.1021/jacs.9b11084
https://doi.org/10.1021/jacs.9b11084
Autor:
Linda Zhang, Mark D Allendorf, Rafael Balderas-Xicohténcatl, Darren P Broom, George S Fanourgakis, George E Froudakis, Thomas Gennett, Katherine E Hurst, Sanliang Ling, Chiara Milanese, Philip A Parilla, Daniele Pontiroli, Mauro Riccò, Sarah Shulda, Vitalie Stavila, Theodore A Steriotis, Colin J Webb, Matthew Witman, Michael Hirscher
Publikováno v:
Progress in Energy
Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H2 adsorbs reversibly, with very fast kinetics, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d9d9575d55e27fb2857631c0ea5e6f5
https://doi.org/10.1088/2516-1083/ac8d44
https://doi.org/10.1088/2516-1083/ac8d44
Autor:
Darren P. Broom, George E. Froudakis, Pantelis N. Trikalitis, George S. Fanourgakis, Michael Hirscher, Colin J. Webb
Publikováno v:
International Journal of Hydrogen Energy. 44:7768-7779
Hydrogen storage in nanoporous materials has been attracting a great deal of attention in recent years, as high gravimetric H2 capacities, exceeding 10 wt% in some cases, can be achieved at 77 K using materials with particularly high surface areas. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc58a6f7e0174e89b38c29bffa2f4cc4
https://doi.org/10.1016/j.ijhydene.2019.01.224
https://doi.org/10.1016/j.ijhydene.2019.01.224
A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials
Autor:
Konstantinos Gkagkas, George E. Froudakis, Emmanuel Tylianakis, Emmanuel Klontzas, George S. Fanourgakis
Publikováno v:
The journal of physical chemistry. A. 123(28)
In the present study, we propose a new set of descriptors that, along with a few structural features of nanoporous materials, can be used by machine learning algorithms for accurate predictions of the gas uptake capacities of these materials. All new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00a6a8169b12dad92df1be037e59bd84
https://pubmed.ncbi.nlm.nih.gov/31264869
https://pubmed.ncbi.nlm.nih.gov/31264869
Autor:
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, Fangqun Yu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a196e7c71295a3251d09c5dfaf7eba3
https://doi.org/10.5194/acp-2018-1340-supplement
https://doi.org/10.5194/acp-2018-1340-supplement
Autor:
Yang Yang, Hitoshi Matsui, Ulrike Lohmann, Susanne E. Bauer, Alf Grini, Jill S. Johnson, Maria Sfakianaki, Vlassis A. Karydis, Alf Kirkevåg, Tommi Bergman, Roland Schrödner, Fangqun Yu, Philip Stier, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, Stelios Myriokefalitakis, Kenneth S. Carslaw, Julia Schmale, Twan van Noije, Martin Gysel Beer, Alexandra P. Tsimpidi, Xiaohong Liu, John K. Kodros, Douglas S. Hamilton, Gan Luo, Maria Kanakidou, Risto Makkonen, Daniel M. Westervelt, Stefano Decesari, Nikos Kalivitis, David Neubauer, Nikos Daskalakis, Mingxuan Wu, Natalie M. Mahowald, M. Yoshioka, Jeffrey R. Pierce, George S. Fanourgakis, Athanasios Nenes
Publikováno v:
Atmospheric Chemistry and Physics Discussions
A total of sixteen global chemistry transport models and general circulation models have participated in this study. Fourteen models have been evaluated with regard to their ability to reproduce near-surface observed number concentration of aerosol p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1bbe4d2d27718054e8233c58a2075d36
https://doi.org/10.5194/acp-2018-1340
https://doi.org/10.5194/acp-2018-1340