Modeling Quantum Effects on Adsorption and Diffusion of Hydrogen in Metal–Organic Frameworks

2015 ◽  
pp. 251-294
Author(s):  
Giovanni Garberoglio
Keyword(s):  
Metal Organic Frameworks ◽  
Quantum Effects ◽  
Metal Organic ◽  
And Diffusion ◽  
Diffusion Of Hydrogen

On the relationship between the structure of metal–organic frameworks and the adsorption and diffusion of hydrogen

2011 ◽  
Vol 37 (7) ◽  
pp. 621-639 ◽  
Author(s):  
Nethika S. Suraweera ◽  
Ruichang Xiong ◽  
J. P. Luna ◽  
Donald M. Nicholson ◽  
David J. Keffer
Keyword(s):  
Metal Organic Frameworks ◽  
Structure Of Metal ◽  
Metal Organic ◽  
The Relationship ◽  
And Diffusion ◽  
Diffusion Of Hydrogen

scholarly journals Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

2020 ◽  
Vol 10 (2) ◽  
pp. 569 ◽  
Author(s):  
Xiaomei Deng ◽  
Wenyuan Yang ◽  
Shuhua Li ◽  
Hong Liang ◽  
Zenan Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Plants ◽  
Co2 Adsorption ◽  
Optimal Algorithm ◽  
Metal Organic Frameworks ◽  
Dynamics Simulation ◽  
Low Concentration ◽  
Metal Organic ◽  
The Law ◽  
And Diffusion

The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.

scholarly journals Hydrogen storage mechanism and diffusion in metal–organic frameworks

2019 ◽  
Vol 21 (15) ◽  
pp. 7756-7764 ◽  
Author(s):  
Kenichi Koizumi ◽  
Katsuyuki Nobusada ◽  
Mauro Boero
Keyword(s):  
Energy Storage ◽  
Metal Organic Frameworks ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Storage Mechanism ◽  
Hydrogen Molecules ◽  
P Diffusion ◽  
Metal Organic ◽  
And Storage ◽  
And Diffusion

Diffusion and storage of hydrogen molecules in metal organic frameworks are crucial for the development of next-generation energy storage devices.

Nuclear Quantum Effects in the Layering and Diffusion of Hydrogen Isotopes in Carbon Nanotubes

2015 ◽  
Vol 6 (17) ◽  
pp. 3367-3372 ◽  
Author(s):  
Piotr Kowalczyk ◽  
Artur P. Terzyk ◽  
Piotr A. Gauden ◽  
Sylwester Furmaniak ◽  
Katsumi Kaneko ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Quantum Effects ◽  
Hydrogen Isotopes ◽  
Nuclear Quantum Effects ◽  
And Diffusion ◽  
Diffusion Of Hydrogen
Sign in / Sign up

Export Citation Format

Share Document