Hydrogen storage in porous crystalline materials: insights on the role of interaction strength from simulation and machine learning

Hydrogen is a promising renewable fuel due to its carbon-free nature and relatively high energy content by mass. However, a major hurdle for its widespread adoption as a vehicular fuel is its low density at ambient conditions, posing a challenge for onboard storage. Toward fully realizing a cost-effective, hydrogen-powered fuel cell vehicle, the U.S. Department of Energy (DOE) has set a system-level hydrogen storage target of 30 g/L for 2020, which is expected to require storing around 60 g/L at the material-level. While considerable research has been performed on hydrogen storage materials, it is still unclear whether these storage demands can be met by physisorption-based hydrogen storage systems. To assess the viability of these targets, grand canonical Monte Carlo (GCMC) simulations were used to calculate 18,000+ hydrogen loadings in porous crystals featuring catecholate functionalities at different thermodynamic conditions. From the data, the effects of interaction strength on the deliverable capacity of the material were elucidated. The simulation data was also used to develop an artificial neural network (ANN) model to predict hydrogen loadings using the force field parameters, textural properties of the crystal and thermodynamic conditions as input. The model was used to explore optimal operating conditions for hydrogen storage beyond those initially simulated with GCMC. It was found that optimizing the H2-catecholate interaction strength allowed some porous crystals to achieve deliverable capacities of 60 g/L with a 100 bar/77K ↔ 5 bar/160K swing in pressure and temperature. Additionally, it was shown that other porous crystals can reach 95% of the above deliverable capacity with a storage pressure of only 20 bar as long as the H2-catecholate interaction strength is optimized.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Chemical and Biological Engineering

Grantor dc:publisher

Colorado School of Mines. Arthur Lakes Library

Year dc:date.issued

2018

Author dc:creator

• Schweitzer, Benjamin

Advisor dc:contributor.advisor

• Gómez-Gualdrón, Diego A.

Committee members dc:contributor.committeemember

• Carreon, Moises A.
• Sum, Amadeu K.

• covalent organic frameworks
• machine learning
• molecular simulation
• hydrogen storage
• adsorption
• metal organic frameworks

Statement dc:rights

• Copyright of the original work is retained by the author.

Language dc:language.iso

eng, English

Identifier

T 8647

OAI identifier oai:identifier

oai:repository.mines.edu:11124/172833