Impact of the strength and spatial distribution of adsorption sites on methane deliverable capacity in nanoporous materials

The methane deliverable capacity of adsorbent materials is a critical performance metric that will determine the viability of using adsorbed natural gas (ANG) technology in vehicular applications. ARPA-E recently set a target deliverable capacity of 315 cc(STP)/cc that a viable adsorbent material should achieve to yield a driving range competitive with incumbent fuels. However, recent computational screening of hundreds of thousands of materials suggests that the target is unattainable. In this work, we aim to determine whether the observed limits in deliverable capacity (similar to 200 cc(STP)/cc) are fundamental limits arising from thermodynamic or material design constraints. Our efforts focus on simulating methane adsorption isotherms in a large number of systems, resulting in a broad exploration of different combinations of spatial distributions and energetics of adsorption sites. All systems were classified into five adsorption scenarios with varying degrees of realism in the manner that adsorption sites are created and endowed with energetics. The scenarios range from methane adsorption on discrete idealized lattice sites to adsorption in metal-organic frameworks with coordinatively unsaturated sites (CUS) provided by metalated catechol groups. Our findings strongly suggest that the ARPA-E target is unattainable, although not due to thermodynamic constraints but due to material design constraints. On the other hand, we also find that the currently observed deliverable capacity limits may be moderately surpassed. For instance, incorporation of CUS in IRMOF-10 is predicted to yield a 217 cc(STP)/cc deliverable capacity. The modified material has a similar to 0.85 void fraction and a heat of adsorption of similar to 15 kJ/mol. This suggests that similar, moderate improvements over existing materials could be achieved as long as CUS incorporation still maintains a relatively large void fraction. Nonetheless, we conclude that more significant improvements in deliverable capacity will require changes in the currently proposed operation conditions. (C) 2016 Elsevier Ltd. All rights reserved.

Advancing computational and data-driven methods for the design and discovery of nanoporous materials

Seyedmohamad Moosavi

Metal-organic framework (MOFs) and related nanoporous materials have emerged as promising candidates for a variety of applications, such as gas separation and storage, catalysis, sensing, etc. Their building block structure allows us to generate a huge number of distinct materials only by changing the metal nodes and organic linkers. This, in principle, allows the design and discovery of materials that perform optimally for a given application. However, the conventional process of material development, from discovery to synthesis and performance evaluation, is too slow and expensive for exploring this enormous pool of materials. Complimentary methods are therefore needed to accelerate this process. The aim of this thesis is to investigate and expand the computational and data-driven methods for the development of nanoporous materials for gas separation and storage. The prevailing use of these methods is for high-throughput screening of the materials for a target application. However, the capability and success of such a screening approach depend on fast, reliable, and accurate prediction of material properties, as well as on the effective exploration of the chemical space. Therefore, in this thesis, we develop material descriptors for the chemistry and pore geometry of MOFs, which allow us to use machine learning to rapidly evaluate their adsorption properties with high accuracy. We next introduce a methodology to quantify the diversity of material databases to assess how well the chemical space is explored when a given material database is screened. We illustrate the importance of this diversity analysis by showing how the lack of diversity in MOF databases hinders material discovery, leads to chemical insights that are not generalisable and makes machine learning models not transferable. The promising materials discovered in a screening study are only of interest if they can be synthesised and are sufficiently stable to withstand the operating conditions of the corresponding application. Therefore, we investigate the applicability and capability of computational and data-driven methods to address some of the challenges in the material synthesis and the mechanical stability of MOFs. Material synthesis still mainly rests on the chemical intuition of synthetic chemists. Here, we introduce a method using machine learning and a genetic algorithm to capture this chemical intuition for MOF synthesis. We demonstrate how this simple approach can be powerful for guiding the synthesis of new materials. Lastly, we study how the mechanical stability of MOFs, which is fundamentally important for most of their practical applications, is affected by its underlying structure, i.e., the framework bonding topology and ligand structure. We show how this understanding can be used to develop strategies to design MOFs with enhanced mechanical stability.

EPFL2020

Evaluating different classes of porous materials for carbon capture

Johanna Maria Huck, Berend Smit

Carbon Capture and Sequestration (CCS) is one of the promising ways to significantly reduce the CO2 emission from power plants. In particular, amongst several separation strategies, adsorption by nano-porous materials is regarded as a potential means to efficiently capture CO2 at the place of its origin in a post-combustion process. The search for promising materials in such a process not only requires the screening of a multitude of materials but also the development of an adequate evaluation metric. Several evaluation criteria have been introduced in the literature concentrating on a single adsorption or material property at a time. Parasitic energy is a new approach for material evaluation to address the energy load imposed on a power plant while applying CCS. In this work, we evaluate over 60 different materials with respect to their parasitic energy, including experimentally realized and hypothetical materials such as metal-organic frameworks (MOFs), zeolitic imidazolate frameworks (ZIFs), porous polymer networks (PPNs), and zeolites. The results are compared to other proposed evaluation criteria and performance differences are studied regarding the regeneration modes, (i.e. Pressure-Swing (PSA) and Temperature-Swing Adsorption (TSA)) as well as the flue gas composition.

Royal Soc Chemistry2014

The materials genome in action: identifying the performance limits for methane storage

Rocio Mercado, Cory Matthew Simon, Berend Smit

Analogous to the way the Human Genome Project advanced an array of biological sciences by mapping the human genome, the Materials Genome Initiative aims to enhance our understanding of the fundamentals of materials science by providing the information we need to accelerate the development of new materials. This approach is particularly applicable to recently developed classes of nanoporous materials, such as metal-organic frameworks (MOFs), which are synthesized from a limited set of molecular building blocks that can be combined to generate a very large number of different structures. In this Perspective, we illustrate how a materials genome approach can be used to search for high-performance adsorbent materials to store natural gas in a vehicular fuel tank. Drawing upon recent reports of large databases of existing and predicted nanoporous materials generated in silico, we have collected and compared on a consistent basis the methane uptake in over 650 000 materials based on the results of molecular simulation. The data that we have collected provide candidate structures for synthesis, reveal relationships between structural characteristics and performance, and suggest that it may be difficult to reach the current Advanced Research Project Agency-Energy (ARPA-E) target for natural gas storage.

Royal Society of Chemistry2015

Advancing computational and data-driven methods for the design and discovery of nanoporous materials

Seyedmohamad Moosavi

EPFL2020