Cory M. Simon

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

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.

Kinetically tuned dimensional augmentation as a versatile synthetic route towards robust metal-organic frameworks.

Metal-organic frameworks with high stability have been pursued for many years due to the sustainability requirement for practical applications. However, researchers have had great difficulty synthesizing chemically ultra-stable, highly porous metal-organic frameworks in the form of crystalline solids, especially as single crystals. Here we present a kinetically tuned dimensional augmentation synthetic route for the preparation of highly crystalline and extremely robust metal-organic frameworks with a preserved metal cluster core. Through this versatile synthetic route, we obtain large single crystals of 34 different iron-containing metal-organic frameworks. Among them, PCN-250(Fe2Co) exhibits high volumetric uptake of hydrogen and methane, and is also stable in water and aqueous solutions with a wide range of pH values.

Metal–organic framework with optimally selective xenon adsorption and separation

Nuclear energy is among the most viable alternatives to our current fossil fuel-based energy economy. The mass deployment of nuclear energy as a low-emissions source requires the reprocessing of used nuclear fuel to recover fissile materials and mitigate radioactive waste. A major concern with reprocessing used nuclear fuel is the release of volatile radionuclides such as xenon and krypton that evolve into reprocessing facility off-gas in parts per million concentrations. The existing technology to remove these radioactive noble gases is a costly cryogenic distillation; alternatively, porous materials such as metal–organic frameworks have demonstrated the ability to selectively adsorb xenon and krypton at ambient conditions. Here we carry out a high-throughput computational screening of large databases of metal–organic frameworks and identify SBMOF-1 as the most selective for xenon. We affirm this prediction and report that SBMOF-1 exhibits by far the highest reported xenon adsorption capacity and a remarkable Xe/Kr selectivity under conditions pertinent to nuclear fuel reprocessing.

In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure-property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane-methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

pyIAST: Ideal adsorbed solution theory (IAST) Python package

Ideal adsorbed solution theory (LAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the pure-component isotherm data. pylAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition. Source code: https://github.com/CorySimon/pyIAST Documentation: http://pyiast.readthedocs.org/en/latest/ Program summary Program title: pyIAST Catalogue identifier: AEZA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEZA_v1_0.html Program obtainable from: CPC Program Library, Queens University, Belfast, N. Ireland Licensing provisions: MIT No. of lines in distributed program, including test data, etc.: 38478 No. of bytes in distributed program, including test data, etc.: 1918879 Distribution format: tar.gz Programming language: Python. Operating system: Linux, Mac, Windows. Classification: 23. External routines: Pandas, Numpy, Scipy Nature of problem: Using ideal adsorbed solution theory (IAST) to predict mixed gas adsorption isotherms from pure-component adsorption isotherm data. Solution method: Characterize the pure-component adsorption isotherm from experimental or simulated data by fitting a model or using linear interpolation; solve the nonlinear system of equations of IAST. Running time: Less than a second

Optimizing nanoporous materials for gas storage

In this work, we address the question of which thermodynamic factors determine the deliverable capacity of methane in nanoporous materials. The deliverable capacity is one of the key factors that determines the performance of a material for methane storage in automotive fuel tanks. To obtain insights into how the molecular characteristics of a material are related to the deliverable capacity, we developed several statistical thermodynamic models. The predictions of these models are compared with the classical thermodynamics approach of Bhatia and Myers [Bhatia and Myers, Langmuir, 2005, 22, 1688] and with the results of molecular simulations in which we screen the International Zeolite Association (IZA) structure database and a hypothetical zeolite database of over 100,000 structures. Both the simulations and our models do not support the rule of thumb that, for methane storage, one should aim for an optimal heat of adsorption of 18.8 kJ mol(-1). Instead, our models show that one can identify an optimal heat of adsorption, but that this optimal heat of adsorption depends on the structure of the material and can range from 8 to 23 kJ mol(-1). The different models we have developed are aimed to determine how this optimal heat of adsorption is related to the molecular structure of the material.

Materials genome in action: Identifying the performance limits of physical hydrogen storage

The Materials Genome is in action: the molecular codes for millions of materials have been sequenced, predictive models have been developed, and now the challenge of hydrogen storage is targeted. Renewably generated hydrogen is an attractive transportation fuel with zero carbon emissions, but its storage remains a significant challenge. Nanoporous adsorbents have shown promising physical adsorption of hydrogen approaching targeted capacities, but the scope of studies has remained limited. Here the Nanoporous Materials Genome, containing over 850 000 materials, is analyzed with a variety of computational tools to explore the limits of hydrogen storage. Optimal features that maximize net capacity at room temperature include pore sizes of around 6 Å and void fractions of 0.1, while at cryogenic temperatures pore sizes of 10 Å and void fractions of 0.5 are optimal. Our top candidates are found to be commercially attractive as “cryo-adsorbents”, with promising storage capacities at 77 K and 100 bar with 30% enhancement to 40 g/L, a promising alternative to liquefaction at 20 K and compression at 700 bar.

Noria: A Highly Xe-Selective Nanoporous Organic Solid

Separation of xenon and krypton is of industrial and environmental concern; the existing technologies use cryogenic distillation. Thus, a cost-effective, alternative technology for the separation of Xe and Kr and their capture from air is of significant importance. Herein, we report the selective Xe uptake in a crystalline porous organic oligomeric molecule, noria, and its structural analogue, PgC-noria, under ambient conditions. The selectivity of noria towards Xe arises from its tailored pore size and small cavities, which allows a directed non-bonding interaction of Xe atoms with a large number of carbon atoms of the noria molecular wheel in a confined space.

Statistical mechanical model of gas adsorption in porous crystals with dynamic moieties

Significance Some nanoporous, crystalline materials possess dynamic/flexible constituents, for example, a ligand that can rotate. Much like the induced-fit model of enzyme–substrate binding in biology, these dynamic moieties often change conformation when gas molecules adsorb. Such flexible constituents may endow nanoporous materials with enhanced properties for gas storage and separations, chemical sensing, drug delivery, and catalysis. We developed and solved a statistical mechanical model of gas adsorption in a porous material with a rotating ligand that is shared between cages. Our model contributes a more intimate understanding of gas adsorption in nanoporous materials with moving parts and lends insights into how to harness these dynamic constituents for adsorption-based processes. Some nanoporous, crystalline materials possess dynamic constituents, for example, rotatable moieties. These moieties can undergo a conformation change in response to the adsorption of guest molecules, which qualitatively impacts adsorption behavior. We pose and solve a statistical mechanical model of gas adsorption in a porous crystal whose cages share a common ligand that can adopt two distinct rotational conformations. Guest molecules incentivize the ligands to adopt a different rotational configuration than maintained in the empty host. Our model captures inflections, steps, and hysteresis that can arise in the adsorption isotherm as a signature of the rotating ligands. The insights disclosed by our simple model contribute a more intimate understanding of the response and consequence of rotating ligands integrated into porous materials to harness them for gas storage and separations, chemical sensing, drug delivery, catalysis, and nanoscale devices. Particularly, our model reveals design strategies to exploit these moving constituents and engineer improved adsorbents with intrinsic thermal management for pressure-swing adsorption processes.

The role of dendritic spine morphology in the compartmentalization and delivery of surface receptors

Since AMPA receptors are major molecular players in both short- and long-term plasticity, it is important to identify the time-scales of and factors affecting the lateral diffusion of AMPARs on the dendrite surface. Using a mathematical model, we study how the dendritic spine morphology affects two processes: (1) compartmentalization of the surface receptors in a single spine to retain local chemistry and (2) the delivery of receptors to the post-synaptic density (PSD) of spines via lateral diffusion following insertion onto the dendrite shaft. Computing the mean first passage time (MFPT) of surface receptors on a sample of real spine morphologies revealed that a constricted neck and bulbous head serve to compartmentalize receptors, consistent with previous works. The residence time of a Brownian diffusing receptor on the membrane of a single spine was computed to be ∼ 5 s. We found that the location of the PSD corresponds to the location at which the maximum MFPT occurs, the position that maximizes the residence time of a diffusing receptor. Meanwhile, the same geometric features of the spine that compartmentalize receptors inhibit the recruitment of AMPARs via lateral diffusion from dendrite insertion sites. Spines with narrow necks will trap a smaller fraction of diffusing receptors in the their PSD when considering competition for receptors between the spines, suggesting that ideal geometrical features involve a tradeoff depending on the intent of compartmentalizing the current receptor pool or recruiting new AMPARs in the PSD. The ultimate distribution of receptors among the spine PSDs by lateral diffusion from the dendrite shaft is an interplay between the insertion location and the shape and locations of both the spines and their PSDs. The time-scale for delivery of receptors to the PSD of spines via lateral diffusion was computed to be ∼ 60 s.