Peter G. Boyd

Bio: Peter G. Boyd is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Metal-organic framework & SBus. The author has an hindex of 20, co-authored 26 publications receiving 2084 citations. Previous affiliations of Peter G. Boyd include University of Ottawa.

Direct Observation and Quantification of CO2 Binding Within an Amine-Functionalized Nanoporous Solid

Abstract: Understanding the molecular details of CO(2)-sorbent interactions is critical for the design of better carbon-capture systems. Here we report crystallographic resolution of CO(2) molecules and their binding domains in a metal-organic framework functionalized with amine groups. Accompanying computational studies that modeled the gas sorption isotherms, high heat of adsorption, and CO(2) lattice positions showed high agreement on all three fronts. The modeling apportioned specific binding interactions for each CO(2) molecule, including substantial cooperative binding effects among the guest molecules. The validation of the capacity of such simulations to accurately model molecular-scale binding bodes well for the theory-aided development of amine-based CO(2) sorbents. The analysis shows that the combination of appropriate pore size, strongly interacting amine functional groups, and the cooperative binding of CO(2) guest molecules is responsible for the low-pressure binding and large uptake of CO(2) in this sorbent material.

Data-driven design of metal-organic frameworks for wet flue gas CO2 capture.

Abstract: Limiting the increase of CO2 in the atmosphere is one of the largest challenges of our generation1. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO2 emissions2, much effort is focused on developing solid adsorbents that can efficiently capture CO2 from flue gases emitted from anthropogenic sources3. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO2 from nitrogen4-7 do not perform well when using realistic flue gas that contains water, because water competes with CO2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive8,9. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO2-binding sites-which we term 'adsorbaphores'-that endow MOFs with CO2/N2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

Abstract: In this work, we have developed quantitative structure–property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.

Understanding the diversity of the metal-organic framework ecosystem

Abstract: Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.

Force-Field Prediction of Materials Properties in Metal-Organic Frameworks

Abstract: In this work, MOF bulk properties are evaluated and compared using several force fields on several well-studied MOFs, including IRMOF-1 (MOF-5), IRMOF-10, HKUST-1, and UiO-66. It is found that, surprisingly, UFF and DREIDING provide good values for the bulk modulus and linear thermal expansion coefficients for these materials, excluding those that they are not parametrized for. Force fields developed specifically for MOFs including UFF4MOF, BTW-FF, and the DWES force field are also found to provide accurate values for these materials' properties. While we find that each force field offers a moderately good picture of these properties, noticeable deviations can be observed when looking at properties sensitive to framework vibrational modes. This observation is more pronounced upon the introduction of framework charges.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Carbon Dioxide Capture in Metal–Organic Frameworks

Abstract: Kenji Sumida, David L. Rogow, Jarad A. Mason, Thomas M. McDonald, Eric D. Bloch, Zoey R. Herm, Tae-Hyun Bae, Jeffrey R. Long

Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation

Abstract: A series of porous crystalline materials known as metal–organic materials are prepared, and a full sorption study shows that controlled pore size (rather than large surface area) coupled with appropriate chemistry lead to materials exhibiting fast and highly selective CO2 sorption. Metal organic frameworks are porous crystalline materials widely studied as potential gas separation and storage materials for clean energy applications. A general trend in this field has been the development of materials with the largest possible surface area with the aim of maximizing uptake of gases. In this paper the authors generate a series of metal organic frameworks and carry out sorption experiments that suggest that surface area may not be as important as was thought. Rather, pore size, coupled with appropriate chemistry, are the keys to fast CO2 uptake and strong CO2 sorption. Materials designed on these principles attain high selectivity for CO2 over nitrogen, oxygen, methane and hydrogen even in the presence of moisture. The energy costs associated with the separation and purification of industrial commodities, such as gases, fine chemicals and fresh water, currently represent around 15 per cent of global energy production, and the demand for such commodities is projected to triple by 2050 (ref. 1). The challenge of developing effective separation and purification technologies that have much smaller energy footprints is greater for carbon dioxide (CO2) than for other gases; in addition to its involvement in climate change, CO2 is an impurity in natural gas, biogas (natural gas produced from biomass), syngas (CO/H2, the main source of hydrogen in refineries) and many other gas streams. In the context of porous crystalline materials that can exploit both equilibrium and kinetic selectivity, size selectivity and targeted molecular recognition are attractive characteristics for CO2 separation and capture, as exemplified by zeolites 5A and 13X (ref. 2), as well as metal–organic materials (MOMs)3,4,5,6,7,8,9. Here we report that a crystal engineering7 or reticular chemistry5,9 strategy that controls pore functionality and size in a series of MOMs with coordinately saturated metal centres and periodically arrayed hexafluorosilicate (SiF62−) anions enables a ‘sweet spot’ of kinetics and thermodynamics that offers high volumetric uptake at low CO2 partial pressure (less than 0.15 bar). Most importantly, such MOMs offer an unprecedented CO2 sorption selectivity over N2, H2 and CH4, even in the presence of moisture. These MOMs are therefore relevant to CO2 separation in the context of post-combustion (flue gas, CO2/N2), pre-combustion (shifted synthesis gas stream, CO2/H2) and natural gas upgrading (natural gas clean-up, CO2/CH4).