Thomas D. Daff

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

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.

Characterization of Zn‐Containing Metal–Organic Frameworks by Solid‐State 67Zn NMR Spectroscopy and Computational Modeling

Metal-organic frameworks (MOFs) are an extremely important class of porous materials with many applications. The metal centers in many important MOFs are zinc cations. However, their Zn environments have not been characterized directly by (67)Zn solid-state NMR (SSNMR) spectroscopy. This is because (67)Zn (I=5/2) is unreceptive with many unfavorable NMR characteristics, leading to very low sensitivity. In this work, we report, for the first time, a (67)Zn natural abundance SSNMR spectroscopic study of several representative zeolitic imidazolate frameworks (ZIFs) and MOFs at an ultrahigh magnetic field of 21.1 T. Our work demonstrates that (67)Zn magic-angle spinning (MAS) NMR spectra are highly sensitive to the local Zn environment and can differentiate non-equivalent Zn sites. The (67)Zn NMR parameters can be predicted by theoretical calculations. Through the study of MOF-5 desolvation, we show that with the aid of computational modeling, (67)Zn NMR spectroscopy can provide valuable structural information on the MOF systems with structures that are not well described. Using ZIF-8 as an example, we further demonstrate that (67)Zn NMR spectroscopy is highly sensitive to the guest molecules present inside the cavities. Our work also shows that a combination of (67)Zn NMR data and molecular dynamics simulation can reveal detailed information on the distribution and the dynamics of the guest species. The present work establishes (67)Zn SSNMR spectroscopy as a new tool complementary to X-ray diffraction for solving outstanding structural problems and for determining the structures of many new MOFs yet to come.

A single-ligand ultra-microporous MOF for precombustion CO2 capture and hydrogen purification

A single small-ligand–based ultra-microporous MOF showing high CO2 selectivity and PSA working capacity for H2 purification. Metal organic frameworks (MOFs) built from a single small ligand typically have high stability, are rigid, and have syntheses that are often simple and easily scalable. However, they are normally ultra-microporous and do not have large surface areas amenable to gas separation applications. We report an ultra-microporous (3.5 and 4.8 Å pores) Ni-(4-pyridylcarboxylate)2 with a cubic framework that exhibits exceptionally high CO2/H2 selectivities (285 for 20:80 and 230 for 40:60 mixtures at 10 bar, 40°C) and working capacities (3.95 mmol/g), making it suitable for hydrogen purification under typical precombustion CO2 capture conditions (1- to 10-bar pressure swing). It exhibits facile CO2 adsorption-desorption cycling and has CO2 self-diffusivities of ~3 × 10−9 m2/s, which is two orders higher than that of zeolite 13X and comparable to other top-performing MOFs for this application. Simulations reveal a high density of binding sites that allow for favorable CO2-CO2 interactions and large cooperative binding energies. Ultra-micropores generated by a small ligand ensures hydrolytic, hydrostatic stabilities, shelf life, and stability toward humid gas streams.

A density functional theory investigation of the molecular and dissociative adsorption of hydrazine on defective copper surfaces

Density functional theory calculations of the adsorption of hydrazine (N2H4) on the copper (111), (100) and (110) surfaces have shown that the surface structure is key in determining the thermodynamics of adsorption, with low coordinated atoms, resulting from the surface geometry, providing sites for strong adsorption. Although the uneven structure of the (110) surface allows for the strongest binding of molecular hydrazine through bridging between surface atoms, the addition of adatoms to the otherwise more stable and flatter (111) and (100) surfaces provides sites that enable binding to almost the same extent. The thermodynamics of dissociative adsorption by breaking of the hydrazine N–N bond show that this binding mode is strongly favoured over molecular adsorption both on the planar surfaces and at the adatoms. The strength of adsorption is shown to increase with decreasing surface stability, with adsorption energies for dissociative adsorption ranging from 229 kJ mol−1 to 257 kJ mol−1, whereas molecular hydrazine adsorbs with the release of 94 kJ mol−1 to 107 kJ mol−1.

Materials design by evolutionary optimization of functional groups in metal-organic frameworks

Machine learning is used to optimize functional groups of metal-organic frameworks for a specific application. A genetic algorithm that efficiently optimizes a desired physical or functional property in metal-organic frameworks (MOFs) by evolving the functional groups within the pores has been developed. The approach has been used to optimize the CO2 uptake capacity of 141 experimentally characterized MOFs under conditions relevant for postcombustion CO2 capture. A total search space of 1.65 trillion structures was screened, and 1035 derivatives of 23 different parent MOFs were identified as having exceptional CO2 uptakes of >3.0 mmol/g (at 0.15 atm and 298 K). Many well-known MOF platforms were optimized, with some, such as MIL-47, having their CO2 adsorption increase by more than 400%. The structures of the high-performing MOFs are provided as potential targets for synthesis.

Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum

AbstractHydrogen embrittlement is a complex phenomenon, involving several length- and timescales, that affects a large class of metals. It can significantly reduce the ductility and load-bearing capacity and cause cracking and catastrophic brittle failures at stresses below the yield stress of susceptible materials. Despite a large research effort in attempting to understand the mechanisms of failure and in developing potential mitigating solutions, hydrogen embrittlement mechanisms are still not completely understood. There are controversial opinions in the literature regarding the underlying mechanisms and related experimental evidence supporting each of these theories. The aim of this paper is to provide a detailed review up to the current state of the art on the effect of hydrogen on the degradation of metals, with a particular focus on steels. Here, we describe the effect of hydrogen in steels from the atomistic to the continuum scale by reporting theoretical evidence supported by quantum calculation and modern experimental characterisation methods, macroscopic effects that influence the mechanical properties of steels and established damaging mechanisms for the embrittlement of steels. Furthermore, we give an insight into current approaches and new mitigation strategies used to design new steels resistant to hydrogen embrittlement.