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

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.

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.

Phosphonate monoesters as carboxylate-like linkers for metal organic frameworks.

Bidentate phosphonate monoesters are analogues of popular dicarboxylate linkers in MOFs, but with an alkoxy tether close to the coordinating site. Herein, we report 3-D MOF materials based upon phosphonate monoester linkers. Cu(1,4-benzenediphosphonate bis(monoalkyl ester), CuBDPR, with an ethyl tether is nonporous; however, the methyl tether generates an isomorphous framework that is porous and captures CO(2) with a high isosteric heat of adsorption of 45 kJ mol(-1). Computational modeling reveals that the CO(2) uptake is extremely sensitive both to the flexing of the structure and to the orientation of the alkyl tether.

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

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.

A single-ligand ultra-microporous MOF for precombustion CO2 capture and hydrogen 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) m(2)/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.

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.