scholarly journals Quest for an Optimal Methane Hydrate Formation in the Pores of Hydrolytically Stable Metal–Organic Frameworks

2020 ◽  
Vol 142 (31) ◽  
pp. 13391-13397 ◽  
Author(s):  
Carlos Cuadrado-Collados ◽  
Georges Mouchaham ◽  
Luke Daemen ◽  
Yongqiang Cheng ◽  
Anibal Ramirez-Cuesta ◽  
...  
Keyword(s):  
Methane Hydrate ◽  
Metal Organic Frameworks ◽  
Hydrate Formation ◽  
Metal Organic

scholarly journals Paving the way for methane hydrate formation on metal–organic frameworks (MOFs)

2016 ◽  
Vol 7 (6) ◽  
pp. 3658-3666 ◽  
Author(s):  
Mirian E. Casco ◽  
Fernando Rey ◽  
José L. Jordá ◽  
Svemir Rudić ◽  
François Fauth ◽  
...  
Keyword(s):  
Neutron Scattering ◽  
Powder Diffraction ◽  
Methane Hydrate ◽  
Inelastic Neutron Scattering ◽  
Metal Organic Frameworks ◽  
Hydrate Formation ◽  
Inelastic Neutron ◽  
X Ray ◽  
Metal Organic ◽  
First Time

The formation of methane hydrates on MOFs has been identified for the first time using inelastic neutron scattering and synchrotron X-ray powder diffraction.

Metal–Organic Framework HKUST-1 Promotes Methane Hydrate Formation for Improved Gas Storage Capacity

2020 ◽  
Vol 12 (47) ◽  
pp. 53510-53518
Author(s):  
Shurraya Denning ◽  
Ahmad AA Majid ◽  
Jolie M. Lucero ◽  
James M. Crawford ◽  
Moises A. Carreon ◽  
...  
Keyword(s):  
Methane Hydrate ◽  
Storage Capacity ◽  
Metal Organic Framework ◽  
Hydrate Formation ◽  
Gas Storage ◽  
Metal Organic ◽  
Organic Framework

Metal-Organic Frameworks

2021 ◽  
Author(s):  
Lars Öhrström ◽  
Francoise M. Amombo Noa
Keyword(s):  
Metal Organic Frameworks ◽  
Metal Organic

Substrate templated synthesis of single-phase and uniform Zr-porphyrin-based metal–organic frameworks

2020 ◽  
Vol 7 (1) ◽  
pp. 221-231
Author(s):  
Seong Won Hong ◽  
Ju Won Paik ◽  
Dongju Seo ◽  
Jae-Min Oh ◽  
Young Kyu Jeong ◽  
...  
Keyword(s):  
Chemical Bath Deposition ◽  
Single Phase ◽  
Metal Organic Frameworks ◽  
Templated Synthesis ◽  
Pore Structures ◽  
Phase Pure ◽  
Metal Organic ◽  
Cbd Method

We successfully demonstrate that the chemical bath deposition (CBD) method is a versatile method for synthesizing phase-pure and uniform MOFs by controlling their nucleation stages and pore structures.

Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O2 Binding

2019 ◽  
Author(s):  
Andrew Rosen ◽  
M. Rasel Mian ◽  
Timur Islamoglu ◽  
Haoyuan Chen ◽  
Omar Farha ◽  
...  
Keyword(s):  
Density Functional ◽  
Metal Organic Frameworks ◽  
Redox Activity ◽  
Screening Methods ◽  
Bridging Ligands ◽  
Metal Centers ◽  
Computational Screening ◽  
Open Metal Sites ◽  
Metal Organic ◽  
Unsaturated Metal Sites

<p>Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O<sub>2</sub> and N<sub>2</sub> adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br<sup>-</sup>, μ-Cl<sup>-</sup>, μ-F<sup>-</sup>, μ-SH<sup>-</sup>, or μ-OH<sup>-</sup> groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O<sub>2</sub> affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O<sub>2</sub>. In contrast with O<sub>2</sub> adsorption, N<sub>2</sub> adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V<sup>2+</sup> open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl<sup>-</sup> ligands of M<sub>2</sub>Cl<sub>2</sub>(BBTA) (H<sub>2</sub>BBTA = 1<i>H</i>,5<i>H</i>-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH<sup>-</sup> groups would significantly enhance the strength of O<sub>2</sub> adsorption at the open metal sites without a corresponding increase in the N<sub>2</sub> affinity. Experimental investigation of Co<sub>2</sub>Cl<sub>2</sub>(BBTA) and Co<sub>2</sub>(OH)<sub>2</sub>(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O<sub>2</sub> at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH<sup>-</sup><sub> </sub>ligands and the presence of H-bonding interactions between the μ-OH<sup>-</sup> bridging ligands and the O<sub>2</sub> adsorbate.</p>

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

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