Analysis of the Water Adsorption Isotherms in UiO-Based Metal–Organic Frameworks

2021 ◽  
Author(s):  
Mason C. Lawrence ◽  
Michael J. Katz
Keyword(s):  
Adsorption Isotherms ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Metal Organic

Tuning water adsorption, stability, and phase in Fe-MIL-101 and Fe-MIL-88 analogs with amide functionalization

2021 ◽  
Author(s):  
Andrew Kuznicki ◽  
Gregory Lorzing ◽  
Eric D Bloch
Keyword(s):  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
High Tunability ◽  
Metal Organic

Metal-organic frameworks (MOFs) of the MIL series of materials have been widely studied as a result of their high tunability and the diversity of structure types that exist for these...

Underoil superhydrophilic surfaces: water adsorption in metal–organic frameworks

2018 ◽  
Vol 6 (4) ◽  
pp. 1692-1699 ◽  
Author(s):  
Mingming Liu ◽  
Lu Tie ◽  
Jing Li ◽  
Yuanyuan Hou ◽  
Zhiguang Guo
Keyword(s):  
Crude Oil ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Building Blocks ◽  
Water Filtration ◽  
On Demand ◽  
Emulsion Separation ◽  
Metal Organic ◽  
Self Cleaning ◽  
Superhydrophilic Surfaces

Inspired by sarcocarps, metal–organic frameworks (MOFs) that can capture moisture spontaneously are presented as building blocks for the construction of underoil superhydrophilic surfaces. The MOF coating showed excellent self-cleaning properties to crude oil under water, and achieved on-demand emulsion separation through selective water filtration and adsorption.

Understanding Inflections and Steps in Carbon Dioxide Adsorption Isotherms in Metal-Organic Frameworks

2008 ◽  
Vol 130 (2) ◽  
pp. 406-407 ◽  
Author(s):  
Krista S. Walton ◽  
Andrew R. Millward ◽  
David Dubbeldam ◽  
Houston Frost ◽  
John J. Low ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Adsorption Isotherms ◽  
Metal Organic Frameworks ◽  
Carbon Dioxide Adsorption ◽  
Metal Organic

scholarly journals Upgrading the Hydrogen Storage of MOF-5 by Post-Synthetic Exchange with Divalent Metal Ions

2021 ◽  
Vol 11 (24) ◽  
pp. 11687
Author(s):  
Abdul Malik P. Peedikakkal ◽  
Isam H. Aljundi
Keyword(s):  
Metal Ions ◽  
Adsorption Isotherms ◽  
Metal Organic Frameworks ◽  
Hydrogen Uptake ◽  
Metal Exchange ◽  
Mixed Metal ◽  
H2 Adsorption ◽  
Hydrogen Molecules ◽  
Metal Metal ◽  
Metal Organic

In metal-organic frameworks (MOFs), mixed-metal clusters have the opportunity to adsorb hydrogen molecules due to a greater charge density of the metal. Such interactions may subsequently enhance the gravimetric uptake of hydrogen. However, only a few papers have explored the ability of mixed-metal MOFs to increase hydrogen uptake. The present work reveals the preparation of mixed metal metal-organic frameworks M-MOF-5 (where M = Ni2+, Co2+, and Fe2+) (where MOF-5 designates MOFs such as Zn2+ and 1,4-benzenedicarboxylic acid ligand) using the post-synthetic exchange (PSE) technique. Powder X-ray diffraction patterns and scanning electron microscopy images indicate the presence of crystalline phases after metal exchange, and the inductively coupled plasma–mass spectroscopy analysis confirmed the exchange of metals by means of the PSE technique. The nitrogen adsorption isotherms established the production of microporous M-MOF-5. Although the additional metal ions decreased the surface area, the exchanged materials displayed unique features in the gravimetric uptake of hydrogen. The parent MOF-5 and the metal exchanged materials (Ni-MOF-5, Co-MOF-5, and Fe-MOF-5) demonstrated hydrogen capacities of 1.46, 1.53, 1.53, and 0.99 wt.%, respectively. The metal-exchanged Ni-MOF-5 and Co-MOF-5 revealed slightly higher H2 uptake in comparison with MOF-5; however, the Fe-MOF-5 showed a decrease in uptake due to partial discrete complex formation (discrete complexes with one or more metal ions) with less crystalline nature. The Sips model was found to be excellent in describing the H2 adsorption isotherms with a correlation coefficient ≅ 1. The unique hydrogen uptakes of Ni− and Co-MOF-5 shown in this study pave the way for further improvement in hydrogen uptake.

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2020 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

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