Synthesis, Water Adsorption, and Proton Conductivity of Solid-Solution-Type Metal-Organic Frameworks Al(OH)(bdc-OH)x(bdc-NH2)1−x

2014 ◽  
Vol 9 (5) ◽  
pp. 1316-1320 ◽  
Author(s):  
Teppei Yamada ◽  
Yuki Shirai ◽  
Hiroshi Kitagawa
Keyword(s):  
Solid Solution ◽  
Proton Conductivity ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Solution Type ◽  
Metal Organic

Synthesis, characterisation, water adsorption and proton conductivity of three Cd(ii) based luminescent metal–organic frameworks

2014 ◽  
Vol 1 (8) ◽  
pp. 611-620 ◽  
Author(s):  
Srinivasulu Parshamoni ◽  
Himanshu Sekhar Jena ◽  
Suresh Sanda ◽  
Sanjit Konar
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Proton Conductivity ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Impedance Measurement ◽  
Metal Organic ◽  
Higher Temperature

Three Cd(ii) based H-bonded 3D MOFs show a significant amount of uptake for water vapor and an impedance measurement indicates that they show proton conductivity at a higher temperature (358 K) at 95% relative humidity.

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...

Metal–organic frameworks derived from a semi-rigid anthracene-based ligand and sulfonates: proton conductivity and dye degradation studies

CrystEngComm ◽  
2021 ◽  
Author(s):  
Aurobinda Mohanty ◽  
Udai P. Singh ◽  
Arijit Ghorai ◽  
Susanta Banerjee ◽  
R. J. Butcher
Keyword(s):  
Methylene Blue ◽  
Relative Humidity ◽  
Proton Conductivity ◽  
Dye Degradation ◽  
Metal Organic Frameworks ◽  
High Proton Conductivity ◽  
Methylene Blue Degradation ◽  
High Proton ◽  
Metal Organic ◽  
Degradation Studies

The MOFs were constructed by ligand AHP and sulfonate analogues. MOF4 exhibits a high proton conductivity of 1.95 × 10−3 S cm−1 at 95 °C and 98% relative humidity. MOFs 1–5 also serve as photocatalysts for methylene blue degradation.

scholarly journals The role of lattice vibration in the terahertz region for proton conduction in 2D metal–organic frameworks

2020 ◽  
Vol 11 (6) ◽  
pp. 1538-1541
Author(s):  
Tomoya Itakura ◽  
Hiroshi Matsui ◽  
Tomofumi Tada ◽  
Susumu Kitagawa ◽  
Aude Demessence ◽  
...  
Keyword(s):  
Proton Conductivity ◽  
Electronic Structures ◽  
Lattice Vibration ◽  
Metal Organic Frameworks ◽  
Proton Conduction ◽  
Terahertz Region ◽  
Metal Organic

Terahertz-regime vibrations of 2D MOFs dominate the intrinsic proton conductivity, and the motions depend on the valence electronic structures.

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.

scholarly journals High proton conductivity in cyanide-bridged metal–organic frameworks: understanding the role of water

2015 ◽  
Vol 3 (44) ◽  
pp. 22347-22352 ◽  
Author(s):  
Yuan Gao ◽  
Richard Broersen ◽  
Wouter Hageman ◽  
Ning Yan ◽  
Marjo C. Mittelmeijer-Hazeleger ◽  
...  
Keyword(s):  
Proton Conductivity ◽  
Metal Organic Frameworks ◽  
Water Molecules ◽  
High Proton Conductivity ◽  
Lattice Water ◽  
High Proton ◽  
Metal Organic

Hop hop hop: the proton conductivity of the [Nd(mpca)2Nd(H2O)6Mo(CN)8]·nH2O is enabled by lattice water molecules in its channels.

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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