Water adsorption/desorption over metal-organic frameworks with ammonium group for possible application in adsorption heat transformation

2019 ◽  
Vol 373 ◽  
pp. 1064-1071 ◽  
Author(s):  
Hyung Jun An ◽  
Mithun Sarker ◽  
Dong Kyu Yoo ◽  
Sung Hwa Jhung
Keyword(s):  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Adsorption Heat ◽  
Ammonium Group ◽  
Metal Organic ◽  
Heat Transformation ◽  
Adsorption Desorption

Air-Con Metal–Organic Frameworks in Binder Composites for Water Adsorption Heat Transformation Systems

2019 ◽  
Vol 58 (47) ◽  
pp. 21493-21503 ◽  
Author(s):  
Serkan Gökpinar ◽  
Sebastian-Johannes Ernst ◽  
Emrah Hastürk ◽  
Marc Möllers ◽  
Ilias El Aita ◽  
...  
Keyword(s):  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Adsorption Heat ◽  
Metal Organic ◽  
Heat Transformation ◽  
Transformation Systems

Mesoporous Metal-Organic Frameworks with Exceptionally High Working Capacities for Adsorption Heat Transformation

2017 ◽  
Vol 30 (4) ◽  
pp. 1704350 ◽  
Author(s):  
Zong-Wen Mo ◽  
Hao-Long Zhou ◽  
Dong-Dong Zhou ◽  
Rui-Biao Lin ◽  
Pei-Qin Liao ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
Adsorption Heat ◽  
Metal Organic ◽  
Heat Transformation ◽  
Mesoporous Metal

scholarly journals Screening of metal–organic frameworks for water adsorption heat transformation using structure–property relationships

RSC Advances ◽  
2020 ◽  
Vol 10 (57) ◽  
pp. 34621-34631
Author(s):  
Min Xu ◽  
Zhangli Liu ◽  
Xiulan Huai ◽  
Lanting Lou ◽  
Jiangfeng Guo
Keyword(s):  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Quantitative Structure ◽  
Structure Property ◽  
Adsorption Performance ◽  
Structure Property Relationships ◽  
Structure Property Relationship ◽  
Physicochemical Features ◽  
Metal Organic ◽  
Heat Transformation

Quantitative structure–property relationship models that correlate the water adsorption performance of MOFs to their physicochemical features have been established.

scholarly journals Composite Salt in Porous Metal-Organic Frameworks for Adsorption Heat Transformation

2017 ◽  
Vol 27 (21) ◽  
pp. 1606424 ◽  
Author(s):  
Luis Garzón-Tovar ◽  
Javier Pérez-Carvajal ◽  
Inhar Imaz ◽  
Daniel Maspoch
Keyword(s):  
Metal Organic Frameworks ◽  
Porous Metal ◽  
Adsorption Heat ◽  
Metal Organic ◽  
Heat Transformation

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.

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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