Microporous Lanthanide Metal-Organic Frameworks Containing Coordinatively Linked Interpenetration: Syntheses, Gas Adsorption Studies, Thermal Stability Analysis, and Photoluminescence Investigation

2009 ◽  
Vol 48 (5) ◽  
pp. 2072-2077 ◽  
Author(s):  
Shengqian Ma ◽  
Daqiang Yuan ◽  
Xi-Sen Wang ◽  
Hong-Cai Zhou
Keyword(s):  
Thermal Stability ◽  
Stability Analysis ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Adsorption Studies ◽  
Metal Organic ◽  
Lanthanide Metal

Two lanthanide metal–organic frameworks constructed from tris(4-carboxyphenyl)phosphane oxide with gas adsorption and magnetic properties

CrystEngComm ◽  
2018 ◽  
Vol 20 (30) ◽  
pp. 4291-4296 ◽  
Author(s):  
Tuoping Hu ◽  
Qiannan Zhao ◽  
Liangqin Huo ◽  
Lingling Gao ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Magnetic Properties ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Alternating Current ◽  
Metal Organic ◽  
Lanthanide Metal ◽  
Adsorption Of Co

Based on the tripodal tris(4-carboxyphenyl)phosphane oxide ligand, two lanthanide metal–organic frameworks were obtained, with 1 showing highly selective gas adsorption of CO2/CH4 and 2 exhibiting direct and alternating current magnetic properties.

Robust lanthanide metal–organic frameworks with “all-in-one” multifunction: efficient gas adsorption and separation, tunable light emission and luminescence sensing

2021 ◽  
Author(s):  
Bo Li ◽  
Jian-Peng Dong ◽  
Zhe Zhou ◽  
Rui Wang ◽  
Li-Ya Wang ◽  
...  
Keyword(s):  
Gas Adsorption ◽  
Light Emission ◽  
Metal Organic Frameworks ◽  
Gas Uptake ◽  
Metal Organic ◽  
Lanthanide Metal

A robust lanthanide MOF platform displays all-in-one multifunction, including excellent gas uptake and separation, tunable light emission and efficient luminescence sensing.

Luminescent metal organic frameworks for sensing and gas adsorption studies

CrystEngComm ◽  
2019 ◽  
Vol 21 (36) ◽  
pp. 5470-5481 ◽  
Author(s):  
Pankaj Verma ◽  
Udai P. Singh ◽  
Ray J. Butcher
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Three Dimensional ◽  
Adsorption Studies ◽  
Metal Organic

Two three-dimensional metal organic frameworks (LZn and LCd) were synthesized solvothermally for sensing of nitro phenolic explosives and gas adsorption studies. LZn showed selectivity towards N2 gas at 77 K.

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>

Sign in / Sign up

Export Citation Format

Share Document