Using Artificial Neural Network and Ideal Adsorbed Solution Theory for Predicting the CO2/CH4Selectivities of Metal–Organic Frameworks: A Comparative Study

2017 ◽  
Vol 56 (49) ◽  
pp. 14610-14622 ◽  
Author(s):  
Kourosh Esfandiari ◽  
Ali Asghar Ghoreyshi ◽  
Mohsen Jahanshahi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Metal Organic Frameworks ◽  
Solution Theory ◽  
Ideal Adsorbed Solution Theory ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Artificial Neural ◽  
Adsorbed Solution Theory

scholarly journals Predicting adsorption selectivities from pure gas isotherms for gas mixtures in metal–organic frameworks

2020 ◽  
Vol 11 (3) ◽  
pp. 643-655 ◽  
Author(s):  
Arpan Kundu ◽  
Kaido Sillar ◽  
Joachim Sauer
Keyword(s):  
Geometric Mean ◽  
Metal Organic Frameworks ◽  
Gas Mixtures ◽  
Mixing Rule ◽  
Lateral Interactions ◽  
Solution Theory ◽  
Ideal Adsorbed Solution Theory ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Adsorbed Solution Theory

A new mixing rule (geometric mean) is proposed with substantial improvements compared to the widely used ideal adsorbed solution theory for adsorbates with strong lateral interactions.

Optimization studies and artificial neural network modeling for pyrene adsorption onto UiO-66(Zr) and NH2-UiO-66(Zr) metal organic frameworks

Polyhedron ◽  
2020 ◽  
Vol 192 ◽  
pp. 114857 ◽  
Author(s):  
Zakariyya Uba Zango ◽  
Anita Ramli ◽  
Khairulazhar Jumbri ◽  
Nonni Soraya Sambudi ◽  
Hamza Ahmad Isiyaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Metal Organic Frameworks ◽  
Neural Network Modeling ◽  
Artificial Neural Network Modeling ◽  
Metal Organic ◽  
Artificial Neural

A highly stable MOF with a rod SBU and a tetracarboxylate linker: unusual topology and CO2adsorption behaviour under ambient conditions

2014 ◽  
Vol 50 (31) ◽  
pp. 4047-4049 ◽  
Author(s):  
Ru-Jin Li ◽  
Mian Li ◽  
Xiao-Ping Zhou ◽  
Dan Li ◽  
Michael O'Keeffe
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Framework ◽  
Solution Theory ◽  
Ambient Conditions ◽  
Ideal Adsorbed Solution Theory ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Adsorbed Solution Theory ◽  
Organic Framework

A Mn-based rod metal–organic framework (MOF), ROD-6, with a new lrk net is synthesized and studied with Ideal Adsorbed Solution Theory (IAST) for selective gas adsorption.

scholarly journals Role of Electrostatic Effects in the Pure Component and Binary Adsorption of Ethylene and Ethane in Cu-Tricarboxylate Metal-Organic Frameworks

2007 ◽  
Vol 25 (8) ◽  
pp. 607-619 ◽  
Author(s):  
Timothy M. Nicholson ◽  
Suresh K. Bhatia
Keyword(s):  
Selective Adsorption ◽  
Isosteric Heat ◽  
Pure Component ◽  
Solution Theory ◽  
Ideal Adsorbed Solution Theory ◽  
Separation Factors ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Gcmc Simulations ◽  
Adsorbed Solution Theory

The interaction of ethane and ethylene with a Cu-tricarboxylate complex was investigated, showing that at low loadings the lighter molecule has a higher binding energy as a result of interaction with framework Cu and H-bonding with basic framework oxygen atoms. This leads to the selective adsorption of ethylene at low pressure by a factor of ca. 2. This is overcome by the stronger van der Waals interaction of ethane at high loadings, explaining recent literature data. Both experimental data and single-component Grand Canonical Monte Carlo (GCMC) simulations were fitted well with the Unilan model and mixture isotherms were satisfactorily predicted by the Ideal Adsorbed Solution Theory when compared with binary simulation results. Both binary GCMC simulations and Ideal Adsorbed Solution Theory predictions yielded separation factors of ca. 2 and a difference in isosteric heat of 3 kJ/mol. The results suggest that the Cu-BTC framework offers a possible route for the separation of ethane and ethylene, a Holy Grail of adsorption.

Green Synthesis and Enhanced CO2 Capture Performance of Perfluorinated Cerium-Based Metal-Organic Frameworks with UiO-66 and MIL-140 Topology

2018 ◽  
Author(s):  
Roberto D’Amato ◽  
Anna Donnadio ◽  
Mariolino Carta ◽  
Claudio Sangregorio ◽  
Riccardo Vivani ◽  
...  
Keyword(s):  
Photoelectron Spectroscopy ◽  
Metal Organic Frameworks ◽  
Isosteric Heat ◽  
Experimental Conditions ◽  
Cerium Ammonium Nitrate ◽  
Ideal Adsorbed Solution Theory ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Better Than ◽  
The Ideal

Reaction of cerium ammonium nitrate and tetrafluoroterephthalic acid in water afforded two new metal-organic frameworks with UiO-66 [F4_UiO-66(Ce)] and MIL-140 [F4_MIL-140A(Ce)] topologies. The two compounds can be obtained in the same experimental conditions, just by varying the amount of acetic acid used as crystallization modulator in the synthesis. Both F4_UiO-66(Ce) and F4_MIL-140A(Ce) feature pores with size < 8 Å, which classifies them as ultramicroporous. Combination of X-ray photoelectron spectroscopy and magnetic susceptibility measurements revealed that both compounds contain a small amount of Ce(III), which is preferentially accumulated near the surface of the crystallites. The CO<sub>2</sub> sorption properties of F4_UiO-66(Ce) and F4_MIL-140A(Ce) were investigated, finding that they perform better than their Zr-based analogues. F4_MIL-140A(Ce) displays an unusual S-shaped isotherm with steep uptake increase at pressure < 0.2 bar at 298 K. This makes F4_MIL-140A(Ce) exceptionally selective for CO<sub>2</sub> over N<sub>2</sub>: the calculated selectivity, according to the ideal adsorbed solution theory for a 0.15:0.85 mixture at 1 bar and 293 K, is higher than 1900, amongst the highest ever reported for metal-organic frameworks. The calculated isosteric heat of CO<sub>2 </sub>adsorption is in the range of 38-40 kJ mol<sup>-1</sup>, indicating a strong physisorptive character.

scholarly journals Comparative Study of Artificial Neural Network Based Channel Equalization Methods for MmWave Communications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Noe M. Yungaicela-Naula ◽  
Leyre Azpilicueta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Channel Equalization ◽  
Artificial Neural
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