scholarly journals Physical property and gas transport studies of ultrathin polysulfone membrane from 298.15 to 328.15 K and 2 to 50 bar: atomistic molecular simulation and empirical modelling

RSC Advances ◽  
2020 ◽  
Vol 10 (54) ◽  
pp. 32370-32392
Author(s):  
S. S. M. Lock ◽  
K. K. Lau ◽  
Norwahyu Jusoh ◽  
A. M. Shariff ◽  
Y. F. Yeong ◽  
...  
Keyword(s):  
Transport Properties ◽  
Molecular Simulation ◽  
Gas Transport ◽  
Physical Property ◽  
Operating Conditions ◽  
Polysulfone Membrane ◽  
Empirical Modelling ◽  
Transport Studies ◽  
Ultrathin Membranes

Pioneering work to elucidate and model the effect of operating conditions on physical and transport properties of ultrathin membranes.

scholarly journals A Molecular Simulation Study of Silica/Polysulfone Mixed Matrix Membrane for Mixed Gas Separation

Polymers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 2199
Author(s):  
Khadija Asif ◽  
Serene Sow Mun Lock ◽  
Syed Ali Ammar Taqvi ◽  
Norwahyu Jusoh ◽  
Chung Loong Yiin ◽  
...  
Keyword(s):  
Transport Properties ◽  
Molecular Simulation ◽  
Gas Separation ◽  
Membrane Separation ◽  
Gas Transport ◽  
Mixed Matrix ◽  
Mixed Gas ◽  
Gas Transport Properties ◽  
Simulation Results ◽  
Mixed Gases

Polysulfone-based mixed matrix membranes (MMMs) incorporated with silica nanoparticles are a new generation material under ongoing research and development for gas separation. However, the attributes of a better-performing MMM cannot be precisely studied under experimental conditions. Thus, it requires an atomistic scale study to elucidate the separation performance of silica/polysulfone MMMs. As most of the research work and empirical models for gas transport properties have been limited to pure gas, a computational framework for molecular simulation is required to study the mixed gas transport properties in silica/polysulfone MMMs to reflect real membrane separation. In this work, Monte Carlo (MC) and molecular dynamics (MD) simulations were employed to study the solubility and diffusivity of CO2/CH4 with varying gas concentrations (i.e., 30% CO2/CH4, 50% CO2/CH4, and 70% CO2/CH4) and silica content (i.e., 15–30 wt.%). The accuracy of the simulated structures was validated with published literature, followed by the study of the gas transport properties at 308.15 K and 1 atm. Simulation results concluded an increase in the free volume with an increasing weight percentage of silica. It was also found that pure gas consistently exhibited higher gas transport properties when compared to mixed gas conditions. The results also showed a competitive gas transport performance for mixed gases, which is more apparent when CO2 increases. In this context, an increment in the permeation was observed for mixed gas with increasing gas concentrations (i.e., 70% CO2/CH4 > 50% CO2/CH4 > 30% CO2/CH4). The diffusivity, solubility, and permeability of the mixed gases were consistently increasing until 25 wt.%, followed by a decrease for 30 wt.% of silica. An empirical model based on a parallel resistance approach was developed by incorporating mathematical formulations for solubility and permeability. The model results were compared with simulation results to quantify the effect of mixed gas transport, which showed an 18% and 15% percentage error for the permeability and solubility, respectively, in comparison to the simulation data. This study provides a basis for future understanding of MMMs using molecular simulations and modeling techniques for mixed gas conditions that demonstrate real membrane separation.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

The Effect of the Type and Concentration of the Crosslinking Diene on the Gas Transport Properties of Membranes Based on Polyoctylmethylsiloxane

2020 ◽  
Vol 2 (6) ◽  
pp. 399-406
Author(s):  
E. A. Grushevenko ◽  
I. L. Borisov ◽  
D. S. Bakhtin ◽  
V. V. Volkov ◽  
A. V. Volkov
Keyword(s):  
Transport Properties ◽  
Gas Transport ◽  
Gas Transport Properties
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