Investigation of Gas Permeability in Carbon Nanotube (CNT)−Polymer Matrix Membranes via Modifying CNTs with Functional Groups/Metals and Controlling Modification Location

2011 ◽  
Vol 115 (14) ◽  
pp. 6661-6670 ◽  
Author(s):  
Lei Ge ◽  
Zhonghua Zhu ◽  
Feng Li ◽  
Shaomin Liu ◽  
Li Wang ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Functional Groups ◽  
Polymer Matrix ◽  
Gas Permeability ◽  
Matrix Membranes

Effect of new metal–organic framework (zeolitic imidazolate framework [ZIF-12]) in mixed matrix membranes on structure, morphology, and gas separation properties

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mehtap Safak Boroglu ◽  
Ismail Boz ◽  
Busra Kaya
Keyword(s):  
Gas Separation ◽  
Gas Permeability ◽  
Metal Organic Framework ◽  
Thermomechanical Analysis ◽  
Variable Pressure ◽  
Mixed Matrix Membranes ◽  
Mixed Matrix ◽  
Sem Images ◽  
Zeolitic Imidazolate Framework ◽  
Matrix Membranes

Abstract In our study, the synthesis of zeolitic imidazolate framework (ZIF-12) crystals and the preparation of mixed matrix membranes (MMMs) with various ZIF-12 loadings were targeted. The characterization of ZIF-12 and MMMs were carried out by Fourier transform infrared spectroscopy analysis, thermogravimetric analysis, scanning electron microscopy (SEM), and thermomechanical analysis. The performance of MMMs was measured by the ability of binary gas separation. Commercial polyetherimide (PEI-Ultem® 1000) polymer was used as the polymer matrix. The solution casting method was utilized to obtain dense MMMs. In the SEM images of ZIF-12 particles, the particles with a rhombic dodecahedron structure were identified. From SEM images, it was observed that the distribution of ZIF-12 particles in the MMMs was homogeneous and no agglomeration was present. Gas permeability experiments of MMMs were measured for H2, CO2, and CH4 gases at steady state, at 4 bar and 35 °C by constant volume-variable pressure method. PEI/ZIF-12-30 wt% MMM exhibited high permeability and ideal selectivity values for H2/CH4 and CO2/CH4 were P H 2 / CH 4 = 331.41 ${P}_{{\text{H}}_{2}/{\text{CH}}_{4}}=331.41$ and P CO 2 / CH 4 = 53.75 ${P}_{{\text{CO}}_{2}/{\text{CH}}_{4}}=53.75$ gas pair.

scholarly journals Modelling the Molecular Permeation through Mixed-Matrix Membranes Incorporating Tubular Fillers

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 58
Author(s):  
Ali Zamani ◽  
F. Handan Tezel ◽  
Jules Thibault
Keyword(s):  
Aspect Ratio ◽  
Relative Permeability ◽  
Polymer Matrix ◽  
Volume Fraction ◽  
Mixed Matrix Membranes ◽  
Analytical Prediction ◽  
Mixed Matrix ◽  
Permeability Ratio ◽  
Filler Volume Fraction ◽  
Matrix Membranes

Membrane-based processes are considered a promising separation method for many chemical and environmental applications such as pervaporation and gas separation. Numerous polymeric membranes have been used for these processes due to their good transport properties, ease of fabrication, and relatively low fabrication cost per unit membrane area. However, these types of membranes are suffering from the trade-off between permeability and selectivity. Mixed-matrix membranes, comprising a filler phase embedded into a polymer matrix, have emerged in an attempt to partly overcome some of the limitations of conventional polymer and inorganic membranes. Among them, membranes incorporating tubular fillers are new nanomaterials having the potential to transcend Robeson’s upper bound. Aligning nanotubes in the host polymer matrix in the permeation direction could lead to a significant improvement in membrane permeability. However, although much effort has been devoted to experimentally evaluating nanotube mixed-matrix membranes, their modelling is mostly based on early theories for mass transport in composite membranes. In this study, the effective permeability of mixed-matrix membranes with tubular fillers was estimated from the steady-state concentration profile within the membrane, calculated by solving the Fick diffusion equation numerically. Using this approach, the effects of various structural parameters, including the tubular filler volume fraction, orientation, length-to-diameter aspect ratio, and permeability ratio were assessed. Enhanced relative permeability was obtained with vertically aligned nanotubes. The relative permeability increased with the filler-polymer permeability ratio, filler volume fraction, and the length-to-diameter aspect ratio. For water-butanol separation, mixed-matrix membranes using polydimethylsiloxane with nanotubes did not lead to performance enhancement in terms of permeability and selectivity. The results were then compared with analytical prediction models such as the Maxwell, Hamilton-Crosser and Kang-Jones-Nair (KJN) models. Overall, this work presents a useful tool for understanding and designing mixed-matrix membranes with tubular fillers.

Thiol–ene photopolymerization of vinyl-functionalized metal–organic frameworks towards mixed-matrix membranes

2018 ◽  
Vol 6 (44) ◽  
pp. 21961-21968 ◽  
Author(s):  
Chinnadurai Satheeshkumar ◽  
Hyun Jung Yu ◽  
Hyojin Park ◽  
Min Kim ◽  
Jong Suk Lee ◽  
...  
Keyword(s):  
Polymer Matrix ◽  
Metal Organic Frameworks ◽  
Mixed Matrix Membranes ◽  
Mixed Matrix ◽  
Metal Organic ◽  
Matrix Membranes

A thiol–ene ‘click’ photopolymerization methodology for the covalent connection between vinyl-functionalized metal–organic frameworks (MOFs) and the polymer matrix.

Pebax-1657 mixed matrix membrane containing surface modified multi-walled carbon nanotubes for gas separation

RSC Advances ◽  
2016 ◽  
Vol 6 (83) ◽  
pp. 79563-79577 ◽  
Author(s):  
S. A. Habibiannejad ◽  
A. Aroujalian ◽  
A. Raisi
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Functional Groups ◽  
Gas Separation ◽  
Mixed Matrix Membrane ◽  
Mixed Matrix ◽  
Multi Walled Carbon Nanotubes ◽  
Surface Modified ◽  
Walled Carbon Nanotubes

In this study different functional groups on the surface of carbon nanotube enhanced the performance of Pebax 1657/MWNTs.

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method

2020 ◽  
pp. 002199832095354 ◽  
Author(s):  
Tien-Thinh Le
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Tensile Strength ◽  
Carbon Nanotube ◽  
Polymer Matrix ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Weight Fraction ◽  
Percentage Error ◽  
Input Variables

This paper is devoted to the development and construction of a practical Machine Learning (ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) composites. To this end, a database was compiled from the available literature, composed of 11 input variables. The input variables for predicting tensile strength of nanocomposites were selected for the following main reasons: (i) type of polymer matrix, (ii) mechanical properties of polymer matrix, (iii) physical characteristics of CNTs, (iv) mechanical properties of CNTs and (v) incorporation parameters such as CNT weight fraction, CNT surface modification method and processing method. As the problem of prediction is highly dimensional (with 11 dimensions), the Gaussian Process Regression (GPR) model was selected and optimized by means of a parametric study. The correlation coefficient (R), Willmott’s index of agreement (IA), slope of regression, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed as error measurement criteria when training the GPR model. The GPR model exhibited good performance for both training and testing parts (RMSE = 5.982 and 5.327 MPa, MAE = 3.447 and 3.539 MPa, respectively). In addition, uncertainty analysis was also applied to estimate the prediction confidence intervals. Finally, the prediction capability of the GPR model with different ranges of values of input variables was investigated and discussed. For practical application, a Graphical User Interface (GUI) was developed in Matlab for predicting the tensile strength of nanocomposites.

scholarly journals Origin of the electrocatalytic activity in carbon nanotube fiber counter-electrodes for solar-energy conversion

2020 ◽  
Vol 2 (10) ◽  
pp. 4400-4409
Author(s):  
Alba Martínez-Muíño ◽  
Moumita Rana ◽  
Juan J. Vilatela ◽  
Rubén D. Costa
Keyword(s):  
Solar Cells ◽  
Carbon Nanotube ◽  
Solar Energy ◽  
High Activity ◽  
Functional Groups ◽  
Electrocatalytic Activity ◽  
Counter Electrodes ◽  
Fe Catalyst ◽  
Carbon Nanotube Fiber

A study of the role of functional groups and residual Fe catalyst on the high activity of carbon nanotube (CNT) fibre counter electrodes outperforming Pt in dye-sensitised solar cells (DSSCs) with Co2+/Co3+ redox couple electrolytes.

scholarly journals A Review on Computational Modeling Tools for MOF-Based Mixed Matrix Membranes

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 36 ◽  
Author(s):  
Keskin ◽  
Alsoy Altinkaya
Keyword(s):  
Computational Modeling ◽  
Gas Permeability ◽  
Gas Permeation ◽  
Mixed Matrix Membranes ◽  
Great Promise ◽  
Mixed Matrix ◽  
Modeling Tools ◽  
Trade Off ◽  
Membrane Materials ◽  
Matrix Membranes

Computational modeling of membrane materials is a rapidly growing field to investigate the properties of membrane materials beyond the limits of experimental techniques and to complement the experimental membrane studies by providing insights at the atomic-level. In this study, we first reviewed the fundamental approaches employed to describe the gas permeability/selectivity trade-off of polymer membranes and then addressed the great promise of mixed matrix membranes (MMMs) to overcome this trade-off. We then reviewed the current approaches for predicting the gas permeation through MMMs and specifically focused on MMMs composed of metal organic frameworks (MOFs). Computational tools such as atomically-detailed molecular simulations that can predict the gas separation performances of MOF-based MMMs prior to experimental investigation have been reviewed and the new computational methods that can provide information about the compatibility between the MOF and the polymer of the MMM have been discussed. We finally addressed the opportunities and challenges of using computational studies to analyze the barriers that must be overcome to advance the application of MOF-based membranes.

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