Prediction of sorption in polymers using quantum chemical calculations: Application to polymer membranes

Shah, Mitesh R.; Yadav, Ganapati D.

doi:10.1016/j.memsci.2012.09.037

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…Many of these thermodynamic models and theories require fitting a number of adjustable parameters that reduce their applicability and cast doubt on their predictive ability [45][46][47][48]. The COSMObased models are outstanding alternative to these models, by which a predictive description of many industrial processes can be carried out [49][50][51]. For the present work, a COSMO-based model was employed for calculation of activity coefficients [52].…”

Section: Il Imentioning

confidence: 99%

“…The geometry optimization of compound molecules was done using the Dmol 3 Module with the use of density functional theory of generalized gradient approximate (GGA) with Volsko-Wilk-Nusair functional (VWN) and v.4.0.0 basis of double numerical basis including d-polarization function (DND) level of theory was implemented as recommended in [52]. The same setting was used for energy optimization as recommended in [49,50]. The obtained ".…”

Section: A C C E P T E Dmentioning

confidence: 99%

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Molecular-level understanding of supported ionic liquid membranes for gas separation

Mohammadi

Asadollahzadeh

Shirazian

2018

Journal of Molecular Liquids

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Section: Il Imentioning

confidence: 99%

Section: A C C E P T E Dmentioning

confidence: 99%

Molecular-level understanding of supported ionic liquid membranes for gas separation

Mohammadi

Asadollahzadeh

Shirazian

2018

Journal of Molecular Liquids

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“…Currently, a promising alternative to laboratory screening is computer simulation studies, where new polymers are designed in silico. Several computational methods can predict various properties of polymer materials, namely Quantum Chemistry (QC) [6,7], molecular mechanics (MM) [8,9], Finite Elements (FE) [10], Quantitative Structure-Property Relationships (QSPR) [11][12][13], and QSPR neural networks [14,15]. They operate on different space-time scales, which define their limitations on system size and computational resources.…”

Section: Introductionmentioning

confidence: 99%

New Polymers In Silico Generation and Properties Prediction

Knizhnik,

Komarov,

Potapkin

et al. 2023

Nanomanufacturing

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We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data.

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“…Works in the literature deal with the quantitative prediction of permeability for polymeric materials. − Memari et al have studied gas mixture (H 2 , CO 2 , and CH 4 ) sorptions in poly(ethylene) below its melting point using Monte Carlo (MC) molecular simulations. ,, From a technical point of view, the consideration of larger permeant molecules such as hydrocarbons, alcohols, or ethers may lead to problems of insertions and thus problems of convergence. Moreover, the inclusion of molecular simulation techniques such as MC or molecular dynamics (MD) into high throughput screening procedures still represents a challenge, and approaches based on the concept of quantitative structure–property relationship (QSPR) appear more appropriate .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Alternative Gasoline Sorption in a Semicrystalline Poly(ethylene)

2015

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In this work, we first report the acquisition of new experimental data and then the development of quantitative structure-property relationships on the basis of sorption values for neat compounds and up to quinary mixtures of some hydrocarbons, alcohols, and ethers, in a semicrystalline poly(ethylene). Two machine learning methods (i.e., genetic function approximation and support vector machines) and two families of descriptors (i.e., functional group counts and substructural molecular fragments) were used to derive predictive models. Models were then used to predict sorption variations when increasing the number of carbon atoms in a series of hydrocarbons and for n-alkan-1-ols. In addition to the performed internal/external validations, the model was further tested for surrogate gasolines containing ca. 300 compounds, and predicted sorption values were in excellent agreement with experimental data (R(2) = 0.940).

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Prediction of sorption in polymers using quantum chemical calculations: Application to polymer membranes

Cited by 15 publications

References 45 publications

Molecular-level understanding of supported ionic liquid membranes for gas separation

Molecular-level understanding of supported ionic liquid membranes for gas separation

New Polymers In Silico Generation and Properties Prediction

Prediction of Alternative Gasoline Sorption in a Semicrystalline Poly(ethylene)

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