Polyimides Based on the Diethyltoluenediamine Isomer Mixture: Synthesis and Gas Transport Properties

Кузнецов, А. А.; Tsegelskaya, A. Yu.; Орлова, А. М.; Белов, Н. А.; Чирков, С. В.; Nikiforov, R. Yu.; Alentiev, A. Yu.

doi:10.1134/s2517751619050044

“…The test set in this work was selected from papers published in 2019 and 2020 reporting gas permeability of polymers of intrinsic microporosity (PIMs) [18,35,36] and polyimides [37][38][39][40][41][42],…”

Section: Methodsmentioning

confidence: 99%

“…We selected publications with experimental data not recorded in the Polymer Gas Separation Membrane Database for PIMs [18,35,36] and polyimides [37][38][39][40][41][42]. Representative molecular structures of the PIMs and polyimides are shown in Fig.…”

Section: Validation Of the Imputation Models On The Test Setmentioning

confidence: 99%

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Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

Qi¹,

Longo²,

Thornton³

et al. 2021

Preprint

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<a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a>

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“…Membrane Database for PIMs [18,35,36] and polyimides [37][38][39][40][41][42]. Representative molecular structures of the PIMs and polyimides are shown in Fig.…”

Section: Validation Of the Imputation Models On The Test Setmentioning

confidence: 99%

Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

Qi¹,

Longo²,

Thornton³

et al. 2021

Preprint

View full text Add to dashboard Cite

<a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a>

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“…To achieve effective separation of gases, vapors, and liquids at a molecular level, the prime task is the choice of the membrane material [1][2][3][4][5][6]. Thus, membrane materials based on silicone rubbers became the most widely used for the isolation of light hydrocarbons from their mixtures with methane and/or other permanent gases [2,3,7,8].…”

mentioning

confidence: 99%

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

Грушевенко

¹

,

Borisov

²

,

Bakhtin

³

et al. 2020

Membr. Membr. Technol.

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The effect of the type and amount of crosslinking reagent on the gas transport properties of polyoctylmethylsiloxane (POMS) membranes is studied for the first time. 1,7-Octadiene (OD), divinyltetramethyldisiloxane (DMDS), and two samples of polydimethylsiloxane (PDMS 500, PDMS 25000 with the molecular weights of 500, 25000 g/mol, respectively) are used as crosslinking reagents. The permeability, diffusion, and solubility coefficients are measured for n-butane, methane, carbon dioxide, and nitrogen, and the corresponding ideal selectivities of permeability, diffusion, and solubility are calculated. It is shown that the optimal membrane properties of POMS with the use of DMDS as the crosslinking reagent are achieved at a concentration of DMDS of 3%: the maximum values of the permeability coefficient of n-butane (9400 Barrer) and ideal selectivity of permeability of n-butane/methane (26.1). A decrease in both the selectivity of solubility and selectivity of diffusion is observed for the n-butane/methane gas pair in the DMDS, PDMS 500, and PDMS 25000 series of crosslinking reagents with an increase in the fraction of a siloxane crosslinking reagent.

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Polyimides Based on the Diethyltoluenediamine Isomer Mixture: Synthesis and Gas Transport Properties

Cited by 11 publications

References 22 publications

Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

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

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