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Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding

Membrane separation technology is applied in natural gas processing, while a high-performance membrane is highly in demand. This paper considers the bright future of functionalized graphene oxide (GO) membranes in acid gas removal from natural gas. By molecular simulations, the adsorption and diffus...

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Autores principales: Liu, Quan, Yang, Zhonglian, Liu, Gongping, Sun, Longlong, Xu, Rong, Zhong, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693057/
https://www.ncbi.nlm.nih.gov/pubmed/36422148
http://dx.doi.org/10.3390/membranes12111155
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author Liu, Quan
Yang, Zhonglian
Liu, Gongping
Sun, Longlong
Xu, Rong
Zhong, Jing
author_facet Liu, Quan
Yang, Zhonglian
Liu, Gongping
Sun, Longlong
Xu, Rong
Zhong, Jing
author_sort Liu, Quan
collection PubMed
description Membrane separation technology is applied in natural gas processing, while a high-performance membrane is highly in demand. This paper considers the bright future of functionalized graphene oxide (GO) membranes in acid gas removal from natural gas. By molecular simulations, the adsorption and diffusion behaviors of several unary gases (N(2), CH(4), CO(2), H(2)S, and SO(2)) are explored in the 1,4-phenylenediamine-2-sulfonate (PDASA)-doped GO channels. Molecular insights show that the multilayer adsorption of acid gases evaluates well by the Redlich-Peterson model. A tiny amount of PDASA promotes the solubility coefficient of CO(2) and H(2)S, respectively, up to 4.5 and 5.3 mmol·g(−1)·kPa(−1), nearly 2.5 times higher than those of a pure GO membrane, which is due to the improved binding affinity, great isosteric heat, and hydrogen bonds, while N(2) and CH(4) only show single-layer adsorption with solubility coefficients lower than 0.002 mmol·g(−1)·kPa(−1), and their weak adsorption is insusceptible to PDASA. Although acid gas diffusivity in GO channels is inhibited below 20 × 10(−6) cm(2)·s(−1) by PDASA, the solubility coefficient of acid gases is certainly high enough to ensure their separation efficiency. As a result, the permeabilities (P) of acid gases and their selectivities (α) over CH(4) are simultaneously improved (P(CO2) = 7265.5 Barrer, α(CO2/CH4) = 95.7; P(()(H2S+CO2)) = 42075.1 Barrer, α(H2S/CH4) = 243.8), which outperforms most of the ever-reported membranes. This theoretical study gives a mechanistic understanding of acid gas separation and provides a unique design strategy to develop high-performance GO membranes toward efficient natural gas processing.
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spelling pubmed-96930572022-11-26 Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding Liu, Quan Yang, Zhonglian Liu, Gongping Sun, Longlong Xu, Rong Zhong, Jing Membranes (Basel) Article Membrane separation technology is applied in natural gas processing, while a high-performance membrane is highly in demand. This paper considers the bright future of functionalized graphene oxide (GO) membranes in acid gas removal from natural gas. By molecular simulations, the adsorption and diffusion behaviors of several unary gases (N(2), CH(4), CO(2), H(2)S, and SO(2)) are explored in the 1,4-phenylenediamine-2-sulfonate (PDASA)-doped GO channels. Molecular insights show that the multilayer adsorption of acid gases evaluates well by the Redlich-Peterson model. A tiny amount of PDASA promotes the solubility coefficient of CO(2) and H(2)S, respectively, up to 4.5 and 5.3 mmol·g(−1)·kPa(−1), nearly 2.5 times higher than those of a pure GO membrane, which is due to the improved binding affinity, great isosteric heat, and hydrogen bonds, while N(2) and CH(4) only show single-layer adsorption with solubility coefficients lower than 0.002 mmol·g(−1)·kPa(−1), and their weak adsorption is insusceptible to PDASA. Although acid gas diffusivity in GO channels is inhibited below 20 × 10(−6) cm(2)·s(−1) by PDASA, the solubility coefficient of acid gases is certainly high enough to ensure their separation efficiency. As a result, the permeabilities (P) of acid gases and their selectivities (α) over CH(4) are simultaneously improved (P(CO2) = 7265.5 Barrer, α(CO2/CH4) = 95.7; P(()(H2S+CO2)) = 42075.1 Barrer, α(H2S/CH4) = 243.8), which outperforms most of the ever-reported membranes. This theoretical study gives a mechanistic understanding of acid gas separation and provides a unique design strategy to develop high-performance GO membranes toward efficient natural gas processing. MDPI 2022-11-16 /pmc/articles/PMC9693057/ /pubmed/36422148 http://dx.doi.org/10.3390/membranes12111155 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Quan
Yang, Zhonglian
Liu, Gongping
Sun, Longlong
Xu, Rong
Zhong, Jing
Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title_full Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title_fullStr Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title_full_unstemmed Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title_short Functionalized GO Membranes for Efficient Separation of Acid Gases from Natural Gas: A Computational Mechanistic Understanding
title_sort functionalized go membranes for efficient separation of acid gases from natural gas: a computational mechanistic understanding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693057/
https://www.ncbi.nlm.nih.gov/pubmed/36422148
http://dx.doi.org/10.3390/membranes12111155
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