Literature DB >> 35082452

Molecular Methods for Assessing the Morphology, Topology, and Performance of Polyamide Membranes.

Riley Vickers1, Timothy M Weigand1, Cass T Miller1, Orlando Coronell1.   

Abstract

The molecular-scale morphology and topology of polyamide composite membranes determine the performance characteristics of these materials. However, molecular-scale simulations are computationally expensive and morphological and topological characterization of molecular structures are not well developed. Molecular dynamics simulation and analysis methods for the polymerization, hydration, and quantification of polyamide membrane structures were developed and compared to elucidate efficient approaches for producing and analyzing the polyamide structure. Polymerization simulations that omitted the reaction-phase solvent did not change the observed hydration, pore-size distribution, or water permeability, while improving the simulation efficiency. Pre-insertion of water into the aggregate pores (radius ≈ 4 Å) of dry domains enabled shorter hydration simulations and improved simulation scaling, without altering pore structure, properties, or performance. Medial axis and Minkowski functional methods were implemented to identify permeation pathways and quantify the polyamide morphology and topology, respectively. Better agreement between simulations and experimentally observed systems was accomplished by increasing the domain size rather than increasing the number of ensemble realizations of smaller systems. The largest domain hydrated was an order of magnitude larger by volume than the largest domain previously reported. This work identifies methods that can enable more efficient and meaningful fundamental modeling of membrane materials.

Entities:  

Keywords:  Minkowski functionals; hydration methods; molecular dynamics; polymerization; pore morphology and topology

Year:  2021        PMID: 35082452      PMCID: PMC8786217          DOI: 10.1016/j.memsci.2021.120110

Source DB:  PubMed          Journal:  J Memb Sci        ISSN: 0376-7388            Impact factor:   8.742


  16 in total

1.  Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation.

Authors:  Araz Jakalian; David B Jack; Christopher I Bayly
Journal:  J Comput Chem       Date:  2002-12       Impact factor: 3.376

Review 2.  On developing coarse-grained models for biomolecular simulation: a review.

Authors:  Sereina Riniker; Jane R Allison; Wilfred F van Gunsteren
Journal:  Phys Chem Chem Phys       Date:  2012-06-08       Impact factor: 3.676

3.  scikit-image: image processing in Python.

Authors:  Stéfan van der Walt; Johannes L Schönberger; Juan Nunez-Iglesias; François Boulogne; Joshua D Warner; Neil Yager; Emmanuelle Gouillart; Tony Yu
Journal:  PeerJ       Date:  2014-06-19       Impact factor: 2.984

4.  The future of seawater desalination: energy, technology, and the environment.

Authors:  Menachem Elimelech; William A Phillip
Journal:  Science       Date:  2011-08-05       Impact factor: 47.728

5.  Intrinsic Nanoscale Structure of Thin Film Composite Polyamide Membranes: Connectivity, Defects, and Structure-Property Correlation.

Authors:  Xiaoxiao Song; Bowen Gan; Saren Qi; Hao Guo; Chuyang Y Tang; Yong Zhou; Congjie Gao
Journal:  Environ Sci Technol       Date:  2020-03-05       Impact factor: 9.028

6.  Molecular Insights into the Composition-Structure-Property Relationships of Polyamide Thin Films for Reverse Osmosis Desalination.

Authors:  Hui Zhang; Mao See Wu; Kun Zhou; Adrian Wing-Keung Law
Journal:  Environ Sci Technol       Date:  2019-05-22       Impact factor: 9.028

7.  Aromatic Polyamide Reverse-Osmosis Membrane: An Atomistic Molecular Dynamics Simulation.

Authors:  Tao Wei; Lin Zhang; Haiyang Zhao; Heng Ma; Md Symon Jahan Sajib; Hua Jiang; Sohail Murad
Journal:  J Phys Chem B       Date:  2016-09-28       Impact factor: 2.991

8.  Dissecting the Role of Substrate on the Morphology and Separation Properties of Thin Film Composite Polyamide Membranes: Seeing Is Believing.

Authors:  Lu Elfa Peng; Zhikan Yao; Zhe Yang; Hao Guo; Chuyang Y Tang
Journal:  Environ Sci Technol       Date:  2020-05-20       Impact factor: 9.028

9.  All-atom empirical potential for molecular modeling and dynamics studies of proteins.

Authors:  A D MacKerell; D Bashford; M Bellott; R L Dunbrack; J D Evanseck; M J Field; S Fischer; J Gao; H Guo; S Ha; D Joseph-McCarthy; L Kuchnir; K Kuczera; F T Lau; C Mattos; S Michnick; T Ngo; D T Nguyen; B Prodhom; W E Reiher; B Roux; M Schlenkrich; J C Smith; R Stote; J Straub; M Watanabe; J Wiórkiewicz-Kuczera; D Yin; M Karplus
Journal:  J Phys Chem B       Date:  1998-04-30       Impact factor: 2.991

10.  Molecular Dynamics Study of Carbon Nanotubes/Polyamide Reverse Osmosis Membranes: Polymerization, Structure, and Hydration.

Authors:  Takumi Araki; Rodolfo Cruz-Silva; Syogo Tejima; Kenji Takeuchi; Takuya Hayashi; Shigeki Inukai; Toru Noguchi; Akihiko Tanioka; Takeyuki Kawaguchi; Mauricio Terrones; Morinobu Endo
Journal:  ACS Appl Mater Interfaces       Date:  2015-10-27       Impact factor: 9.229

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