1
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Zhu H, Szymczyk A, Ghoufi A. Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future. DISCOVER NANO 2024; 19:91. [PMID: 38771417 PMCID: PMC11109084 DOI: 10.1186/s11671-024-04020-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024]
Abstract
Nanofiltration (NF) and reverse osmosis (RO) processes are physical separation technologies used to remove contaminants from liquid streams by employing dense polymer-based membranes with nanometric voids that confine fluids at the nanoscale. At this level, physical properties such as solvent and solute permeabilities are intricately linked to molecular interactions. Initially, numerous studies focused on developing macroscopic transport models to gain insights into separation properties at the nanometer scale. However, continuum-based models have limitations in nanoconfined situations that can be overcome by force field molecular simulations. Continuum-based models heavily rely on bulk properties, often neglecting critical factors like liquid structuring, pore geometry, and molecular/chemical specifics. Molecular/mesoscale simulations, while encompassing these details, often face limitations in time and spatial scales. Therefore, achieving a comprehensive understanding of transport requires a synergistic integration of both approaches through a multiscale approach that effectively combines and merges both scales. This review aims to provide a comprehensive overview of the state-of-the-art in multiscale modeling of transport through NF/RO membranes, spanning from the nanoscale to continuum media.
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Affiliation(s)
- Haochen Zhu
- State Key Laboratory of Pollution Control and Resources Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, 1239 Siping Rd., Shanghai, 200092, China.
| | - Anthony Szymczyk
- CNRS, ISCR (Institut des Sciences Chimiques de Rennes) - UMR 6226, Univ Rennes, 35000, Rennes, France.
| | - Aziz Ghoufi
- CNRS, ICMPE (Institut de Chimie et des Matériaux Paris-Est) - UMR 7182, Univ Paris-East Creteil, 94320, Thiais, France.
- CNRS, IPR (Institut de Physique de Rennes) - UMR 6251, Univ Rennes, 35000, Rennes, France.
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2
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Xian W, Zhan YS, Maiti A, Saab AP, Li Y. Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials. Polymers (Basel) 2024; 16:1387. [PMID: 38794580 PMCID: PMC11125212 DOI: 10.3390/polym16101387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/06/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer-particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer-particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.
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Affiliation(s)
- Weikang Xian
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (W.X.); (Y.-S.Z.)
| | - You-Shu Zhan
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (W.X.); (Y.-S.Z.)
| | - Amitesh Maiti
- Lawrence Livermore National Laboratory, Livermore, CA 94550, USA; (A.M.); (A.P.S.)
| | - Andrew P. Saab
- Lawrence Livermore National Laboratory, Livermore, CA 94550, USA; (A.M.); (A.P.S.)
| | - Ying Li
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; (W.X.); (Y.-S.Z.)
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3
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Zhang XZ, Shi R, Lu ZY, Qian HJ. Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties. JACS AU 2024; 4:1018-1030. [PMID: 38559727 PMCID: PMC10976574 DOI: 10.1021/jacsau.3c00756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well.
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Affiliation(s)
| | | | - Zhong-Yuan Lu
- State Key Laboratory of Supramolecular
Structure and Materials, Institute of Theoretical Chemistry, College
of Chemistry, Jilin University, Changchun 130021, China
| | - Hu-Jun Qian
- State Key Laboratory of Supramolecular
Structure and Materials, Institute of Theoretical Chemistry, College
of Chemistry, Jilin University, Changchun 130021, China
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4
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Lesniewski MC, Noid WG. Insight into the Density-Dependence of Pair Potentials for Predictive Coarse-Grained Models. J Phys Chem B 2024; 128:1298-1316. [PMID: 38271676 DOI: 10.1021/acs.jpcb.3c06890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
We investigate the temperature- and density-dependence of effective pair potentials for 1-site coarse-grained (CG) models of two industrial solvents, 1,4-dioxane and tetrahydrofuran. We observe that the calculated pair potentials are much more sensitive to density than to temperature. The generalized-Yvon-Born-Green framework reveals that this striking density-dependence reflects corresponding variations in the many-body correlations that determine the environment-mediated indirect contribution to the pair mean force. Moreover, we demonstrate, perhaps surprisingly, that this density-dependence is not important for accurately modeling the intermolecular structure. Accordingly, we adopt a density-independent interaction potential and transfer the density-dependence of the calculated pair potentials into a configuration-independent volume potential. Furthermore, we develop a single global potential that accurately models the intermolecular structure and pressure-volume equation of state across a very wide range of liquid state points. Consequently, this work provides fundamental insight into the density-dependence of effective pair potentials and also provides a significant step toward developing predictive CG models for efficiently modeling industrial solvents.
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Affiliation(s)
- Maria C Lesniewski
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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5
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Chen Q, Liu Z, Huang Y, Hu A, Huang W, Zhang L, Cui L, Liu J. Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:17088-17099. [PMID: 37983181 DOI: 10.1021/acs.langmuir.3c01878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Natural rubber (NR) with excellent mechanical properties, mainly attributed to its strain-induced crystallization (SIC), has garnered significant scientific and technological interest. With the aid of molecular dynamics (MD) simulations, we can investigate the impacts of crucial structural elements on SIC on the molecular scale. Nonetheless, the computational complexity and time-consuming nature of this high-precision method constrain its widespread application. The integration of machine learning with MD represents a promising avenue for enhancing the speed of simulations while maintaining accuracy. Herein, we developed a crystallinity algorithm tailored to the SIC properties of natural rubber materials. With the data enhancement algorithm, the high evaluation value of the prediction model ensures the accuracy of the computational simulation results. In contrast to the direct utilization of small sample prediction algorithms, we propose a novel concept grounded in feature engineering. The proposed machine learning (ML) methodology consists of (1) An eXtreme Gradient Boosting (XGB) model to predict the crystallinity of NR; (2) a generative adversarial network (GAN) data augmentation algorithm to optimize the utilization of the limited training data, which is utilized to construct the XGB prediction model; (3) an elaboration of the effects induced by phospholipid and protein percentage (ω), hydrogen bond strength (εH), and non-hydrogen bond strength (εNH) of natural rubber materials with crystallinity prediction under dynamic conditions are analyzed by employing weight integration with feature importance analysis. Eventually, we succeeded in concluding that εH has the most significant effect on the strain-induced crystallinity, followed by ω and finally εNH.
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Affiliation(s)
- Qionghai Chen
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Zhanjie Liu
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Yongdi Huang
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Anwen Hu
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Wanhui Huang
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Liqun Zhang
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
| | - Lihong Cui
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
| | - Jun Liu
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China
- Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing100029, People's Republic of China
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6
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Laurent H, Hughes MDG, Walko M, Brockwell DJ, Mahmoudi N, Youngs TGA, Headen TF, Dougan L. Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering. Biomacromolecules 2023; 24:4869-4879. [PMID: 37874935 PMCID: PMC10646990 DOI: 10.1021/acs.biomac.3c00583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/03/2023] [Indexed: 10/26/2023]
Abstract
Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.
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Affiliation(s)
- Harrison Laurent
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
| | - Matt D. G. Hughes
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Martin Walko
- School
of Chemistry, University of Leeds, Leeds, United
Kingdom, LS2 9JT
| | - David J. Brockwell
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
| | - Najet Mahmoudi
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Tristan G. A. Youngs
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Thomas F. Headen
- ISIS
Neutron and Muon Source, Rutherford Appleton
Laboratory, Harwell Oxford, Didcot, United Kingdom, OX11 0QX
| | - Lorna Dougan
- School
of Physics and Astronomy, University of
Leeds, Leeds, United Kingdom, LS2
9JT
- Astbury
Centre for Structural Molecular Biology, University of Leeds, Leeds, United Kingdom LS2
9JT
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7
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Wang Y, Mou X, Ji Y, Pan F, Li S. Interaction of Macromolecular Chain with Phospholipid Membranes in Solutions: A Dissipative Particle Dynamics Simulation Study. Molecules 2023; 28:5790. [PMID: 37570760 PMCID: PMC10420874 DOI: 10.3390/molecules28155790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The interaction between macromolecular chains and phospholipid membranes in aqueous solution was investigated using dissipative particle dynamics simulations. Two cases were considered, one in which the macromolecular chains were pulled along parallel to the membrane surfaces and another in which they were pulled vertical to the membrane surfaces. Several parameters, including the radius of gyration, shape factor, particle number, and order parameter, were used to investigate the interaction mechanisms during the dynamics processes by adjusting the pulling force strength of the chains. In both cases, the results showed that the macromolecular chains undergo conformational transitions from a coiled to a rod-like structure. Furthermore, the simulations revealed that the membranes can be damaged and repaired during the dynamic processes. The role of the pulling forces and the adsorption interactions between the chains and membranes differed in the parallel and perpendicular pulling cases. These findings contribute to our understanding of the interaction mechanisms between macromolecules and membranes, and they may have potential applications in biology and medicine.
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Affiliation(s)
- Yuane Wang
- Department of Physics, Wenzhou University, Wenzhou 325035, China; (Y.W.); (X.M.); (Y.J.)
| | - Xuankang Mou
- Department of Physics, Wenzhou University, Wenzhou 325035, China; (Y.W.); (X.M.); (Y.J.)
| | - Yongyun Ji
- Department of Physics, Wenzhou University, Wenzhou 325035, China; (Y.W.); (X.M.); (Y.J.)
| | - Fan Pan
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325035, China
| | - Shiben Li
- Department of Physics, Wenzhou University, Wenzhou 325035, China; (Y.W.); (X.M.); (Y.J.)
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8
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Ricci E, Vergadou N. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers. J Phys Chem B 2023; 127:2302-2322. [PMID: 36888553 DOI: 10.1021/acs.jpcb.2c06354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.
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Affiliation(s)
- Eleonora Ricci
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
- Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
| | - Niki Vergadou
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
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9
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Khan P, Kaushik R, Jayaraj A. Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer-Nanoparticle Hybrid Systems. ACS OMEGA 2022; 7:47567-47586. [PMID: 36591142 PMCID: PMC9798744 DOI: 10.1021/acsomega.2c06248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer-nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer-nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer-nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field.
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Affiliation(s)
- Parvez Khan
- Department
of Chemical Engineering, Aligarh Muslim
University, Aligarh202002, India
| | - Rahul Kaushik
- Laboratory
for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa230-0045, Japan
| | - Abhilash Jayaraj
- Department
of Chemistry, Wesleyan University, Middletown, Connecticut06459, United States
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10
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Schmid F. Understanding and Modeling Polymers: The Challenge of Multiple Scales. ACS POLYMERS AU 2022. [DOI: 10.1021/acspolymersau.2c00049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Friederike Schmid
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128Mainz, Germany
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11
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Heil CM, Jayaraman A. Polymer solution structure and dynamics within pores of hexagonally close-packed nanoparticles. SOFT MATTER 2022; 18:8175-8187. [PMID: 36263835 DOI: 10.1039/d2sm01102f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Using coarse-grained molecular dynamics simulations, we examine structure and dynamics of polymer solutions under confinement within the pores of a hexagonally close-packed (HCP) nanoparticle system with nanoparticle diameter fifty times that of the polymer Kuhn segment size. We model a condition where the polymer chain is in a good solvent (i.e., polymer-polymer interaction is purely repulsive and polymer-solvent and solvent-solvent interactions are attractive) and the polymer-nanoparticle and solvent-nanoparticle interactions are purely repulsive. We probe three polymer lengths (N = 10, 114, and 228 Kuhn segments) and three solution concentrations (1, 10, and 25%v) to understand how the polymer chain conformations and chain center-of-mass diffusion change under confinement within the pores of the HCP nanoparticle structure from those seen in bulk. The known trend of bulk polymer Rg2 decreasing with increasing concentration no longer holds when confined in the pores of HCP nanoparticle structure; for example, for the 114-mer, the HCP 〈Rg2〉 at 1%v concentration is lower than HCP 〈Rg2〉 at 10%v concentration. The 〈Rg2〉 of the 114-mer and 228-mer exhibit the largest percent decline going from bulk to HCP at the 1%v concentration and the smallest percent decline at the 25%v concentration. We also provide insight into how the confinement ratio (CR) of polymer chain size to pore size within tetrahedral and octahedral pores in the HCP arrangement of nanoparticles affects the chain conformation and diffusion at various concentrations. At the same concentration, the N = 114 has significantly more movement between pores than the N = 228 chains. For the N = 114 polymer, the diffusion between pores (i.e., inter-pore diffusion) accelerates the overall diffusion rate for the confined HCP system while for the N = 228 polymer, the polymer diffusion in the entire HCP is dominated by the diffusion within the tetrahedral or octahedral pores with minor contributions from inter-pore diffusion. These findings augment the fundamental understanding of macromolecular diffusion through large, densely packed nanoparticle assemblies and are relevant to research focused on fabrication of polymer composite materials for chemical separations, storage, optics, and photonics. We perform coarse-grained molecular dynamics simulations to understand structure and dynamics of polymer solutions under confinement within hexagonal close packed nanoparticles with radii much larger than the polymer chain's bulk radius of gyration.
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Affiliation(s)
- Christian M Heil
- Department of Chemical and Biomolecular Engineering, 150 Academy St., University of Delaware, Newark, DE 19716, USA.
| | - Arthi Jayaraman
- Department of Chemical and Biomolecular Engineering, 150 Academy St., University of Delaware, Newark, DE 19716, USA.
- Department of Materials Science and Engineering, 201 DuPont Hall, University of Delaware, Newark, DE 19716, USA
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12
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Sun L, Pan F, Li S. Self-Assembly of Lipid Mixtures in Solutions: Structures, Dynamics Processes and Mechanical Properties. MEMBRANES 2022; 12:membranes12080730. [PMID: 35893448 PMCID: PMC9394357 DOI: 10.3390/membranes12080730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 02/01/2023]
Abstract
The self-assembly of lipid mixtures in aqueous solution was investigated by dissipative particle dynamics simulation. Two types of lipid molecules were modelled, where three mixed structures, i.e., the membrane, perforated membrane and vesicle, were determined in the self-assembly processes. Phase behaviour was investigated by using the phase diagrams based on the tail chain lengths for the two types of lipids. Several parameters, such as chain number and average radius of gyration, were employed to explore the structural formations of the membrane and perforated membrane in the dynamic processes. Interface tension was used to demonstrate the mechanical properties of the membrane and perforated membrane in the equilibrium state and dynamics processes. Results help us to understand the self-assembly mechanism of the biomolecule mixtures, which has a potential application for designing the lipid molecule-based bio-membranes in solutions.
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Affiliation(s)
| | - Fan Pan
- Correspondence: (F.P.); (S.L.)
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13
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Peroukidis SD, Stott IP, Mavrantzas VG. Coarse-Grained Model Incorporating Short- and Long-Range Effective Potentials for the Fast Simulation of Micelle Formation in Solutions of Ionic Surfactants. J Phys Chem B 2022; 126:5555-5569. [PMID: 35838193 DOI: 10.1021/acs.jpcb.2c02751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A coarse-grained model comprising short- and long-range effective potentials, parametrized with the iterative Boltzmann inversion (IBI) method, is presented for capturing micelle formation in aqueous solutions of ionic surfactants using as a model system sodium dodecyl sulfate (SDS). In the coarse-grained (CG) model, each SDS molecule is represented as a sequence of four beads while each water molecule is modeled as a single bead. The proposed CG scheme involves ten potential energy functions: four of them describe bonded interactions and control the distribution functions of intramolecular degrees of freedom (bond lengths, valence angles, and dihedrals) along an SDS molecule while the other six account for intermolecular interactions between pairs of SDS and water beads and control the radial distribution functions. The nonbonded effective potentials between coarse-grained SDS molecules extend up to about 12 nm and capture structural and morphological features of the micellar solution both at short and long distances. The long-range component of these potentials, in particular, captures correlations between surfactant molecules belonging to different micelles and is essential to describe ordering associated with micelle formation. A new strategy is introduced for determining the effective potentials through IBI by using information (target distribution functions) extracted from independent atomistic simulations of a micellar reference system (a salt-free SDS solution at total surfactant concentration cT equal to 103 mM, temperature T equal to 300 K, and pressure P equal to 1 atm) obtained through a multiscale approach described in an earlier study. It employs several optimization steps for bonded and nonbonded interactions and a gradual parametrization of the short- and long-range components of the latter, followed by reparametrization of the bonded ones. The proposed CG model can reproduce remarkably accurately the microstructure and morphology of the reference system within only a few hours of computational time. It is therefore very promising for future studies of structural and morphological behavior of various liquid surfactant formulations.
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Affiliation(s)
- Stavros D Peroukidis
- Department of Chemical Engineering, University of Patras and FORTH-ICE/HT, GR 26504, Patras, Greece
| | - Ian P Stott
- Unilever Research and Development Port Sunlight, Bebington CH63 3JW, United Kingdom
| | - Vlasis G Mavrantzas
- Department of Chemical Engineering, University of Patras and FORTH-ICE/HT, GR 26504, Patras, Greece.,Particle Technology Laboratory, Department of Mechanical and Process Engineering, ETH Zürich, CH-8092 Zürich, Switzerland
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14
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Nguyen HTL, Huang DM. Systematic bottom-up molecular coarse-graining via force and torque matching using anisotropic particles. J Chem Phys 2022; 156:184118. [DOI: 10.1063/5.0085006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We derive a systematic and general method for parametrizing coarse-grained molecular models consisting of anisotropic particles from fine-grained (e.g. all-atom) models for condensed-phase molecular dynamics simulations. The method, which we call anisotropic force-matching coarse-graining (AFM-CG), is based on rigorous statistical mechanical principles, enforcing consistency between the coarse-grained and fine-grained phase-space distributions to derive equations for the coarse-grained forces, torques, masses, and moments of inertia in terms of properties of a condensed-phase fine-grained system. We verify the accuracy and efficiency of the method by coarse-graining liquid-state systems of two different anisotropic organic molecules, benzene and perylene, and show that the parametrized coarse-grained models more accurately describe properties of these systems than previous anisotropic coarse-grained models parametrized using other methods that do not account for finite-temperature and many-body effects on the condensed-phase coarse-grained interactions. The AFM-CG method will be useful for developing accurate and efficient dynamical simulation models of condensed-phase systems of molecules consisting of large, rigid, anisotropic fragments, such as liquid crystals, organic semiconductors, and nucleic acids.
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15
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Pugar JA, Gang C, Huang C, Haider KW, Washburn NR. Predicting Young's Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2022; 14:16568-16581. [PMID: 35353501 DOI: 10.1021/acsami.1c24715] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Using a data set of 63 industrially relevant and complex elastomers, we demonstrate that accurate machine learning predictions are possible when monomer chemistry is used to estimate interactions at interchain length scales. Here, these features were used to accurately (r2 = 0.91) predict the Young's modulus of polyurethane and polyurethane-urea elastomers. Furthermore, by a query of the trained model for compositions that yield a target modulus within the range of accessible values, the capabilities of using this methodology as a design tool are demonstrated. The presented methodology could become increasingly useful in building models for materials with small data sets and may guide the interpretation of the underlying physicochemical forces.
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Affiliation(s)
- Joseph A Pugar
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Calvin Gang
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Christine Huang
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Karl W Haider
- Covestro LLC, 1 Covestro Circle, Pittsburgh, Pennsylvania 15205, United States
| | - Newell R Washburn
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
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16
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Larson RG, Van Dyk AK, Chatterjee T, Ginzburg VV. Associative Thickeners for Waterborne Paints: Structure, Characterization, Rheology, and Modeling. Prog Polym Sci 2022. [DOI: 10.1016/j.progpolymsci.2022.101546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Sheng J, Chen W, Cui K, Li L. Polymer crystallization under external flow. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:036601. [PMID: 35060493 DOI: 10.1088/1361-6633/ac4d92] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
The general aspects of polymer crystallization under external flow, i.e., flow-induced crystallization (FIC) from fundamental theoretical background to multi-scale characterization and modeling results are presented. FIC is crucial for modern polymer processing, such as blowing, casting, and injection modeling, as two-third of daily-used polymers is crystalline, and nearly all of them need to be processed before final applications. For academics, the FIC is intrinsically far from equilibrium, where the polymer crystallization behavior is different from that in quiescent conditions. The continuous investigation of crystallization contributes to a better understanding on the general non-equilibrium ordering in condensed physics. In the current review, the general theories related to polymer nucleation under flow (FIN) were summarized first as a preliminary knowledge. Various theories and models, i.e., coil-stretch transition and entropy reduction model, are briefly presented together with the modified versions. Subsequently, the multi-step ordering process of FIC is discussed in detail, including chain extension, conformational ordering, density fluctuation, and final perfection of the polymer crystalline. These achievements for a thorough understanding of the fundamental basis of FIC benefit from the development of various hyphenated rheometer, i.e., rheo-optical spectroscopy, rheo-IR, and rheo-x-ray scattering. The selected experimental results are introduced to present efforts on elucidating the multi-step and hierarchical structure transition during FIC. Then, the multi-scale modeling methods are summarized, including micro/meso scale simulation and macroscopic continuum modeling. At last, we briefly describe our personal opinions related to the future directions of this field, aiming to ultimately establish the unified theory of FIC and promote building of the more applicable models in the polymer processing.
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Affiliation(s)
- Junfang Sheng
- National Synchrotron Radiation Laboratory, Anhui Provincial Engineering Laboratory of Advanced Functional Polymer Film, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Wei Chen
- National Synchrotron Radiation Laboratory, Anhui Provincial Engineering Laboratory of Advanced Functional Polymer Film, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Kunpeng Cui
- National Synchrotron Radiation Laboratory, Anhui Provincial Engineering Laboratory of Advanced Functional Polymer Film, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Liangbin Li
- National Synchrotron Radiation Laboratory, Anhui Provincial Engineering Laboratory of Advanced Functional Polymer Film, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, People's Republic of China
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18
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Wang J, in ’t Veld PJ, Robbins MO, Ge T. Effects of Coarse-Graining on Molecular Simulation of Craze Formation in Polymer Glass. Macromolecules 2022. [DOI: 10.1021/acs.macromol.1c01969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jiuling Wang
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | | | - Mark O. Robbins
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Ting Ge
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
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19
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Nguyen D, Tao L, Li Y. Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design. Front Chem 2022; 9:820417. [PMID: 35141207 PMCID: PMC8819075 DOI: 10.3389/fchem.2021.820417] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/31/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics—polymeric configuration characterization, feed-forward property prediction, and inverse design—in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.
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Affiliation(s)
- Danh Nguyen
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
| | - Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
- Polymer Program, Institute of Materials Science, University of Connecticut, Mansfield, CT, United States
- *Correspondence: Ying Li,
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20
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Sattor AK, Pervaje AK, Pasquinelli MA, Khan SA, Santiso EE. Multiscale Constitutive Modeling of the Mechanical Properties of Polypropylene Fibers from Molecular Simulation Data. Macromolecules 2022. [DOI: 10.1021/acs.macromol.1c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Amulya K. Sattor
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Amulya K. Pervaje
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Melissa A. Pasquinelli
- Fiber and Polymer Science Program, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Saad A. Khan
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erik E. Santiso
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
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21
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Rissanou A, Chazirakis A, Polinska P, Burkhart C, Doxastakis M, Harmandaris V. Polybutadiene Copolymers via Atomistic and Systematic Coarse-Grained Simulations. Macromolecules 2022. [DOI: 10.1021/acs.macromol.1c01939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Anastassia Rissanou
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
| | - Antonis Chazirakis
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
| | | | - Craig Burkhart
- The Goodyear Tire and Rubber Company, 142 Goodyear Blvd., 44305 Akron, Ohio, United States
| | - Manolis Doxastakis
- Department of Chemical and Biomolecular Engineering, University of Tennessee, 37996 Knoxville, Tennessee, United States
| | - Vagelis Harmandaris
- Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas, (FORTH), IACM/FORTH, GR-71110 Heraklion, Greece
- Department of Mathematics and Applied Mathematics, University of Crete, GR-71409 Heraklion, Crete, Greece
- Computation-Based Science and Technology Research Center, The Cyprus Institute, 2121 Nicosia, Cyprus
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22
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Molecular simulation of the viscosity of asymmetric dense mixtures. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.117052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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23
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Influence of the Filler Particles' Surface Morphology on the Polyurethane Matrix's Structure Formation in the Composite. Polymers (Basel) 2021; 13:polym13223864. [PMID: 34833164 PMCID: PMC8624056 DOI: 10.3390/polym13223864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/03/2022] Open
Abstract
This article presents the surface morphology effect of silicon carbide (SiC) particles on the polyurethane binder’s structure formation in a dispersed-filled composite. The difference in the morphology and surface relief of filler particles was ensured by the implementation of plasma chemical modification. As a result of this modification, the filler consisted of core-shell particles characterized by a SiC core and a carbon shell (SiC@C), as well as a carbon shell decorated with silicon nanoparticles (SiC@C/SiNP) or nanos (SiC@C/SiNW). The study of the relaxation properties of polyurethane composites has shown that the strongest limiting effect on the molecular mobility of boundary layer’s chain segments is exerted by a highly developed surface with a complex relief of SiC@C/SiNP and SiC@C/SiNW particles. An empirical method was proposed to find the polymer fractions spent on the formation of the boundary, transition and bulk layers of the polymer matrix in the composite. It was shown that the morphology of the filler particles’ surface does not affect the dependence of the boundary layer thickness on the filler’s volume fraction. However, with an increase in the degree of surface development, the boundary layer thickness decreases.
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24
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Reis M, Gusev F, Taylor NG, Chung SH, Verber MD, Lee YZ, Isayev O, Leibfarth FA. Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis. J Am Chem Soc 2021; 143:17677-17689. [PMID: 34637304 PMCID: PMC10833148 DOI: 10.1021/jacs.1c08181] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.
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Affiliation(s)
- Marcus Reis
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Filipp Gusev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Nicholas G Taylor
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Sang Hun Chung
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Matthew D Verber
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Yueh Z Lee
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Frank A Leibfarth
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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25
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Dhamankar S, Webb MA. Chemically specific coarse‐graining of polymers: Methods and prospects. JOURNAL OF POLYMER SCIENCE 2021. [DOI: 10.1002/pol.20210555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Satyen Dhamankar
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
| | - Michael A. Webb
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
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26
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Hou M, Qi W, Li L, Xu R, Xue J, Zhang Y, Song C, Wang T. Carbon molecular sieve membrane with tunable microstructure for CO2 separation: Effect of multiscale structures of polyimide precursors. J Memb Sci 2021. [DOI: 10.1016/j.memsci.2021.119541] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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27
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Sha W, Fu J, Guo F. Wetting characteristics of polymer adhesives with different chain bending stiffness. HIGH PERFORM POLYM 2021. [DOI: 10.1177/09540083211035016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Polymer adhesives are widely used in daily applications and in industry owing to their flexibility and overall non-toxicity, particularly in interfacial adhesion. The spreading of polymer adhesives on adherend is one of the essential considerations for the interfacial adhesion of polymer adhesives, which is strongly related to their wetting behaviors. While relationships between polymer microstructure and adhesion have been investigated in previous studies, it remains challenging to unveil the effect of polymer microstructure on wettability. To address this issue, here we utilize coarse-grained molecular dynamics (CGMD) simulations to systematically elucidate how the wettability of a polymer adhesive droplet on a surface depends on bending stiffness. The wetting dynamics and the contact angle are studied to show the evolution of morphology of droplets during the wetting process. The results indicate the wettability is weakened by the increase of bending stiffness of polymer chain. Detailed thermodynamic property analysis is further conducted, revealing that the adhesion between the polymer droplet and substrate deteriorates due to the decline of wettability. Interestingly, we observe such deterioration becomes more significant by both increasing the temperature and decreasing the bending stiffness. Our study sheds light on the dependence of chain bending stiffness and temperature on the wetting behavior of polymer adhesive droplets, and offers insights, which, upon experimental validation can then be used for the design of adhesives or hydrogels.
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Affiliation(s)
- Wenhao Sha
- School of Naval Architecture, Ocean and Civil Engineering (State Key Laboratory of Ocean Engineering), Shanghai Jiao Tong University, Shanghai, China
| | - Jimin Fu
- Nanotechnology Center, Institute of Textiles & Clothing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Fenglin Guo
- School of Naval Architecture, Ocean and Civil Engineering (State Key Laboratory of Ocean Engineering), Shanghai Jiao Tong University, Shanghai, China
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28
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da Costa CHS, Dos Santos AM, Alves CN, Martí S, Moliner V, Santana K, Lameira J. Assessment of the PETase conformational changes induced by poly(ethylene terephthalate) binding. Proteins 2021; 89:1340-1352. [PMID: 34075621 DOI: 10.1002/prot.26155] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/13/2021] [Accepted: 05/29/2021] [Indexed: 12/12/2022]
Abstract
Recently, a bacterium strain of Ideonella sakaiensis was identified with the uncommon ability to degrade the poly(ethylene terephthalate) (PET). The PETase from I. sakaiensis strain 201-F6 (IsPETase) catalyzes the hydrolysis of PET converting it to mono(2-hydroxyethyl) terephthalic acid (MHET), bis(2-hydroxyethyl)-TPA (BHET), and terephthalic acid (TPA). Despite the potential of this enzyme for mitigation or elimination of environmental contaminants, one of the limitations of the use of IsPETase for PET degradation is the fact that it acts only at moderate temperature due to its low thermal stability. Besides, molecular details of the main interactions of PET in the active site of IsPETase remain unclear. Herein, molecular docking and molecular dynamics (MD) simulations were applied to analyze structural changes of IsPETase induced by PET binding. Results from the essential dynamics revealed that the β1-β2 connecting loop is very flexible. This loop is located far from the active site of IsPETase and we suggest that it can be considered for mutagenesis to increase the thermal stability of IsPETase. The free energy landscape (FEL) demonstrates that the main change in the transition between the unbound to the bound state is associated with the β7-α5 connecting loop, where the catalytic residue Asp206 is located. Overall, the present study provides insights into the molecular binding mechanism of PET into the IsPETase structure and a computational strategy for mapping flexible regions of this enzyme, which can be useful for the engineering of more efficient enzymes for recycling plastic polymers using biological systems.
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Affiliation(s)
| | - Alberto M Dos Santos
- Centro de Ciências Exatas e Tecnologias, Federal University of Maranhão, São Luis, Maranhão, Brazil
| | - Cláudio Nahum Alves
- Institute of Natural Sciences, Federal University of Pará, Belém, Pará, Brazil
| | - Sérgio Martí
- Institute of Advanced Materials (INAM), Universitat Jaume I, Castellón, Spain
| | - Vicent Moliner
- Institute of Advanced Materials (INAM), Universitat Jaume I, Castellón, Spain
| | - Kauê Santana
- Institute of Biodiversity, Federal University of Western Pará, Santarém, Pará, Brazil
| | - Jerônimo Lameira
- Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil
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29
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Mousavi AA, Arash B, Rolfes R. Optimization assisted coarse-grained modeling of agglomerated nanoparticle reinforced thermosetting polymers. POLYMER 2021. [DOI: 10.1016/j.polymer.2021.123741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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30
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Ge T, Wang J, Robbins MO. Effects of Coarse-Graining on Molecular Simulations of Mechanical Properties of Glassy Polymers. Macromolecules 2021. [DOI: 10.1021/acs.macromol.0c02467] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ting Ge
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Jiuling Wang
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Mark O. Robbins
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, United States
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31
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Radhakrishnan R. A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects. AIChE J 2021; 67:e17026. [PMID: 33790479 PMCID: PMC7988612 DOI: 10.1002/aic.17026] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/09/2020] [Accepted: 08/13/2020] [Indexed: 01/14/2023]
Abstract
Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.
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Affiliation(s)
- Ravi Radhakrishnan
- Department of Chemical and Biomolecular EngineeringPenn Institute for Computational Science, University of PennsylvaniaPhiladelphiaPhiladelphiaUSA
- Department of BioengineeringPenn Institute for Computational Science, University of PennsylvaniaPhiladelphiaPhiladelphiaUSA
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32
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Yue T, Li S, Zhang Z, Chen Y, Zhang L, Liu J. Optimizing the heterogeneous network structure to achieve polymer nanocomposites with excellent mechanical properties. Phys Chem Chem Phys 2021; 23:4437-4452. [PMID: 33595012 DOI: 10.1039/d0cp06532c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Designing and optimizing the polymer network structure at the molecular level to manipulate its mechanical properties are of great scientific significance. Although heterogeneous multi-network structures have been extensively investigated, little effort has been devoted to investigating heterogeneous single-networks with a well-defined interface. Herein, through coarse-grained molecular dynamics simulation, we successfully fabricated a heterogeneous single-network, which was divided into several regions with different crosslink densities. Firstly, we found that there is an optimal crosslink density ratio between high and low crosslink density regions to obtain the best stress-strain behavior. Secondly, the effect of the regularity of the network topology (by changing the distribution of two-phase regions) on mechanical properties was also studied. It was clearly observed that the polymer network showed better elastic response and mechanical properties as the distribution of two-phase regions became uniform. Finally, we investigated the effect of the selective distribution of nanoparticles (NPs) on mechanical properties by introducing NPs into a pre-designed multiphase network. Results showed that the selective distribution of NPs in the high crosslink density region had a more significant effect on the mechanical reinforcement. Generally, our simulated results may provide some guidelines to design polymer network structures to achieve high-performance polymer nanocomposites with excellent mechanical properties.
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Affiliation(s)
- Tongkui Yue
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, People's Republic of China.
| | - Sai Li
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, People's Republic of China.
| | - Zhiyu Zhang
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, People's Republic of China.
| | - Yulong Chen
- College of Materials Science and Engineering, Zhejiang University of Technology, People's Republic of China
| | - Liqun Zhang
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, People's Republic of China. and Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, People's Republic of China and State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, People's Republic of China
| | - Jun Liu
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, People's Republic of China. and Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, People's Republic of China and State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, People's Republic of China
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33
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Darabi E, Itskov M. A generalized tube model of rubber elasticity. SOFT MATTER 2021; 17:1675-1684. [PMID: 33367440 DOI: 10.1039/d0sm02055a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the present paper, a new type of micro-mechanically motivated chain network model for rubber-like materials is proposed. The model captures topological constraints of polymer network chains, in particular, entanglements. The model demonstrates how the local molecular packing constraints modify under deformation and shows the impact of these changes on the macroscopic elasticity of the material. To this end, we combine concepts of a confining tube and a slip-link (reptation) model. In these models, entanglements of polymer chains play an important role. The nature of entanglements is discussed, and relationships governing entanglements are formulated in terms of molecular physics. In the context of nonlinear elasticity, we apply a non-affine concept which captures the liquid-like behavior of polymer networks at smaller scales in a more realistic way. Model predictions show good agreement with experimental results from uniaxial and biaxial tension tests.
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Affiliation(s)
- Ehsan Darabi
- Department of Continuum Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany.
| | - Mikhail Itskov
- Department of Continuum Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany.
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34
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Abstract
A multiscale approach to discrete element modeling is presented. A distinctive feature of the method is that each macroscopic discrete element has an associated atomic sample representing the material’s atomic structure. The dynamics of the elements on macro and micro levels are described by systems of ordinary differential equations, which are solved in a self-consistent manner. A full cycle of multiscale simulations is applied to polycrystalline silicon. Macroscale elastic properties of silicon were obtained only using data extracted from the quantum mechanical properties. The results of computational experiments correspond well to the reference data.
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35
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Ye H, Xian W, Li Y. Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges. ACS OMEGA 2021; 6:1758-1772. [PMID: 33521417 PMCID: PMC7841771 DOI: 10.1021/acsomega.0c05321] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/04/2021] [Indexed: 05/02/2023]
Abstract
Machine learning (ML) has emerged as one of the most powerful tools transforming all areas of science and engineering. The nature of molecular dynamics (MD) simulations, complex and time-consuming calculations, makes them particularly suitable for ML research. This review article focuses on recent advancements in developing efficient and accurate coarse-grained (CG) models using various ML methods, in terms of regulating the coarse-graining process, constructing adequate descriptors/features, generating representative training data sets, and optimization of the loss function. Two classes of the CG models are introduced: bottom-up and top-down CG methods. To illustrate these methods and demonstrate the open methodological questions, we survey several important principles in constructing CG models and how these are incorporated into ML methods and improved with specific learning techniques. Finally, we discuss some key aspects of developing machine-learned CG models with high accuracy and efficiency. Besides, we describe how these aspects are tackled in state-of-the-art methods and which remain to be addressed in the near future. We expect that these machine-learned CG models can address thermodynamic consistent, transferable, and representative issues in classical CG models.
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Affiliation(s)
- Huilin Ye
- Department
of Mechanical Engineering, University of
Connecticut, Storrs, Connecticut 06269, United States
| | - Weikang Xian
- Department
of Mechanical Engineering, University of
Connecticut, Storrs, Connecticut 06269, United States
| | - Ying Li
- Department
of Mechanical Engineering, University of
Connecticut, Storrs, Connecticut 06269, United States
- Polymer
Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, United States
- E-mail: . Phone: +1 860 4867110. Fax: +1 860 4865088
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36
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Kapoor U, Kulshreshtha A, Jayaraman A. Development of Coarse-Grained Models for Poly(4-vinylphenol) and Poly(2-vinylpyridine): Polymer Chemistries with Hydrogen Bonding. Polymers (Basel) 2020; 12:E2764. [PMID: 33238611 PMCID: PMC7709027 DOI: 10.3390/polym12112764] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022] Open
Abstract
In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries-poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.
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Affiliation(s)
- Utkarsh Kapoor
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
| | - Arjita Kulshreshtha
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
| | - Arthi Jayaraman
- Department of Chemical and Biomolecular Engineering, Colburn Laboratory, University of Delaware, 150 Academy Street, Newark, DE 19716, USA; (U.K.); (A.K.)
- Department of Materials Science and Engineering, University of Delaware, Newark, DE 19716, USA
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37
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Sadeghi MS, Moghbeli MR, Goddard WA. A coarse-grain force field based on quantum mechanics (CGq FF) for molecular dynamics simulation of poly(ethylene glycol)-block-poly(ε-caprolactone) (PEG-b-PCL) micelles. Phys Chem Chem Phys 2020; 22:24028-24040. [PMID: 33078174 DOI: 10.1039/d0cp04364h] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In order to provide the means to predict from molecular dynamics (MD) simulations the structures of copolymer-based micelles in solution, we developed coarse grain force field (CGq FF) parameters for poly(ethylene glycol) (PEG) and for poly(ε-caprolactone) (PCL). A key advance here is the use of quantum mechanics to train the parameters describing the non-bonded (NB) interactions between the CG beads. The functional forms are the same as the MARTINI CG FF so standard MD codes can be used. Our CGq FF describes well the experimentally observed properties for the polymer-air and polymer-water interfaces, indicating the accuracy of the NB interactions. The structural properties (density, radius of gyration (Rg), and end-to-end distance (h)) match both experiment and all atom (AA) simulations. We illustrate the application of this CGq FF by following the formation of a spherical micelle from 250 chains of PEG23-b-PCL9 diblock copolymer, each block with molecular weight of 1000 Daltons (10 500 beads, corresponding to 123 250 atoms), in a water box with 119 139 water beads (426 553 water molecules).
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Affiliation(s)
- Maryam S Sadeghi
- Smart Polymers and Nanocomposites Research Group, School of Chemical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Mohammad Reza Moghbeli
- Smart Polymers and Nanocomposites Research Group, School of Chemical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - William A Goddard
- Materials and Process Simulation Center (MSC), California Institute of Technology, Pasadena, California 91125, USA.
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38
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Tamir E, Srebnik S, Sidess A. Prediction of the relaxation modulus of a fluoroelastomer using molecular dynamics simulation. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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39
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Martzel N, Dequidt A, Devémy J, Blaak R, Garruchet S, Latour B, Goujon F, Munch E, Malfreyt P. Grain Shape Dynamics for Molecular Simulations at the Mesoscale. ADVANCED THEORY AND SIMULATIONS 2020. [DOI: 10.1002/adts.202000124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Nicolas Martzel
- Manufacture Française des Pneumatiques Michelin Site de Ladoux, 23 Place des Carmes Déchaux, France Cedex 9 Clermont‐Ferrand 63040 France
| | - Alain Dequidt
- Université Clermont Auvergne, CNRS, SIGMA Clermont Institut de Chimie de Clermont‐Ferrand Clermont‐Ferrand F‐63000 France
| | - Julien Devémy
- Université Clermont Auvergne, CNRS, SIGMA Clermont Institut de Chimie de Clermont‐Ferrand Clermont‐Ferrand F‐63000 France
| | - Ronald Blaak
- Université Clermont Auvergne, CNRS, SIGMA Clermont Institut de Chimie de Clermont‐Ferrand Clermont‐Ferrand F‐63000 France
| | - Sebastien Garruchet
- Manufacture Française des Pneumatiques Michelin Site de Ladoux, 23 Place des Carmes Déchaux, France Cedex 9 Clermont‐Ferrand 63040 France
| | - Benoit Latour
- Manufacture Française des Pneumatiques Michelin Site de Ladoux, 23 Place des Carmes Déchaux, France Cedex 9 Clermont‐Ferrand 63040 France
| | - Florent Goujon
- Université Clermont Auvergne, CNRS, SIGMA Clermont Institut de Chimie de Clermont‐Ferrand Clermont‐Ferrand F‐63000 France
| | - Etienne Munch
- Manufacture Française des Pneumatiques Michelin Site de Ladoux, 23 Place des Carmes Déchaux, France Cedex 9 Clermont‐Ferrand 63040 France
| | - Patrice Malfreyt
- Université Clermont Auvergne, CNRS, SIGMA Clermont Institut de Chimie de Clermont‐Ferrand Clermont‐Ferrand F‐63000 France
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40
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Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling. MICROMACHINES 2020; 11:mi11060614. [PMID: 32599925 PMCID: PMC7344937 DOI: 10.3390/mi11060614] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 11/30/2022]
Abstract
The increasing demand for micro-injection molding process technology and the corresponding micro-molded products have materialized in the need for models and simulation capabilities for the establishment of a digital twin of the manufacturing process. The opportunities enabled by the correct process simulation include the possibility of forecasting the part quality and finding optimal process conditions for a given product. The present work displays further use of micro-injection molding process simulation for the prediction of feature dimensions and its optimization and microfeature replication behavior due to geometrical boundary effects. The current work focused on the micro-injection molding of three-dimensional microparts and of single components featuring microstructures. First, two virtual a studies were performed to predict the outer diameter of a micro-ring within an accuracy of 10 µm and the flash formation on a micro-component with mass a 0.1 mg. In the second part of the study, the influence of microstructure orientation on the filling time of a microcavity design section was investigated for a component featuring micro grooves with a 15 µm nominal height. Multiscale meshing was employed to model the replication of microfeatures in a range of 17–346 µm in a Fresnel lens product, allowing the prediction of the replication behavior of a microfeature at 91% accuracy. The simulations were performed using 3D modeling and generalized Navier–Stokes equations using a single multi-scale simulation approach. The current work shows the current potential and limitations in the use of micro-injection molding process simulations for the optimization of micro 3D-part and microstructured components.
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41
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Hoy RS, Kröger M. Unified Analytic Expressions for the Entanglement Length, Tube Diameter, and Plateau Modulus of Polymer Melts. PHYSICAL REVIEW LETTERS 2020; 124:147801. [PMID: 32338959 DOI: 10.1103/physrevlett.124.147801] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 03/09/2020] [Indexed: 06/11/2023]
Abstract
By combining molecular dynamics simulations and topological analyses with scaling arguments, we obtain analytic expressions that quantitatively predict the entanglement length N_{e}, the plateau modulus G, and the tube diameter a in melts that span the entire range of chain stiffnesses for which systems remain isotropic. Our expressions resolve conflicts between previous scaling predictions for the loosely entangled [Lin-Noolandi, Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{3}], semiflexible [Edwards-de Gennes: Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{2}], and tightly entangled [Morse, Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{1+ϵ}] regimes, where ℓ_{K} and p are, respectively, the Kuhn and packing lengths. We also find that maximal entanglement (minimal N_{e}) coincides with the onset of local nematic order.
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Affiliation(s)
- Robert S Hoy
- Department of Physics, University of South Florida, Tampa, Florida 33620, USA
| | - Martin Kröger
- Polymer Physics, ETH Zürich, Department of Materials, CH-8093 Zürich, Switzerland
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42
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Chen X, Qi S, Zhang X, Yan D. Influence of Small-Scale Correlation on the Interface Evolution of Semiflexible Homopolymer Blends. ACS OMEGA 2020; 5:7593-7600. [PMID: 32280903 PMCID: PMC7144130 DOI: 10.1021/acsomega.0c00421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 03/17/2020] [Indexed: 06/11/2023]
Abstract
Within the framework of a dynamic self-consistent field theory, we study the effect of the correlations in a small scale on polymer dynamics, adopting the semiflexible homopolymer blends as the model system. This is accomplished by taking the pair correlation function of ideal semiflexible chains as the Onsager coefficient and the Debye function as an approximation to the Onsager coefficient. Relying on the difference of the two pair correlation functions in the small-scale region, we can identify the effect of small-scale correlations. In the equilibrium state, with the chain length growing, the interface width has a continuous transition from the contour length to radius of gyration. The investigation of interfacial evolution and chain orientation reveals that strong small-scale correlations would accelerate the small-scale dynamic process. We also expect that such a small-scale effect should be highlighted in the process where microscopic phase separation happens.
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Affiliation(s)
- Xinxiang Chen
- Department
of Physics, Beijing Normal University, Beijing 100875, China
| | - Shuanhu Qi
- School
of Chemistry, Beihang University, Beijing 100191, China
| | - Xinghua Zhang
- School
of Science, Beijing Jiaotong University, Beijing 100044, China
| | - Dadong Yan
- Department
of Physics, Beijing Normal University, Beijing 100875, China
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43
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Huang LH, Wu CH, Hua CC, Huang TJ. Multiscale simulations of coupled composition-stress-morphology of binary polymer blend. POLYMER 2020. [DOI: 10.1016/j.polymer.2020.122366] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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44
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Everaers R, Karimi-Varzaneh HA, Fleck F, Hojdis N, Svaneborg C. Kremer–Grest Models for Commodity Polymer Melts: Linking Theory, Experiment, and Simulation at the Kuhn Scale. Macromolecules 2020. [DOI: 10.1021/acs.macromol.9b02428] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Ralf Everaers
- Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, Laboratoire de Physique and Centre Blaise Pascal de l’ENS de Lyon, F-69342 Lyon, France
| | | | - Frank Fleck
- Continental Reifen Deutschland GmbH, Jädekamp 30, D-30419 Hannover, Germany
| | - Nils Hojdis
- Institute of Applied Polymer Chemistry, Aachen University of Applied Sciences, Heinrich-Mussmann-Str.1, 52428 Jülich, Germany
| | - Carsten Svaneborg
- University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
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45
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Kempfer K, Devémy J, Dequidt A, Couty M, Malfreyt P. Multi-scale modeling of the polymer-filler interaction. SOFT MATTER 2020; 16:1538-1547. [PMID: 31939976 DOI: 10.1039/c9sm01959f] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We report mesoscopic simulations of the interaction between a silica nanoparticle and cis-1,4-polybutadiene chains with realistic coarse-(CG) grained models. The CG models are obtained with a bottom-up Bayesian method based on trajectory matching of atomistic configurations of the system. We then investigate the structural properties of the interfacial region as a function of the grafting density and polymer chain length. We take advantage of the realistic CG models to explore the dynamics of the nanoparticle over a period of 10 microseconds. We show that the dynamics of the nanoparticle is affected by the grafting density and the polymer chain length of the grafted chains.
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Affiliation(s)
- Kevin Kempfer
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut de Chimie de Clermont-Ferrand, F-63000 Clermont-Ferrand, France.
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46
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Rondina GG, Böhm MC, Müller-Plathe F. Predicting the Mobility Increase of Coarse-Grained Polymer Models from Excess Entropy Differences. J Chem Theory Comput 2020; 16:1431-1447. [DOI: 10.1021/acs.jctc.9b01088] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gustavo G. Rondina
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 8, 64287 Darmstadt, Germany
| | - Michael C. Böhm
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 8, 64287 Darmstadt, Germany
| | - Florian Müller-Plathe
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 8, 64287 Darmstadt, Germany
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47
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Müller M. Process-directed self-assembly of copolymers: Results of and challenges for simulation studies. Prog Polym Sci 2020. [DOI: 10.1016/j.progpolymsci.2019.101198] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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48
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Walker CC, Genzer J, Santiso EE. Extending the fused-sphere SAFT-γ Mie force field parameterization approach to poly(vinyl butyral) copolymers. J Chem Phys 2020; 152:044903. [DOI: 10.1063/1.5126213] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Affiliation(s)
- Christopher C. Walker
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Jan Genzer
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Erik E. Santiso
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
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49
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Lebold KM, Noid WG. Dual-potential approach for coarse-grained implicit solvent models with accurate, internally consistent energetics and predictive transferability. J Chem Phys 2019; 151:164113. [PMID: 31675902 DOI: 10.1063/1.5125246] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The dual-potential approach promises coarse-grained (CG) models that accurately reproduce both structural and energetic properties, while simultaneously providing predictive estimates for the temperature-dependence of the effective CG potentials. In this work, we examine the dual-potential approach for implicit solvent CG models that reflect large entropic effects from the eliminated solvent. Specifically, we construct implicit solvent models at various resolutions, R, by retaining a fraction 0.10 ≤ R ≤ 0.95 of the molecules from a simple fluid of Lennard-Jones spheres. We consider the dual-potential approach in both the constant volume and constant pressure ensembles across a relatively wide range of temperatures. We approximate the many-body potential of mean force for the remaining solutes with pair and volume potentials, which we determine via multiscale coarse-graining and self-consistent pressure-matching, respectively. Interestingly, with increasing temperature, the pair potentials appear increasingly attractive, while the volume potentials become increasingly repulsive. The dual-potential approach not only reproduces the atomic energetics but also quite accurately predicts this temperature-dependence. We also derive an exact relationship between the thermodynamic specific heat of an atomic model and the energetic fluctuations that are observable at the CG resolution. With this generalized fluctuation relationship, the approximate CG models quite accurately reproduce the thermodynamic specific heat of the underlying atomic model.
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Affiliation(s)
- Kathryn M Lebold
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
| | - W G Noid
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA
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50
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Maiti A, Small W, Lewicki JP, Chinn SC, Wilson TS, Saab AP. Age-aware constitutive materials model for a 3D printed polymeric foam. Sci Rep 2019; 9:15923. [PMID: 31685889 PMCID: PMC6828972 DOI: 10.1038/s41598-019-52298-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 08/27/2019] [Indexed: 11/09/2022] Open
Abstract
Traditional open or closed-cell stochastic elastomeric foams have wide-ranging applications in numerous industries: from thermal insulation, shock absorbing/gap-filling support cushions, packaging, to light-weight structural and positional components. Recent developments in 3D printing technologies by direct ink-write have opened the possibility of replacing stochastic foam parts by more controlled printed micro-structures with superior stress-distribution and longer functional life. For successful deployment as mechanical support or structural components, it is crucial to characterize the response of such printed materials to long-term external loads in terms of stress-strain behavior evolution and in terms of irreversible structural and load-bearing capacity changes over time. To this end, here we report a thermal-age-aware constitutive model for a 3D printed close-packed foam structure under compression. The model is based on the Ogden hyperfoam strain-energy functional within the framework of Tobolsky two-network scheme. It accurately describes experimentally measured stress-strain response, compression set, and load retention for various aging times and temperatures. Through the technique of time-temperature-superposition the model enables the prediction of long-term changes along with the quantification of uncertainty stemming from sample-to-sample variation and measurement noise. All aging parameters appear to possess the same Arrhenius activation barrier, which suggests a single dominant aging mechanism at the molecular/network level.
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Affiliation(s)
- A Maiti
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA.
| | - W Small
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - J P Lewicki
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - S C Chinn
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - T S Wilson
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - A P Saab
- Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
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