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Cooper AJ, Howard MP, Kadulkar S, Zhao D, Delaney KT, Ganesan V, Truskett TM, Fredrickson GH. Multiscale modeling of solute diffusion in triblock copolymer membranes. J Chem Phys 2023; 158:024905. [PMID: 36641407 DOI: 10.1063/5.0127570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.
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Affiliation(s)
- Anthony J Cooper
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - Michael P Howard
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - David Zhao
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Kris T Delaney
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Glenn H Fredrickson
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
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Kadulkar S, Howard MP, Truskett TM, Ganesan V. Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks. J Phys Chem B 2021; 125:4838-4849. [PMID: 33914555 DOI: 10.1021/acs.jpcb.1c02004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes and use it to identify the characteristics of morphologies that exhibit optimal transport properties. The ground truth data are obtained from kinetic Monte Carlo (kMC) simulations of cation transport parametrized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, by using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure-property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.
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Affiliation(s)
- Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael P Howard
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering and Department of Physics, University of Texas at Austin, Austin, Texas 78712, United States
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
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Craven NC, Gilmer JB, Spindel CJ, Summers AZ, Iacovella CR, McCabe C. Examining the self-assembly of patchy alkane-grafted silica nanoparticles using molecular simulation. J Chem Phys 2021; 154:034903. [PMID: 33499609 DOI: 10.1063/5.0032658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In this work, molecular dynamics simulations are used to examine the self-assembly of anisotropically coated "patchy" nanoparticles. Specifically, we use a coarse-grained model to examine silica nanoparticles coated with alkane chains, where the poles of the grafted nanoparticle are bare, resulting in strongly attractive patches. Through a systematic screening process, the patchy nanoparticles are found to form dispersed, string-like, and aggregated phases, dependent on the combination of alkane chain length, coating chain density, and the fractional coated surface area. Correlation analysis is used to identify the ability of various particle descriptors to predict bulk phase behavior from more computationally efficient single grafted nanoparticle simulations and demonstrates that the solvent-accessible surface area of the nanoparticle core is a key predictor of bulk phase behavior. The results of this work enhance our knowledge of the phase space of patchy nanoparticles and provide a powerful approach for future screening of these materials.
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Affiliation(s)
- Nicholas C Craven
- Interdisciplinary Materials Science Program, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Justin B Gilmer
- Interdisciplinary Materials Science Program, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Caroline J Spindel
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Andrew Z Summers
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Christopher R Iacovella
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37235, USA
| | - Clare McCabe
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37235, USA
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Kadulkar S, Milliron DJ, Truskett TM, Ganesan V. Transport Mechanisms Underlying Ionic Conductivity in Nanoparticle-Based Single-Ion Electrolytes. J Phys Chem Lett 2020; 11:6970-6975. [PMID: 32787198 DOI: 10.1021/acs.jpclett.0c01937] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent studies have demonstrated the potential of nanoparticle-based single-ion conductors as battery electrolytes. In this work, we introduce a coarse-grained multiscale simulation approach to identify the mechanisms underlying the ion mobilities in such systems and to clarify the influence of key design parameters on conductivity. Our results suggest that for the experimentally studied electrolyte systems, the dominant pathway for cation transport is along the surface of nanoparticles, in the vicinity of nanoparticle-tethered anions. At low nanoparticle concentrations, the connectivity of cationic surface transport pathways and conductivity increase with nanoparticle loading. However, cation mobilities are reduced when nanoparticles are in close vicinity, causing conductivity to decrease for sufficiently high particle loadings. We discuss the impacts of cation and anion choice as well as solvent polarity within this picture and suggest means to enhance ionic conductivities in single-ion conducting electrolytes based on nanoparticle salts.
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Affiliation(s)
- Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Delia J Milliron
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering and Department of Physics, University of Texas at Austin, Austin, Texas 78712, United States
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
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Kadulkar S, Banerjee D, Khabaz F, Bonnecaze RT, Truskett TM, Ganesan V. Erratum: “Influence of morphology of colloidal nanoparticle gels on ion transport and rheology” [J. Chem. Phys. 150, 214903 (2019)]. J Chem Phys 2020; 152:199902. [DOI: 10.1063/5.0011639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Debapriya Banerjee
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Fardin Khabaz
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Roger T. Bonnecaze
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Thomas M. Truskett
- McKetta Department of Chemical Engineering and Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
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