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Computational Study of Network Phase Formation in Self-Assembled Block Polymers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Study of Network Phase Formation in Self-Assembled Block Polymers /Pengyu Chen.
Author:	Chen, Pengyu,
Description:	1 electronic resource (136 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Notes:	Advisors: Dorfman, Kevin D. Committee members: Bates, Frank S.; Mahanthappa, Mahesh K.; Siepmann, J. Ilja.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Materials science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31555829
ISBN:	9798384095347

Computational Study of Network Phase Formation in Self-Assembled Block Polymers /
Chen, Pengyu,

Computational Study of Network Phase Formation in Self-Assembled Block Polymers /Pengyu Chen. - 1 electronic resource (136 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Mesoporous materials with multi-continuous networks are attracting widespread interest due to their potential applications in optical metamaterials, membrane separation, and catalysis. Block polymers, with their simple thermodynamic representation through self-consistent field theory (SCFT), provide an ideal model for examining the self-assembly behaviors in these networks. In this dissertation, we leverage SCFT to deepen our understanding of the thermodynamic principles that govern the formation of these complex network phases and to facilitate the discovery of new network phases. The investigation begins with an exploration of the metastability of cubic single networks via SCFT. Our findings reveal that larger interfacial areas per unit volume, resulting from lower chain-packing densities in the single networks, are a critical factor contributing to their metastability. This phenomenon is largely due to the specific positioning of chain configurations relative to the minimal surfaces. Furthermore, we utilize SCFT to validate the feasibility of using ternary polymer blends for fabricating alternating gyroids (GA). By introducing a small amount of poly(isoprene-b-styrene-b-ethylene oxide) (ISO) triblock terpolymers into blends of double-gyroid-forming diblock copolymers, poly(isoprene-b-styrene) (IS) and poly(styrene-b-ethylene oxide) (SO), the alternating gyroid phase can be effectively stabilized. It is found that the formation of alternating gyroids is particularly sensitive to the asymmetry in thermodynamic parameters, and that matching the preferred domain sizes of the IS and SO diblocks enhances the stability of alternating gyroids. Finally, to overcome the limitations of conventional SCFT in predicting new morphologies, we integrate SCFT with a deep convolutional generative adversarial network (GAN) trained on SCFT density fields of known phases. This innovative approach allows the GAN to generate new input fields for subsequent SCFT calculations, resulting in a library of 349candidate network phases. These phases cover both known networks and new networks with competitively low free energies, offering new directions for inverse materials design, property predictions, and structural determinations of self-assembled network phases. Overall, this dissertation advances our understanding of self-assembled network phases in block polymers and other soft matter systems, proposing new strategies for future research.

English

ISBN: 9798384095347Subjects--Topical Terms:

221779
Materials science.
Subjects--Index Terms:

Block polymers

Computational Study of Network Phase Formation in Self-Assembled Block Polymers /
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520 $a Mesoporous materials with multi-continuous networks are attracting widespread interest due to their potential applications in optical metamaterials, membrane separation, and catalysis. Block polymers, with their simple thermodynamic representation through self-consistent field theory (SCFT), provide an ideal model for examining the self-assembly behaviors in these networks. In this dissertation, we leverage SCFT to deepen our understanding of the thermodynamic principles that govern the formation of these complex network phases and to facilitate the discovery of new network phases. The investigation begins with an exploration of the metastability of cubic single networks via SCFT. Our findings reveal that larger interfacial areas per unit volume, resulting from lower chain-packing densities in the single networks, are a critical factor contributing to their metastability. This phenomenon is largely due to the specific positioning of chain configurations relative to the minimal surfaces. Furthermore, we utilize SCFT to validate the feasibility of using ternary polymer blends for fabricating alternating gyroids (GA). By introducing a small amount of poly(isoprene-b-styrene-b-ethylene oxide) (ISO) triblock terpolymers into blends of double-gyroid-forming diblock copolymers, poly(isoprene-b-styrene) (IS) and poly(styrene-b-ethylene oxide) (SO), the alternating gyroid phase can be effectively stabilized. It is found that the formation of alternating gyroids is particularly sensitive to the asymmetry in thermodynamic parameters, and that matching the preferred domain sizes of the IS and SO diblocks enhances the stability of alternating gyroids. Finally, to overcome the limitations of conventional SCFT in predicting new morphologies, we integrate SCFT with a deep convolutional generative adversarial network (GAN) trained on SCFT density fields of known phases. This innovative approach allows the GAN to generate new input fields for subsequent SCFT calculations, resulting in a library of 349candidate network phases. These phases cover both known networks and new networks with competitively low free energies, offering new directions for inverse materials design, property predictions, and structural determinations of self-assembled network phases. Overall, this dissertation advances our understanding of self-assembled network phases in block polymers and other soft matter systems, proposing new strategies for future research.
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650 4 $a Condensed matter physics. $3 708726
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653 $a Machine learning
653 $a Network phases
653 $a Self-assembly
653 $a Self-consistent field theory
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710 2 $a University of Minnesota. $b Material Science and Engineering. $3 660375
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