Self-assembly of spheroidal triblock Janus nanoparticle solutions in nanotubes

Yusei Kobayashi; Takuya Inokuchi; Atushi Nishimoto; Noriyoshi Arai

doi:10.1039/c8me00074c

Self-assembly of polymer-tethered nanoparticles with uniform and Janus surfaces in nanotubes

Soft Matter ◽

10.1039/d1sm00009h ◽

2021 ◽

Author(s):

Takumi Sato ◽

Yusei Kobayashi ◽

Takenobu Michioka ◽

Noriyoshi Arai

Keyword(s):

Phase Diagrams ◽

Molecular Simulation ◽

Self Assembly ◽

Coarse Grained ◽

Self Assembled

In this study, a coarse-grained molecular simulation was performed to investigate the morphologies and phase diagrams of self-assembled polymer-tethered nanoparticles (NPs) confined in nanotubes (NTs). Unlike ordinary NPs, polymer-tethered NPs...

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Solid–Liquid and Solid–Solid Phase Diagrams of Self-Assembled Triblock Janus Nanoparticles from Solution

The Journal of Physical Chemistry C ◽

10.1021/acs.jpcc.8b02043 ◽

2018 ◽

Vol 122 (16) ◽

pp. 9235-9244 ◽

Cited By ~ 11

Author(s):

Hossein Eslami ◽

Kheiri Bahri ◽

Florian Müller-Plathe

Keyword(s):

Phase Diagrams ◽

Solid Phase ◽

Janus Nanoparticles ◽

Self Assembled ◽

Solid Liquid

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Identifying and Tracking Defects in Dynamic Supramolecular Polymers

10.26434/chemrxiv.8268197.v4 ◽

2019 ◽

Author(s):

Piero Gasparotto ◽

Davide Bochicchio ◽

Michele Ceriotti ◽

Giovanni M. Pavan

Keyword(s):

Self Assembly ◽

Noncovalent Interactions ◽

Molecular Simulations ◽

Machine Learning Techniques ◽

Coarse Grained ◽

Supramolecular Polymers ◽

Structure Stability ◽

Supramolecular Polymer ◽

Soft Systems ◽

Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In the recent years, great efforts have been directed toward reaching the perfection in the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect, and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability and dynamics.

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Identifying and Tracking Defects in Dynamic Supramolecular Polymers

10.26434/chemrxiv.8268197 ◽

2019 ◽

Author(s):

Piero Gasparotto ◽

Davide Bochicchio ◽

Michele Ceriotti ◽

Giovanni M. Pavan

Keyword(s):

Self Assembly ◽

Noncovalent Interactions ◽

Molecular Simulations ◽

Machine Learning Techniques ◽

Coarse Grained ◽

Supramolecular Polymers ◽

Structure Stability ◽

Supramolecular Polymer ◽

Soft Systems ◽

Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In the recent years, great efforts have been directed toward reaching the perfection in the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect, and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability and dynamics.

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Control of the hierarchical assembly of π-conjugated optoelectronic peptides by pH and flow

Organic & Biomolecular Chemistry ◽

10.1039/c7ob00923b ◽

2017 ◽

Vol 15 (26) ◽

pp. 5484-5502 ◽

Cited By ~ 11

Author(s):

Rachael A. Mansbach ◽

Andrew L. Ferguson

Keyword(s):

Self Assembly ◽

Molecular Simulations ◽

The Self ◽

Coarse Grained ◽

Hierarchical Assembly ◽

Influence Of Ph

Coarse-grained molecular simulations reveal the influence of pH and flow on the self-assembly of DFAG-OPV3-GAFD optoelectronic peptides.

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Identifying and Tracking Defects in Dynamic Supramolecular Polymers

10.26434/chemrxiv.8268197.v3 ◽

2019 ◽

Author(s):

Piero Gasparotto ◽

Davide Bochicchio ◽

Michele Ceriotti ◽

Giovanni M. Pavan

Keyword(s):

Self Assembly ◽

Noncovalent Interactions ◽

Molecular Simulations ◽

Machine Learning Techniques ◽

Coarse Grained ◽

Supramolecular Polymers ◽

Structure Stability ◽

Supramolecular Polymer ◽

Soft Systems ◽

Local Defects

A central paradigm of self-assembly is to create ordered structures starting from molecular monomers that spontaneously recognize and interact with each other via noncovalent interactions. In the recent years, great efforts have been directed toward reaching the perfection in the design of a variety of supramolecular polymers and materials with different architectures. The resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers, micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the level of statistical ensembles to assess their average properties. However, molecular simulations recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic behavior and properties. The study of these defects poses considerable challenges, as the flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes a defect, and to characterize its stability and evolution. Here, we demonstrate the power of unsupervised machine learning techniques to systematically identify and compare defects in supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this approach allows a complete data-driven characterization of the internal structure and dynamics of these complex assemblies and of the dynamic pathways for defects formation and resorption. This provides a useful, generally applicable approach to unambiguously identify defects in these dynamic self-assembled materials and to classify them based on their structure, stability and dynamics.

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Orientational Order in Self-Assembled Nanocrystal Superlattices

10.26434/chemrxiv.7108769.v1 ◽

2018 ◽

Author(s):

Zhaochuan Fan ◽

Michael Gruenwald

Keyword(s):

Self Assembly ◽

Orientational Order ◽

Functional Materials ◽

Structural Diversity ◽

Coarse Grained ◽

Precise Control ◽

Nanoparticle Shape ◽

Nanoparticle Interactions ◽

Ligand Interactions ◽

Self Assembled

Self-assembly of nanocrystals into functional materials requires precise control over nanoparticle interactions in solution, which are dominated by organic ligands that densely cover the surface of nanocrystals. Recent experiments have demonstrated that small truncated-octahedral nanocrystals can self-assemble into a range of superstructures with different translational and orientational order of nanocrystals. The origin of this structural diversity remains unclear. Here, we use molecular dynamics computer simulations to study the self-assembly of these nanocrystals over a broad range of ligand lengths and solvent conditions. Our model, which is based on a coarse-grained description of ligands and solvent effects, reproduces the experimentally observed superstructures, including recently observed superlattices with partial and short-ranged orientational alignment of nanocrystals. We show that small differences in nanoparticle shape, ligand length and coverage, and solvent conditions can lead to markedly different self-assembled superstructures due to subtle changes in the free energetics of ligand interactions. Our results rationalize the large variety of different reported superlattices self-assembled from seemingly similar particles and can serve as a guide for the targeted self-assembly of nanocrystal superstructures.

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Self-Assembled Morphologies of Diblock Copolymers Confined in Multiwalled Carbon Nanotubes

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.815.512 ◽

2013 ◽

Vol 815 ◽

pp. 512-515

Author(s):

Yu Xin Zuo ◽

Guo Qing Wang ◽

Ying Yu ◽

Chun Cheng Zuo ◽

Yi Rui Wang

Keyword(s):

Carbon Nanotubes ◽

Multiwalled Carbon Nanotubes ◽

Self Assembly ◽

Diblock Copolymers ◽

Three Dimensional ◽

Md Simulations ◽

Surface Interactions ◽

Coarse Grained ◽

Multiwalled Carbon ◽

Self Assembled

Self-assembly of symmetric diblock copolymers (DCP) confined in multiwalled carbon nanotubes (MWCNTs) is studied using coarse-grained molecular dynamic (MD) simulations. The dependence of the self-assembled morphologies on the strength of the surface interactions is examined systematically. A rich variety of novel morphologies under the three-dimensional confinement have been revealed. The adsorption energy and cohesive energy have been discussed qualitatively and used to account for the appearance of the complex morphological transition.

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Orientational Order in Self-Assembled Nanocrystal Superlattices

10.26434/chemrxiv.7108769 ◽

2018 ◽

Author(s):

Zhaochuan Fan ◽

Michael Gruenwald

Keyword(s):

Self Assembly ◽

Orientational Order ◽

Functional Materials ◽

Structural Diversity ◽

Coarse Grained ◽

Precise Control ◽

Nanoparticle Shape ◽

Nanoparticle Interactions ◽

Ligand Interactions ◽

Self Assembled

Self-assembly of nanocrystals into functional materials requires precise control over nanoparticle interactions in solution, which are dominated by organic ligands that densely cover the surface of nanocrystals. Recent experiments have demonstrated that small truncated-octahedral nanocrystals can self-assemble into a range of superstructures with different translational and orientational order of nanocrystals. The origin of this structural diversity remains unclear. Here, we use molecular dynamics computer simulations to study the self-assembly of these nanocrystals over a broad range of ligand lengths and solvent conditions. Our model, which is based on a coarse-grained description of ligands and solvent effects, reproduces the experimentally observed superstructures, including recently observed superlattices with partial and short-ranged orientational alignment of nanocrystals. We show that small differences in nanoparticle shape, ligand length and coverage, and solvent conditions can lead to markedly different self-assembled superstructures due to subtle changes in the free energetics of ligand interactions. Our results rationalize the large variety of different reported superlattices self-assembled from seemingly similar particles and can serve as a guide for the targeted self-assembly of nanocrystal superstructures.

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