Engineering the morphology of hydrogen-bonded comb-shaped supramolecular polymers: from solution self-assembly to confined assembly

2020 ◽  
Vol 11 (24) ◽  
pp. 4022-4028
Author(s):  
Senbin Chen ◽  
Zhen Geng ◽  
Xihuang Zheng ◽  
Jiangping Xu ◽  
Wolfgang H. Binder ◽  
...  
Keyword(s):  
Self Assembly ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Multi Stage ◽  
Polymer Architectures ◽  
Hydrogen Bonded

A library of nanostructures and multi-stage morphology transformation are realized by introducing a 3D confined assembly to hydrogen-bonded comb-shaped supramolecular polymer architectures.

Programmed Exfoliation of Hierarchical Graphene Nanosheets Mediated by Dynamic Self-Assembly of Supramolecular Polymers

2021 ◽  
Author(s):  
Ashenafi Zeleke Melaku ◽  
Wei-Tsung Chuang ◽  
Yeong-Tarng Shieh ◽  
Chih-Wei Chiu ◽  
D. J. Lee ◽  
...  
Keyword(s):  
Self Assembly ◽  
Graphene Nanosheets ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer

Programmed formation of hierarchical graphene nanosheets, based on a combination of first and second exfoliations using the halogenated solvent ortho-dichlorobenzene (ODCB) and an adenine-functionalized supramolecular polymer (A-PPG), respectively, can be...

Hydrogen-bonded metallo-supramolecular polymers based on ruthenium or iron complexes for the selective extraction of single-walled carbon nanotubes

2018 ◽  
Vol 47 (40) ◽  
pp. 14195-14203 ◽  
Author(s):  
Shota Oka ◽  
Hiroaki Ozawa ◽  
Kai Yoshikawa ◽  
Tomiki Ikeda ◽  
Masa-aki Haga
Keyword(s):  
Carbon Nanotubes ◽  
Surface Modification ◽  
Single Walled Carbon Nanotubes ◽  
Iron Complexes ◽  
Selective Extraction ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Single Walled Carbon ◽  
Walled Carbon Nanotubes ◽  
Hydrogen Bonded

Selective extraction of semiconducting SWNT via entangled surface modification by H-bonded metallo-supramolecular polymer was achieved.

scholarly journals Identifying and Tracking Defects in Dynamic Supramolecular Polymers

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

A Complete Structural and Dynamic Characterization of Supramolecular Polymers via Automatic Learning of Defects

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
High Resolution ◽  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems

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, high-resolution 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 high-resolution 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.

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

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

Supramolecular polymers-derived nonmetal N, S-codoped carbon nanosheets for efficient oxygen reduction reaction

RSC Advances ◽  
2016 ◽  
Vol 6 (58) ◽  
pp. 52937-52944 ◽  
Author(s):  
Zheng Kun Yang ◽  
Ling Lin ◽  
Ya-Nan Liu ◽  
Xiao Zhou ◽  
Cheng-Zong Yuan ◽  
...  
Keyword(s):  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Reduction Reaction ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Carbon Nanosheets ◽  
Hydrogen Bonded

Hydrogen-bonded supramolecular polymer-derived nonmetal N and S codoped carbon nanosheets show superior oxygen reduction performance.

Hydrogen Bonded Supramolecular Polymers in Both Apolar and Aqueous Media: Self-Assembly and Reversible Conversion of Vesicles and Gels

2011 ◽  
Vol 29 (12) ◽  
pp. 2597-2605 ◽  
Author(s):  
Ping Du ◽  
Jun Kong ◽  
Guitao Wang ◽  
Xin Zhao ◽  
Guangyu Li ◽  
...  
Keyword(s):  
Self Assembly ◽  
Aqueous Media ◽  
Supramolecular Polymers ◽  
Hydrogen Bonded

Synthesis and Self Assembly of Hydrogen-Bonded Supramolecular Polymers

2004 ◽  
Vol 217 (1) ◽  
pp. 247-266 ◽  
Author(s):  
Dominique Farnik ◽  
Christian Kluger ◽  
Michael J. Kunz ◽  
Doris Machl ◽  
Laura Petraru ◽  
...  
Keyword(s):  
Self Assembly ◽  
Supramolecular Polymers ◽  
Hydrogen Bonded

Hierarchical self-assembly and controlled disassembly of a cavitand-based host–guest supramolecular polymer

2021 ◽  
Author(s):  
Daniele Zuccaccia ◽  
Roberta Pinalli ◽  
Rita De Zorzi ◽  
Monica Semeraro ◽  
Alberto Credi ◽  
...  
Keyword(s):  
Electrochemical Reduction ◽  
Self Assembly ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Hierarchical Aggregation ◽  
Linear Chains ◽  
Aggregation Modes Of

Two hierarchical aggregation modes of cavitand-based supramolecular polymers allow implementing orthogonal disassembly procedures: electrochemical reduction for linear chains and solvent-driven dissolution for bundles.

Synthesis and Self-Assembly of Hydrogen-Bonded Supramolecular Polymers

2011 ◽  
pp. 53-95 ◽  
Author(s):  
Wolfgang H. Binder ◽  
Claudia Enders ◽  
Florian Herbst ◽  
Katharina Hackethal
Keyword(s):  
Self Assembly ◽  
Supramolecular Polymers ◽  
Hydrogen Bonded
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