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...

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>

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.

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.

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.

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>

scholarly journals Supramolecular polymerization of sulfated dendritic peptide amphiphiles into multivalent L-selectin binders

2021 ◽  
Vol 17 ◽  
pp. 97-104
Author(s):  
David Straßburger ◽  
Svenja Herziger ◽  
Katharina Huth ◽  
Moritz Urschbach ◽  
Rainer Haag ◽  
...  
Keyword(s):  
Self Assembly ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Peptide Amphiphile ◽  
One Dimensional ◽  
Cryogenic Transmission Electron Microscopy ◽  
Naturally Occurring ◽  
Selectin Ligands ◽  
Transmission Electron ◽  
Supramolecular Polymerization

The synthesis of a sulfate-modified dendritic peptide amphiphile and its self-assembly into one-dimensional rod-like architectures in aqueous medium is reported. The influence of the ionic strength on the supramolecular polymerization was probed via circular dichroism spectroscopy and cryogenic transmission electron microscopy. Physiological salt concentrations efficiently screen the charges of the dendritic building block equipped with eight sulfate groups and trigger the formation of rigid supramolecular polymers. Since multivalent sulfated supramolecular structures mimic naturally occurring L-selectin ligands, the corresponding affinity was evaluated using a competitive SPR binding assay and benchmarked to an ethylene glycol-decorated supramolecular polymer.

A dual-responsive cross-linked supramolecular polymer network gel: hierarchical supramolecular self-assembly driven by pillararene-based molecular recognition and metal–ligand interactions

2018 ◽  
Vol 9 (11) ◽  
pp. 1293-1297 ◽  
Author(s):  
Li Shao ◽  
Jie Yang ◽  
Bin Hua
Keyword(s):  
Molecular Recognition ◽  
Self Assembly ◽  
Polymer Network ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Dual Responsive ◽  
Ligand Interactions ◽  
Metal Ligand

A novel 2,2′-bipyridine-bridged pillar[5]arene dimer was synthesized and further applied for constructing linear supramolecular polymers and supramolecular polymer network gels.

scholarly journals Control of self-assembly pathways toward conglomerate and racemic supramolecular polymers

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Marius Wehner ◽  
Merle Insa Silja Röhr ◽  
Vladimir Stepanenko ◽  
Frank Würthner
Keyword(s):  
Self Assembly ◽  
Supramolecular Polymers ◽  
Racemic Mixture ◽  
Supramolecular Polymer ◽  
Thermodynamic Control ◽  
Temperature Dependent ◽  
Force Microscopy ◽  
Atomic Force ◽  
Ft Ir ◽  
Assembly Pathways

Abstract Homo- and heterochiral aggregation during crystallization of organic molecules has significance both for fundamental questions related to the origin of life as well as for the separation of homochiral compounds from their racemates in industrial processes. Herein, we analyse these phenomena at the lowest level of hierarchy – that is the self-assembly of a racemic mixture of (R,R)- and (S,S)-PBI into 1D supramolecular polymers. By a combination of UV/vis and NMR spectroscopy as well as atomic force microscopy, we demonstrate that homochiral aggregation of the racemic mixture leads to the formation of two types of supramolecular conglomerates under kinetic control, while under thermodynamic control heterochiral aggregation is preferred, affording a racemic supramolecular polymer. FT-IR spectroscopy and quantum-chemical calculations reveal unique packing arrangements and hydrogen-bonding patterns within these supramolecular polymers. Time-, concentration- and temperature-dependent UV/vis experiments provide further insights into the kinetic and thermodynamic control of the conglomerate and racemic supramolecular polymer formation.

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