Photoresponsive AA/BB supramolecular polymers comprising stiff-stilbene based guests and bispillar[5]arenes

2017 ◽  
Vol 8 (23) ◽  
pp. 3596-3602 ◽  
Author(s):  
Yuan Wang ◽  
Cai-Li Sun ◽  
Li-Ya Niu ◽  
Li-Zhu Wu ◽  
Chen-Ho Tung ◽  
...  
Keyword(s):  
Supramolecular Polymers ◽  
Supramolecular Polymer

We report a novel photoresponsive AA/BB supramolecular polymer comprising stiff-stilbene bridged guests and disulfide-bridged bispillar[5]arenes.

Crystallization and near-infrared emission from host–guest based supramolecular polymers

2021 ◽  
Author(s):  
Wenxia Yin ◽  
Lingyi Meng ◽  
Tianjun Yu ◽  
Jinping Chen ◽  
Rui Hu ◽  
...  
Keyword(s):  
Near Infrared ◽  
Crystallization Process ◽  
Infrared Emission ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Near Infrared Emission

Crystallization process of a NIR emissive supramolecular polymer formed by host–guest complexation of a distyrylanthracene derivative and cucurbiturils is described.

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...

Selective fluorescence sensing of H2PO4− by the anion induced formation of self-assembled supramolecular polymers

2020 ◽  
Vol 18 (20) ◽  
pp. 3858-3866 ◽  
Author(s):  
Paula Sabater ◽  
Fabiola Zapata ◽  
Adolfo Bastida ◽  
Antonio Caballero
Keyword(s):  
Halogen Bonding ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Fluorescence Sensing ◽  
Tripodal Receptor ◽  
Self Assembled

H2PO4− anions induced the formation of a fluorescent supramolecular polymer by halogen bonding interactions in a bromoimidazolium based tripodal receptor.

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 Chain stopper engineering for hydrogen bonded supramolecular polymers

2010 ◽  
Vol 6 ◽  
pp. 869-875 ◽  
Author(s):  
Thomas Pinault ◽  
Bruno Andrioletti ◽  
Laurent Bouteiller
Keyword(s):  
Hydrogen Bond ◽  
Molar Mass ◽  
Strong Hydrogen Bond ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Hydrogen Bond Donor ◽  
Linear Chains ◽  
Non Covalent Interactions ◽  
Viscous Solutions ◽  
Covalent Interactions

Supramolecular polymers are linear chains of low molar mass monomers held together by reversible and directional non-covalent interactions, which can form gels or highly viscous solutions if the self-assembled chains are sufficiently long and rigid. The viscosity of these solutions can be controlled by adding monofunctional compounds, which interact with the chain extremities: chain stoppers. We have synthesized new substituted ureas and thioureas and tested them as chain stoppers for a bis-urea based supramolecular polymer. In particular, the bis-thiourea analogue of the bis-urea monomer is shown not to form a supramolecular polymer, but a good chain stopper, because it is a strong hydrogen bond donor and a weak acceptor. Moreover, all substituted ureas tested reduce the viscosity of the supramolecular polymer solutions, but the best chain stopper is obtained when two hydrogen bond acceptors are placed in the same relative position as for the monomer and when no hydrogen bond donor is present.

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>

Construction of a pillararene-based supramolecular polymer network and its application in efficient removal of dyes from water

2022 ◽  
Author(s):  
Xiaoqing Lv ◽  
Danyu Xia ◽  
Yujie Cheng ◽  
Jianbin Chao ◽  
Xuehong Wei ◽  
...  
Keyword(s):  
Polymer Network ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Efficient Removal ◽  
Imidazolium Salt

An AB-type monomer based on a pillar[5]arene host and an imidazolium salt guest was successfully synthesized through a facile way. This monomer can self-assemble into linear supramolecular polymers in chloroform....

Intrinsically recyclable and self-healable conductive supramolecular polymers for customizable electronic sensors

2018 ◽  
Vol 6 (47) ◽  
pp. 12992-12999 ◽  
Author(s):  
Hongguang Liao ◽  
Shenglong Liao ◽  
Xinglei Tao ◽  
Chang Liu ◽  
Yapei Wang
Keyword(s):  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Thermal Sensing ◽  
Electronic Sensors ◽  
Green Electronics

An intrinsically conductive supramolecular polymer is prepared with remarkable thermal sensing and recyclable ability, providing a profound potential in green electronics.

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