scholarly journals Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Elisabeth Weyandt ◽  
Luigi Leanza ◽  
Riccardo Capelli ◽  
Giovanni M. Pavan ◽  
Ghislaine Vantomme ◽  
...  
Keyword(s):  
Complex Systems ◽  
Self Assembly ◽  
Chiral Discrimination ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Supramolecular System ◽  
Precise Control ◽  
Aggregate Morphology ◽  
The Individual ◽  
Supramolecular Polymerization

AbstractMulti-component systems often display convoluted behavior, pathway complexity and coupled equilibria. In recent years, several ways to control complex systems by manipulating the subtle balances of interaction energies between the individual components have been explored and thereby shifting the equilibrium between different aggregate states. Here we show the enantioselective chain-capping and dilution-induced supramolecular polymerization with a Zn2+-porphyrin-based supramolecular system when going from long, highly cooperative supramolecular polymers to short, disordered aggregates by adding a monotopic Mn3+-porphyrin monomer. When mixing the zinc and manganese centered monomers, the Mn3+-porphyrins act as chain-cappers for Zn2+-porphyrin supramolecular polymers, effectively hindering growth of the copolymer and reducing the length. Upon dilution, the interaction between chain-capper and monomers weakens as the equilibria shift and long supramolecular polymers form again. This dynamic modulation of aggregate morphology and length is achieved through enantioselectivity in the aggregation pathways and concentration-sensitive equilibria. All-atom and coarse-grained molecular simulations provide further insights into the mixing of the species and their exchange dynamics. Our combined experimental and theoretical approach allows for precise control of molecular self-assembly and chiral discrimination in complex systems.

scholarly journals Living supramolecular polymerization of fluorinated cyclohexanes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Oleksandr Shyshov ◽  
Shyamkumar Vadakket Haridas ◽  
Luca Pesce ◽  
Haoyuan Qi ◽  
Andrea Gardin ◽  
...  
Keyword(s):  
Dipole Moment ◽  
Self Assembly ◽  
Materials Science ◽  
Helical Structure ◽  
The Self ◽  
Supramolecular Polymers ◽  
Aliphatic Compound ◽  
Double Helical Structure ◽  
Key Driver ◽  
Supramolecular Polymerization

AbstractThe development of powerful methods for living covalent polymerization has been a key driver of progress in organic materials science. While there have been remarkable reports on living supramolecular polymerization recently, the scope of monomers is still narrow and a simple solution to the problem is elusive. Here we report a minimalistic molecular platform for living supramolecular polymerization that is based on the unique structure of all-cis 1,2,3,4,5,6-hexafluorocyclohexane, the most polar aliphatic compound reported to date. We use this large dipole moment (6.2 Debye) not only to thermodynamically drive the self-assembly of supramolecular polymers, but also to generate kinetically trapped monomeric states. Upon addition of well-defined seeds, we observed that the dormant monomers engage in a kinetically controlled supramolecular polymerization. The obtained nanofibers have an unusual double helical structure and their length can be controlled by the ratio between seeds and monomers. The successful preparation of supramolecular block copolymers demonstrates the versatility of the approach.

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>

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 Unconventional Chiral Amplification in Luminescent Supramolecular Polymers Based on Trisbiphenylamine-tricarboxamides

2020 ◽  
Vol 02 (01) ◽  
pp. 041-046 ◽  
Author(s):  
Yeray Dorca ◽  
Cristina Naranjo ◽  
Goutam Ghosh ◽  
Rafael Gómez ◽  
Gustavo Fernández ◽  
...  
Keyword(s):  
Self Assembly ◽  
The Self ◽  
Supramolecular Polymers ◽  
Side Chains ◽  
Chiral Amplification ◽  
Supramolecular Polymerization ◽  
Efficient Transfer

We describe the synthesis of two propeller-shaped, emissive trisbiphenylamines 1 and (S)-2. Whilst achiral 1 forms supramolecular polymers following a cooperative mechanism, the self-assembly of chiral (S)-2 can be described by an isodesmic mechanism. Despite the isodesmic character of the supramolecular polymerization of (S)-2, an efficient transfer of chirality from the embedded point chirality of the peripheral side chains to the aggregates is demonstrated. The co-assembly of 1 and (S)-2 in a sergeants-and-soldiers experiment shows a very different dichroic response to that registered for pristine (S)-2, with a copolymerization curve displaying two transitions. Both these transitions coincide with those observed for the pristine achiral and chiral components, thus suggesting a self-sorting effect.

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.

Orientational Order in Self-Assembled Nanocrystal Superlattices

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

<p>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.</p>

Orientational Order in Self-Assembled Nanocrystal Superlattices

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

<p>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.</p>

Controlled Nucleation of Ge Islands on Si and Self-Assembly of Nanoscale Island Clusters

2014 ◽  
Vol 23 (01n02) ◽  
pp. 1420003
Author(s):  
Maria Gherasimova ◽  
See Wee Chee ◽  
Robert Hull ◽  
Mark C. Reuter ◽  
Frances M. Ross
Keyword(s):  
Self Assembly ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Cluster Formation ◽  
Precise Control ◽  
Nucleation Sites ◽  
Single Location ◽  
Potential Applications ◽  
The Individual ◽  
Surface Modified

Germanium nucleation on a silicon surface typically proceeds via spontaneous formation of nanoscopic islands, or quantum dots (QDs), at random locations. However, potential applications of epitaxial QDs, such as quantum cellular automata, require precise control of Ge island positions on Si in clusters with the individual islands' separation of tens of nanometers or less. Controlled Ge island placement with an inter-island separation down to 100 nm can be reliably obtained by depositing Ge onto a Si(001) surface modified with a low dose focused ion beam (FIB) pattern to create preferred nucleation sites for individual islands. Here we investigate QD self-assembly of multiple islands on single topographical features intended to obtain clusters of QDs with smaller separations. We observe formation of 50 nm clusters on a single location defined by the FIB patterning of a larger and shallower feature, where cluster formation is promoted by reduced surface diffusion. We also discuss cluster formation on single sites defined by residual trenches on a footprint of a larger island that has been desorbed. We propose that controlled placement of islands during the first deposition may result in greater control over subsequent cluster size and placement fidelity due to improved uniformity of target features.

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