Polyacrylonitrile Interactions with Carbon Nanotubes in Solution: Conformations and Binding as a Function of Solvent, Temperature, and Concentration

Pramanik, Chandrani; Jamil, Tariq; Gissinger, Jacob R.; Guittet, Darice; Arias‐Monje, Pedro J.; Kumar, Satish; Heinz, Hendrik

doi:10.1002/adfm.201905247

Cited by 20 publications

(19 citation statements)

References 87 publications

(104 reference statements)

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“…Implementation of recent models with improved representations of π-electrons in aromatic rings would enhance the molecular interpretation of our experimental results and improve correlations obtained from simulations presented herein . Recent work by Pramanik et al, showed that through the implementation of such models, better correlations of polymer binding to carbon nanotubes were achieved relative to simple atomistic models . Overall, the binding energy discussion lets us conclude that the measured IFA fluctuation energies correspond to the molecular adhesive energies between the peptides and graphite, and that the amino acid sequence plays a prominent role in the interaction of the peptides with graphene.…”

Section: Resultssupporting

confidence: 62%

“…51 Recent work by Pramanik et al, showed that through the implementation of such models, better correlations of polymer binding to carbon nanotubes were achieved relative to simple atomistic models. 52 Overall, the binding energy discussion lets us conclude that the measured IFA fluctuation energies correspond to the molecular adhesive energies between the peptides and graphite, and that the amino acid sequence plays a prominent role in the interaction of the peptides with graphene.…”

Section: ■ Results and Discussionmentioning

confidence: 89%

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Thermal Selection of Aqueous Molecular Conformations for Tailored Energetics of Peptide Assemblies at Solid Interfaces

et al. 2019

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Key to the development of functional bioinorganic soft interfaces is the predictive control over the micron-scale assembly structure and energetics of biomolecules at solid interfaces. While assembly of labile biomolecules, such as short peptides, at interfaces is a great deal affected by the shape of the molecule, biomolecular conformations are prompted by external solution conditions, involving temperature, pH, and salt concentration. In this light, one can expect that the environmental conformational selection of aqueous biomolecules could potentially allow for fine-tuning of the equilibrium assembly structure at interfaces, as well as, the binding strength and molecular mobility within these assemblies. Here, we demonstrate the energetic and structural tailoring of two-dimensional surface assemblies of graphite-binding dodecapeptides, through the thermal selection of aqueous peptide conformations. Our findings based on a scanning probe energetic analysis, supplemented by molecular dynamics modeling, show that peptide-graphite and peptide−peptide intermolecular interactions strongly depend on the thermally selected molecular conformation and that the extent of the conformational change is directly related to the observed assembled structure. Enabled by these results was the design of a peptide with predictable binding and assembled structure, thus, suggesting environmental preconditioning of peptides as a means for controlling self-assembling active bioinorganic interfaces for bioelectronic implementations such as biomolecular fuel cells and biosensors.

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Section: Resultssupporting

confidence: 62%

Section: ■ Results and Discussionmentioning

confidence: 89%

Thermal Selection of Aqueous Molecular Conformations for Tailored Energetics of Peptide Assemblies at Solid Interfaces

et al. 2019

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“…In those models, we neglected the influence of π-electrons of graphene-related materials, although the polarity of carbon nanotubes affected the interactions of polar molecules and ions . The INTERFACE force field has virtual π-electrons, which is adopted for description of carbon nanotubes. , π-interaction plays one of the major roles in polymer and solvent interactions with carbon nanotube surfaces. An interaction between an ion and π-electrons of a carbon nanotube was considerable for ion adsorption, while an interaction between water and the π-electrons was weak.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…44 The INTERFACE force field has virtual π-electrons, which is adopted for description of carbon nanotubes. 45,46 π-interaction plays one of the major roles in polymer and solvent interactions with carbon nanotube surfaces. An interaction between an ion and π-electrons of a carbon nanotube was considerable for ion adsorption, while an interaction between water and the π-electrons was weak.…”

Section: Methodsmentioning

confidence: 99%

Water Adsorption Control by Surface Nanostructures on Graphene-Related Materials by Grand Canonical Monte Carlo Simulations

Takamatsu

Ohba

2021

Langmuir

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The interfaces of carbon materials play an important role in various technological and scientific research fields. Graphene is the fundamental unit of sp 2 carbon allotropes, and the evaluation of the interfacial properties of graphene-related materials is thus essential to clarify the molecular mechanisms occurring at the interfaces. Ideally, graphene is exclusively composed of sp 2 carbon atoms; however, some parts of graphene normally contain sp 3 carbon atoms with oxygen functional groups, vacancy, and grain boundary defects, and these structural characteristics need to be considered to reveal the interfacial properties. Herein, we demonstrate the interfacial properties of graphene-related materials by analyzing the water adsorption properties of graphene, hydrogenated graphene (graphane), and partially oxidized graphene (named as graphoxide) using grand canonical Monte Carlo simulations. The hydrophobicity evaluated from the simulated water adsorption isotherms followed the order: graphane > graphene > graphoxide with 1% oxygen atomic ratio > graphoxide with 3% oxygen atomic ratio > graphoxide with 5% oxygen atomic ratio. The potential calculations between a single water molecule and graphoxides revealed that the presence of oxygen functional groups enhanced the hydrophilicity of graphoxide. This study also disclosed some differences between the hydrophobic interfaces of graphene and graphane, which have been rarely evaluated. Surprisingly, the hydrophobicity of graphane was higher than that of graphene despite the similar potential well depths between a water molecule and graphene/graphane. This was caused by the restriction of water orientation on graphane; water was preferentially adsorbed on the honeycomb center or concave sites in the initial adsorption, and it was hard to interact with neighboring water molecules. The different structures revealed for the graphenerelated materials with nanoscale roughness played important roles in controlling the water vapor adsorption mechanism.

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“…The advantages of these techniques include insight at the scale of atoms, access to the large nanometer scale, and the ability to simulate entire stress-strain experiments from equilibrium to failure in high accuracy. [6] [7] Simulations then allow to design and screen a larger number of model structures with a variety of defects and nanoscale features of interest, inspired by experimental data and theory, and examine the failure mechanisms in depth. However, although more efficient than experiments, the computational cost is still considerable.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning

Zhao¹,

Winetrout²,

Xu³

et al. 2021

Preprint

Self Cite

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Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 10 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can potentially be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties.

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Polyacrylonitrile Interactions with Carbon Nanotubes in Solution: Conformations and Binding as a Function of Solvent, Temperature, and Concentration

Cited by 20 publications

References 87 publications

Thermal Selection of Aqueous Molecular Conformations for Tailored Energetics of Peptide Assemblies at Solid Interfaces

Thermal Selection of Aqueous Molecular Conformations for Tailored Energetics of Peptide Assemblies at Solid Interfaces

Water Adsorption Control by Surface Nanostructures on Graphene-Related Materials by Grand Canonical Monte Carlo Simulations

Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning

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