Related papers: Implicit Chain Particle Model for Polymer Grafted …
In this study, we investigate both experimentally and computationally the molecular interactions of two distinct polymers with graphene. Our experimental findings indicate that the use of a polymer mixture reduces the transfer induced…
Pyrene-functional PMMAs were prepared via ATRP-controlled polymerization and click reaction, as efficient dispersing agents for the exfoliation of few-layered graphene sheets (GS) in easily processable low boiling point chloroform. In…
Numerical modeling of polycrystal plasticity is computationally intensive. We employ Graph Neural Networks (GNN) to predict stresses on complex geometries for polycrystal plasticity from Finite Element Method (FEM) simulations. We present a…
Carbon nanomembranes (CNMs) are nanometer-thin disordered carbon materials that are suitable for a range of applications, from energy generation and storage, through to water filtration. The structure-property relationships of these…
Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bottom-up, cross-scale modeling framework that integrates…
Thermal conduction in polymer nanocomposites depends on several parameters including the thermal conductivity and geometrical features of the nanoparticles, the particle loading, their degree of dispersion and formation of a percolating…
Graphene is one of the stiffest known materials, with a Young's modulus of 1 TPa, making it an ideal candidate for use as a reinforcement in high-performance composites. However, being a one-atom thick crystalline material, graphene poses…
This work proposes a Graph Neural Network (GNN) modeling approach to predict the resulting surface from a particle based fabrication process. The latter consists of spray-based printing of cementitious plaster on a wall and is facilitated…
Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizing to unseen geometries due to their inherent inductive…
Accurate prediction of permeability in porous media is essential for modeling subsurface flow. While pure data-driven models offer computational efficiency, they often lack generalization across scales and do not incorporate explicit…
Molecular simulations provide an effective route for investigating morphology evolution and structure-property relationship in polymer-clay nanocomposites (PCNCs) incorporating layered silicates like montmorillonite (MMT), an important…
An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accuracy beyond density functional theory is often needed to describe the intermolecular interactions, while most…
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is…
The control of strain in two-dimensional materials opens exciting perspectives for the engineering of their electronic properties. While this expectation has been validated by artificial-lattice studies, it remains elusive in the case of…
High-performance sealing materials that can guard key components against high pressure gases and liquid chemicals while withstanding mechanical deformation are of utmost importance in a number of industries. In this present work, graphene…
Fundamental understanding of macroscopic properties of polymer nanocomposites (PNCs) remains difficult due to the complex interplay of microscopic dynamics and structure, namely interfacial layer relaxations and three-dimensional…
We present LayerPCM, an extension of the polarizable-continuum model coupled to real-time time-dependent density-functional theory for an efficient and accurate description of the electrostatic interactions between molecules and…
Photonic crystals based on graphene plasmons (GPs) are highly tunable and can accurately control photonic transmission at the nanoscales. In this work, the transfer matrix method (TMM) is introduced to study graphene plasmonic crystal (GPC)…
Short fiber reinforced polymer composites have found extensive industrial and engineering applications owing to their unique combination of low cost, relatively easy processing and superior mechanical properties compared to their parent…
This paper presents a study of the polymer-filler interfacial effects on filler dispersion and mechanical reinforcement in Polystyrene (PS) / silica nanocomposites by direct comparison of two model systems: un-grafted and PS-grafted silica…