Modelling and simulation techniques are now considered an essential practice for the
materials industry. In order to gain insight into factors that can affect the final properties of a …

Over the last decade various investigations have been carried out on experimental and
numerical approaches to predict the influence of dual nanofillers (DNFs) and multiscale …

Predictive computational modeling of polymer materials is necessary for the efficient design
of composite materials and the corresponding processing methods. Molecular dynamics …

Modern aerospace applications require lightweight materials with exceptionally high
strength and stiffness. Carbon nanotube (CNT)-reinforced composites have great potential …

Aramid paper is ubiquitous in advanced electronics and high-voltage equipment by virtue of
their outstanding dielectric properties, mechanical reliability, and thermal stability. However …

Carbon nanotubes (CNTs) are an economical and multi‐functional nanofiller that can further
elevate the versatile performance of fiber‐reinforced polymer (FRP). The past two decades …

This paper is devoted to the development and construction of a practical Machine Learning
(ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) …

Photoresponsive hydrogels (PRHs) are soft materials whose mechanical and chemical
properties can be tuned spatially and temporally with relative ease. Both photo …

High performance epoxy composites are highly required in many applications. In this work,
polyimide electrospun nanofibers (PIENFs) with excellent thermal and mechanical …

This study develops a model for electrical conductivity of polymer carbon nanofiber (CNF)
nanocomposites (PCNFs), which includes two steps. In the first step, Kovacs model is …