Fenghua Nie

Coiled carbon nanotubes and their entwined derivatives have attracted attention for their excellent mechanical properties and potential applications. Hydrogenation is an effective way to modify the mechanical response of CCNTs, but its effect on the tensile performance and energy absorption of ECCNTs remains unclear. In this study, molecular dynamics simulations are used to systematically investigate the regulatory effects of hydrogenation and entanglement on the tensile properties and energy absorption capacity of CCNTs and ECCNTs. Different hydrogenation degrees and structural configurations are designed for comparative analysis. Results show that hydrogenation exerts a dual regulatory effect on single CCNTs: moderate hydrogenation enhances tensile strength and total energy absorption, while excessive hydrogenation leads to brittleness, reduced ductility, and impaired energy dissipation. Entanglement significantly improves the tensile load-bearing capacity and energy absorption of CCNTs, with ECCNTs showing 1.5–2.0 times higher spring constants than single CCNTs. Among the ECCNT models, ECCNT2 achieves an optimal balance between tensile stiffness, ductility, and energy absorption efficiency. This study clarifies the mechanistic roles of hydrogenation and entanglement, providing valuable guidance for the rational design of high-performance CCNT-based structural and functional nanomaterials.

Coiled carbon nanotubes (CCNTs) and hydrogenated coiled carbon nanotubes (HCCNTs) are advanced nanomaterials recognized as having exceptional mechanical properties and the potential to enhance composite materials. This study examines the pullout performance of CCNTs and HCCNTs in reinforcing asphalt nanocomposites through molecular dynamics simulations. Results indicate that CCNTs generally exhibit superior maximum pullout forces compared to HCCNTs, particularly at lower temperatures, suggesting stronger adhesion within the asphalt matrix. Conversely, HCCNTs demonstrate enhanced stability and flexibility under varying temperature conditions, allowing them to maintain more pullout energy over longer distances. Molecular movement analyses reveal that CCNTs facilitate greater displacement of asphalt molecules, indicating stronger interactions, while spatial configuration studies show that CCNTs attract more asphalt molecules in close proximity, enhancing their effectiveness. Additionally, HCCNTs possess a larger free volume, which may increase the risk of nano-damage but also allows for greater flexibility during the pullout process. This research highlights the distinct roles of CCNTs and HCCNTs in improving asphalt’s mechanical properties, ultimately contributing to the development of more resilient and sustainable asphalt pavements.

CNT-modified asphalt nanocomposites exhibit superior pullout performance compared to those reinforced with DNTs, primarily due to π–π stacking interactions that facilitate the wrapping of asphalt molecules around the CNT surfaces.

Concrete is a highly heterogeneous composite material, and accurately predicting its elastic modulus remains a major challenge in mechanical analysis. To address this, this study systematically investigates the predictive performance of several classical homogenization methods for estimating the effective elastic modulus of concrete, including the dilute approximation, self-consistent method, generalized self-consistent method, Mori–Tanaka model, differential method, as well as the Voigt and Reuss models. To enhance prediction accuracy, an improved computational framework is proposed based on an iterative strategy that enables dynamic updating of model parameters. This approach combines principles of mesomechanics with numerical simulation techniques and is implemented using Mathematica for both symbolic and numerical computations. The performance of the models is evaluated under varying aggregate volume fractions and aggregate-matrix stiffness combinations, and validated using multiple experimental datasets from the literature. The results show that the iterative strategy significantly improves the predictive accuracy of several models, reducing the maximum error by up to 30%. Further analysis indicates that the dilute method performs best at low aggregate volume fractions, the Mori–Tanaka model yields the most accurate results when the aggregates are stiff and moderately concentrated, and the generalized self-consistent method outperforms the standard version when the elastic moduli of the aggregate and matrix are similar.

Chiral DNTs with methyl groups in a nanocomposite, i.e. DNT_C-CH₃, exhibit the highest interfacial thermal conductance of 0.115 GW m⁻² K⁻¹, representing a 140% improvement over CNTs.