Molecular modeling of physical aging in epoxy polymers

Unravelling the Influence of Surface Modification on the Ultimate Performance of Carbon Fiber/Epoxy Composites

The overall performance of polymer composites depends on not only the intrinsic properties of the polymer matrix and inorganic filler but also the quality of interfacial adhesion. Although many reported approaches have been focused on the chemical treatment for improving interfacial adhesion, the examination of ultimate mechanical performance and long-term properties of polymer composites has been rarely investigated. Herein, we report carbon fiber (CF)/epoxy composites with improved interfacial adhesion by covalent bonding between CFs and the epoxy matrix. This leads to the improved ultimate mechanical properties and enhanced thermal aging performance. Raman mapping demonstrates the formation of an interphase region derived from the covalent bonding between CFs and the epoxy matrix, which enables the uniform fiber distribution and eliminates phase separation during thermal cycling. The covalent attachment of the CF to the epoxy matrix suppresses its migration during temperature fluctuations, preserving the mechanical performance of resulting composites under the thermal aging process. Furthermore, the finite elemental analysis reveals the effectiveness of the chemical treatment of CFs in improving the interfacial strength and toughness of silane-treated CF/epoxy composites. The insight into the mechanical improvement of CF/epoxy composites suggests the high potential of surface modification of inorganic fillers toward polymer composites with tunable properties for different applications.

Modeling the adsorption of PAH mixture in silica nanopores by molecular dynamic simulation combined with machine learning

The persistence of polycyclic aromatic hydrocarbons (PAHs) in contaminated soils is largely controlled by their molecular fate in soil pores. The adsorption and diffusion of 16 PAHs mixture in silica nanopore with diameter of 2.0, 2.5, 3.0 and 3.5 nm, respectively, were characterized by adsorption energy, mean square displacement, free surface area and free volume fraction using molecular dynamic (MD) simulation. Results suggested that PAHs adsorption in silica nanopores was associated with diffusion process while competitive sorption was not the dominant mechanism in context of this study. The partial least squares (PLS) regression and machine learning (ML) methods (i.e. support vector regression, M5 decision tree and multilayer perceptrons) were used to correlate the adsorption energy with the pore diameter and PAH properties (number of carbon atoms, aromatic ring number, boiling point, molecular weight, octanol-water partition coefficient, octanol-organic carbon partition coefficient, solvent accessible area, solvent accessible volume and polarization). Results indicated that the PAH adsorption could not be predicted by linear regression as the R(2)Y and Q(2)Y coefficients of PLS analysis was 0.375 and 0.199, respectively. The nonlinearity was well recognized by ML with correlation coefficient up to 0.9. Overall, the combination of MD simulation and ML approaches can assist in interpreting the sequestration of organic contaminants in the soil nanopores.