Boron-modified phenolic resin: thermal stability and decomposition mechanisms via experiments and simulations

Currently, boron modification represents a significant strategy for enhancing the thermal stability of phenolic resins. However, existing boron modification methods generally suffer from synthetic complexity, and the mechanism of boron's effect on phenolic resins at the molecular level remains incompletely understood. This study investigated how 4-hydroxyphenylboronic acid pinacol ester (4-HPBAPE) doping regulates the glass transition temperature (T_g), thermal conductivity, and pyrolysis behavior of phenolic resin. Results showed that 4-HPBAPE doping increased the glass transition temperature (162 → 187 ℃), while thermal conductivity testing revealed a 10% reduction (0.2154 → 0.1920 W/(m·K)). The errors between the results obtained from MD simulation and the experimental results were all less than 10%. The char yield decreased with increasing 4-HPBAPE doping ratio. ReaxFF MD simulations demonstrated that boron modification of phenolic resins enhanced the production of light hydrocarbons (C₁-C₅) during pyrolysis, resulting in higher mass loss. This occurred via boron-mediated ring-opening and suppression of large aromatic cluster formation. Future efforts could focus on controlling the bonding form of boron to suppress ring-opening reactions, thereby enhancing the char yield.

The materials were characterized using Fourier transform infrared spectrometer (FTIR) and nuclear magnetic resonance spectroscopy (NMR). Thermal conductivity was measured using a DRE-2C thermal conductivity meter. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were performed using a synchronous thermal analyzer. Simulations were carried out with the LAMMPS and Materials Studio software package. Classical molecular dynamics (MD) simulations employed the COMPASS force field, while the reaction force field files containing C, H, O and B element were used. The post-processing of the results was implemented using OVITO and self-programmed Python scripts.