Mesophase pitch is usually prepared by radical polymerization or catalytic polymerization from coal tar, petroleum, and aromatic compounds, and the catalytic synthesis of mesophase pitch from pure aromatic compounds is more controllable in the preparation of high-quality mesophase pitch. However, the corrosive and highly toxic nature of the catalyst has limited the further development of this method. In this study, mesophase pitch was synthetized using ethylene tar and naphthalene as raw materials and boron trifluoride diethyl etherate as a catalyst. The effect of the catalytic reaction on the structure and properties of the mesophase pitch was investigated. The results show that naphthalene plays an important role in the mesophase content and reaction pressure (from above 6 MPa to 2.35 MPa). Mesophase pitch with fine-flow texture can be prepared by introducing more methylene groups, naphthenic structures, and aliphatic hydrocarbons during synthesis. Carbon fibers prepared from mesophase pitch show a split structure, and the thermal conductivity is 730 W/(m·K). This work provides theoretical support for lower toxicity and causticity and for reaction-controlled technology for the synthesis of high-purity mesophase pitch.

High-strength and high-modulus epoxy resins are key elements for preparing carbon-fiber-reinforced polymer composites, which play an irreplaceable role in aerospace. In this study, five optimal epoxy systems were developed utilizing the reverse design strategy. The reverse design strategy was based on the ideal resin and curing agent structures offered by the AI polymer platform, and the rules were summarized to create an optimum resin formulation. The formulations used m-phenylenediamine (MPD) as the principal curing agent, which was modified with 10 wt% diethyltetramethylenediamine (DETDA), 10 wt% 4,4′-diaminodiphenylmethane (DDM), or 10 wt% triethylenetetramine (TETA) to establish multiple crosslinking networks. Systematic characterization using differential scanning calorimetry (DSC) and rheological analysis revealed that the optimized activation energy was 55.95–63.42 kJ/mol, and the processing viscosity was ≤500 mPa·s at 80 °C. A stepwise curing protocol (3 h@80 °C, 2 h@120 °C, and 3 h@180 °C) was established to achieve a complete crosslinking network. The results showed that the system with 10% DDM had a tensile strength of 132.6 MPa, a modulus of 5.0 GPa, and a glass transition temperature of 253.1 °C. This work advances the rational design of epoxy resins by bridging molecular architecture with macroscopic performance, offering a paradigm for developing a next-generation matrix tailored to accommodate extreme operational demands in high-end engineering sectors.

The melting point of a material constitutes a pivotal property with profound implications across various disciplines of science, engineering, and technology. Recent advancements in machine learning potentials have revolutionized the field, enabling ab initio predictions of materials' melting points through atomic-scale simulations. However, a universal simulation methodology that can be universally applied to any material remains elusive. In this paper, we present a generic, fully automated workflow designed to predict the melting points of materials utilizing molecular dynamics simulations. This workflow incorporates two tailored simulation modalities, each addressing scenarios with and without elemental partitioning between solid and liquid phases. When the compositions of both phases remain unchanged upon melting or solidification, signifying the absence of partitioning, the melting point is identified as the temperature at which these phases coexist in equilibrium. Conversely, in cases where elemental partitioning occurs, our workflow estimates both the nominal melting point, marking the initial transition from solid to liquid, and the nominal solidification point, indicating the reverse process. To ensure precision in determining these critical temperatures, we employ an innovative temperature-volume data fitting technique, suitable for a diverse range of materials exhibiting notable volume disparities between their solid and liquid states. This comprehensive approach offers a robust and versatile solution for predicting melting points, fostering advancements in materials science and technology.