Spark plasma sintered alumina-based ceramics: Processing-structure-property correlation with DFT insights

Abstract

Al2O3 is a high-performance structural ceramic widely used in aerospace, military, and biomedical applications due to its high melting point, relatively high hardness, chemical inertness, and moderate wear resistance. Meanwhile, the inherent brittleness and moderate wear performance of Al2O3 ceramics greatly limit their applications in related fields. In this study the SPS-sintered alumina-based ceramics were fabricated via Spark Plasma Sintering (SPS) to investigate the influence of CaF2 and hBN (solid lubricants) additives on the microstructure and mechanical properties. The SPS process was conducted at 1600 °C for 30 min under 30 MPa, producing highly dense ceramics with a relative density >99%. The phase analysis by XRD and thermal studies by TGA/DSC confirmed the successful incorporation of CaF2 and hBN without altering the characteristic α-Al2O3 peaks or phase stability. Pure alumina exhibited the highest hardness, while the addition of hBN reduced hardness by ∼16% due to weak interlayer van der Waals bonding and CaF2 caused only a slight reduction of ∼0.5%. The fracture mode of sintered samples changes from intergranular (in CaF2) to transgranular (in hBN) and finally mixed mode of fracture. The fracture toughness lies in the range of 3.20 to 3.80 MPa m1/2 whereas the friction results confirm hBN was most effective at higher loads, and the hybrid composition gave the best friction reduction at low load through synergistic tribofilm formation. Additionally, first-principles (DFT) calculations were performed to estimate the elastic and mechanical properties using Voigt, Reuss, and Voigt-Reuss-Hill approximations. The computational results were correlated with experimental findings, showing good agreement for pure alumina. Overall, SPS enabled the formation of dense, phase-stable, self-lubricating ceramics with tailored microstructural characteristics that can lead to superior wear performance in extreme environments. © 2026 Elsevier Ltd and Techna Group S.r.l. All rights are reserved, including those for text and data mining, AI training, and similar technologies.