Engineering models for thermal conductivity and elastic modulus of alumina aerogels: from monolithic to composite

Alumina aerogels are a class of nanoporous materials featuring tailorable three-dimensional pore architectures with porosities exceeding 90%, exhibiting promising prospects for energy-efficient thermal management applications. However, quantitative models linking pore structure to macroscopic thermo-mechanical performance remain scarce, hindering their rational design and performance optimization. The thermal conductivity of alumina aerogel was experimentally studied based on the steady-state heat conduction method, and the elastic modulus of the material was also measured based on the thermal mechanical coupling effect method. Combining with the literature data, the engineering calculation models were proposed to predict the thermal conductivity and elastic modulus as well as their upper and lower limits, which can help to provide guidance for the rational design of porous architectures. Furthermore, these models were extended to two representative porous composite materials, namely alumina–silica aerogels and fiber-reinforced alumina aerogels. The effective thermal conductivity and elastic modulus of the two composites were estimated by the Geometric Mean Model and the Gibson and Ashby scaling formula, respectively. Literature data were employed to validate the present predictions. The effect of the molar ratio of Al:Si on the thermal conductivity and elastic modulus of the alumina–silica aerogel was analyzed: the thermal conductivity can be reduced by about 13.1–25.5% as the Al:Si decreases from 8:1 to 2:1 for densities in the range of 50–250 kg/m 3 , while the reduction of the elastic modulus does not exceed 7% as the Al:Si decreases from 8:1 to 3:1. The effect of the glass fiber content on the fiber-reinforced alumina aerogel was also discussed: the elastic modulus can be reinforced by about 1.61–119.2% as the volume fraction of the glass fiber increases from 0.1 to 1.4% in the density range of 100–200 kg/m 3 , while the thermal conductivity is only increased by about 0.18–4.7%. These predictive tools provide a quantitative framework for achieving the optimization of structure–property relationships in advanced porous thermal insulation materials.