Zirconia aerogels for thermal management: Review of synthesis, processing, and properties information architecture

Highlights

• Zirconia aerogels are unique materials, useful for a wide range of applications.

• Many synthetic and processing pathways must be considered for zirconia aerogels.

• Information architecture facilitates experimental optimization of aerogels.

• Data science can elucidate structure-property relationships in colloidal chemistry.

Abstract

Zirconia aerogels are porous nanomaterials with high specific surface areas and low thermal conductivities that are suitable for a wide range of functions. The applications of zirconia aerogels include numerous uses in thermal management systems that are specifically beneficial in aeronautics and aerospace systems. This review seeks to detail the synthesis, processing, and characterization of these unique materials. However, the many distinctive synthesis pathways and processing conditions of zirconia aerogels can make the optimization of these materials difficult, potentially inhibiting further development. Independent variables in the synthesis process alone include zirconium precursor, rare earth stabilizer, solvent system, gelation agent, and surfactant templating agent. If only two distinct options were available for each synthetic variable, there would be up to 32 different synthetic pathways; if there were three options for each variable, 243 different synthetic pathways would be possible. Apart from the gel synthesis, processing conditions, including drying method, drying temperature, drying solvent, and sintering temperature, as well as various techniques used to characterize aerogels, need to be considered. To mitigate the sheer volume of synthetic parameters, this review uses an architected information structure to contemplate approximately 600 aerogel materials, along with the synthesis and processing conditions that make each material unique. By utilizing this information structure, containing over 10,000 relationships amongst 3,800 nodes, the connection between specific properties of zirconia aerogels and the pathways used to produce them can be more easily visualized, leading to a more effective understanding of the many variables that are used in the synthesis and processing of these materials. This review seeks to utilize data science in a way that can elucidate structure-property relationships in colloidal chemistry, providing a more efficient way to evaluate the synthesis and processing of materials with high experimental dimensionality.

Introduction

Aerogels have unique material properties, such as high surface areas and low thermal conductivities. They are useful for a broad range of applications and have gained attention over the last several decades. Zirconia aerogels are used in an expansive variety of applications, including catalyst supports, sorption media, and electrodes in solid oxide fuel cells [1,2]. Due to the extremely low thermal conductivity and high porosity of zirconia aerogels, these materials are often used in thermal management applications, such as high-temperature thermal insulation, especially in aeronautics and aerospace systems [3]. In comparison with silica aerogels, which display sintering and loss of mesoporous structure at high temperatures, zirconia aerogels are promising candidates for applications taking place at temperatures above 1000°C due to their high melting point of ZrO₂ at 2715°C, low thermal conductivity, and presence of both acid and base active centers. The high thermal stability of zirconia aerogels can be increased further by optimizing the aerogel formulation; for example, zirconia metal oxide aerogels stabilized by yttria rare earth dopants are especially thermally stable. This is due to the low thermal conductivity of yttria-stabilized zirconia (0.5 – 2.36 W/m·K), which is made even lower when used as an aerogel system (0.168 – 0.212 W/m·K) [4]. Since the formulation of zirconia aerogels can directly influence the thermal stability of these materials, an understanding of the effects of synthesis and processing on the zirconia aerogel system is valuable.

This review begins with a look at the usefulness of aerogels in general applications over the last decade and a comparison of zirconia aerogels to aerogels made with other various precursors, such as silica, alumina, and carbon. This review then seeks to further understand the synthetic pathways of zirconia aerogels as the synthesis methods, processing conditions, and characterization of zirconia aerogels are investigated. Depictions of the general synthesis and processing pathways of zirconia aerogels, which are similar to various other aerogel systems, can be found in Scheme 1. Prior to formation of the gel, the aerogel’s sol must be formulated using a variety of zirconium precursors, rare earth dopant stabilizers, solvent systems, templating agents, and gelation agents. Following formulation of the sol, the sol-gel method is most commonly used to initiate gelation; however, hydrothermal, sonochemical, electrolysis, solution heating, chemical precipitation, and microwave irradiation methods can also be used. After gelation, the gel can be dried using a variety of drying methods, such as freeze drying, ambient pressure drying, oven drying, atmospheric drying, and most frequently for aerogels, supercritical drying. Dried aerogels are often heat-treated, causing sintering and densification of the pore structure, at a variety of temperatures and times to determine behavior of the system upon high-temperature exposure. As-dried and heat-treated aerogel samples are then characterized using a variety of characterization techniques, including scanning electron microscopy, nitrogen physisorption, x-ray diffraction, and thermogravimetric analysis, to name a few. The entire experimental process from initial sol formulation to characterized product can take on average up to two weeks depending on chosen methods.

There are an extensive number of synthetic variables and processing conditions for zirconia aerogels. The number of synthetic pathways can be determined using the relationship mⁿ where n is the number of synthetic variables considered and m is the number of distinct options possible for each variable. Looking at the sol formation alone, there are at least five distinct synthetic variables to be considered: zirconium precursor, rare earth dopant stabilizer, solvent system, templating agent, and gelation agent. If there were only two distinct options for each of these five variables (m = 2, n = 5), there would be 32 synthetic pathways; if there were three distinct options (m = 3, n = 5), 243 synthetic pathways would be feasible. Typically, m is much greater than two or three, as there are many more potential options for each synthetic variable; for example, m is greater than 17 for the gelation agent variable. The concentrations of each of the five synthetic variables added to the sol also need to be considered, further increasing the value of m. Following the formulation of the sol, variables included in the gelation method, drying method, and sintering method need to also be examined, increasing the value of n.

The large number of synthetic variables shows an exponential increase as each processing step is evaluated. Due to the lengthy experimental time between initial formulation and final characterized product, as well as cost of materials and methods, arbitrary investigation of the synthetic pathways of zirconia aerogels should be avoided. An optimized synthetic pathway, creating zirconia aerogels with high surface area and mesoporous structure, should be determined prior to experimentation. Yet, the sheer number of synthetic and processing variables of zirconia aerogels can make the optimization of these materials difficult and can hinder potential development.

This review uses an architected information structure to begin to mitigate the volume of synthetic variables, which will aid in the understanding and enhancement of these materials. The synthesis, processing, and characterization of 600 zirconia aerogel materials reported since 2014 is the focus of the information structure [1,[3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]]. This knowledge graph contains nearly 3,800 nodes with over 10,000 relationships that visually and dynamically display the connections between aerogel properties and the synthesis and processing methods used to produce them. It is anticipated that this information structure can lead to a greater knowledge of zirconia aerogels and a future optimized synthetic pathway of these materials. In a broader aspect, this review provides an example for ways in which data science can be used to enhance colloidal chemistry, providing more efficient experimentation and development of materials, specifically those with high experimental dimensionality.