Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML)

Christian Scherdel,Eddi Miller,Jan Schmitt,Gudrun Reichenauer

doi:10.3390/pr9040672

Abstract

The requirements for new materials are increasing with each new application, which, in most cases, means an enhancement in the complexity of the development process. Nanoporous sol-gel-based materials, especially aerogels, are promising candidates for thermal superinsulation, electrodes for energy conversion and storage or high-end adsorbers. Their synthesis and processing route is complex, and the relationship between the material/processing parameters and the resulting structural and physical properties is not straightforward. Using small-angle X-ray scattering (SAXS) allows for fast structural characterization of both the gel and the resulting aerogel; combining these results with the respective physical properties of the aerogels and using these data as inputs for machine learning (ML) algorithms provide an approach to predict physical properties on the basis of a structural dataset. This data-driven strategy may be a feasible approach to speed up the development process. Thus, the study aimed to provide a proof of concept of ML-based model derivation from material, process and SAXS data to predict physical properties such as the solid-phase thermal conductivity (λs) of silica aerogels from a structural dataset. Here, we used different data subsets as predictors according to different states of synthesis (wet and dry) to evaluate the model performance.

Highlights

Sol-gel-derived porous solids represent a class of materials with a high number of synthesis and processing parameters
The RMSE values for the linear regression (LR) and Gaussian process regression (GPR) models predicting the response parameter λs are illustrated for each machine learning (ML) strategy, S I to S IV, for the trained and validated models
The results indicate that the synthesis parameter alone are not sufficient for predictions, as gels change too much during processing (e.g., supercritical drying (SCD))

Summary

Introduction

Sol-gel-derived porous solids represent a class of materials with a high number of synthesis and processing parameters. In contrast to other types of porous materials, such as sinter metals, ceramics, foams or fiber felts, the sol-gel route allows for the synthesis of (monolithic) nanoporous materials with extremely high porosities of up to 99% With these special properties, aerogels are unrivaled candidates for thermal superinsulation, energy conversion and storage, catalyst supports and dielectric materials [2,3], to name just a few target applications. Due to the large number of process parameters and the complexity of the resulting structure, a new approach to speed up materials’ development for aerogels would be very helpful in supporting the above-mentioned applications [7]. This publication may be a starting point improving the material development process in the sol-gel system using machine learning

Synthesis of Sol-Gel Materials

Machine Learning Meta Models

Sol-gel

Fast Structural Characterization—Results

Results

Conclusions and Further Research