Data-Driven Review on Aerogels and Polymeric Aerogels

Area of Science:

• Materials Science
• Nanotechnology
• Chemistry

Background:

• Aerogels are advanced solid materials with unique nanostructures, attracting interest for energy and environmental applications.
• The growing volume of aerogel research presents challenges with data reliability and academic reproducibility.
• Freeze-dried aerogels, particularly organic polymers and composites, dominate current literature.

Purpose of the Study:

• To conduct a data-driven review of aerogel chemistry, properties, and applications.
• To analyze process-property data of polymer aerogels using machine learning.
• To critically assess data quality, especially thermal conductivity, in aerogel research.

Main Methods:

• Exhaustive literature survey of papers with "aerogel" in the title.
• Statistical analysis of published aerogel data.
• Machine learning application to process-property data from polymer aerogel studies.

Main Results:

• Identified a significant trend of freeze-dried aerogels composed of organic polymers and composites.
• Developed a data infrastructure framework for future aerogel research using machine learning.
• Highlighted a tendency for unreliable thermal conductivity data in published polymer aerogel studies.

Conclusions:

• Aerogels show promise for 21st-century energy and environmental challenges.
• There is a critical need for improved data quality and standardization in aerogel research.
• Machine learning can aid in establishing robust data infrastructure for advancing aerogel science.