What is it about?
This article predicts via different types of machine learning algorithms the mechanical properties of polymer nanocomposites based on a biopolymer produced by bacteria reinforced with 3 types of nanomaterials: carbon nanotubes, clays and inorganic nanoparticles.
Featured Image
Photo by Elena Mozhvilo on Unsplash
Why is it important?
The developed machine learning models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process.
Perspectives
The approaches developed offer a series of significant benefits, among them, the substantial reduction of experimental work in the laboratory and the consequent optimization of the time and costs associated with procuring materials and performing repetitive physical tests. This allows greater efficiency in the design and development of new hybrid polymeric nanocomposites.
Ana Maria Diez Pascual
Universidad de Alcala de Henares
Read the Original
This page is a summary of: Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques, Composites Part B Engineering, January 2024, Elsevier,
DOI: 10.1016/j.compositesb.2023.111099.
You can read the full text:
Contributors
The following have contributed to this page
