Journal
COMMUNICATIONS MATERIALS
Volume 3, Issue 1, Pages -Publisher
SPRINGERNATURE
DOI: 10.1038/s43246-022-00319-2
Keywords
-
Categories
Funding
- Alexander von Humboldt Foundation
- Office of Naval Research through a Multi-University Research Initiative (MURI) [N00014-17-1-2656, N00014-202175]
- Los Alamos National Laboratory (LANL) Laboratory Directed Research and Development (LDRD) program's project titled Bio-Manufacturing with Intelligent Adaptive Control [20190001DR]
- LANL Center for Nonlinear Studies (CNLS)
- U.S. Department of Energy [89233218CNA000001]
Ask authors/readers for more resources
This study develops multitask deep neural network property predictors to identify potential replacements for petroleum-based commodity plastics. By using the predictors, 14 PHA-based bioplastics are identified from a diverse set of chemistries, with possible synthesis routes discussed.
Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world's plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world's yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available